_models.SearchIndex:","def __init__(self, lang: str, db: PassageDB, analyzer: AnyStr, index_dir=None, ram_size=2048):\n super().__init__()\n lucene.initVM(vmargs=['-Djava.awt.headless=true'])\n\n self.idx_dir = self.get_index_name(lang, index_dir)\n logging.info(f\"Creating index in {self.idx_dir}!\")\n\n self.db = db\n\n # stores index files, poor concurency try NIOFSDirectory instead\n store = SimpleFSDirectory(Paths.get(self.idx_dir))\n # limit max. number of tokens per document.\n # analyzer will not consume more tokens than that\n # analyzer = LimitTokenCountAnalyzer(analyzer, 1048576)\n # configuration for index writer\n config = IndexWriterConfig(analyzers[analyzer]())\n # creates or overwrites index\n config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)\n # setting similarity BM25Similarity(k1=1.2,b=0.75)\n similarity = BM25Similarity(self.k1, self.b)\n config.setSimilarity(similarity)\n config.setRAMBufferSizeMB(float(ram_size))\n # create index writer\n self.writer = IndexWriter(store, config)\n\n self.ftdata = FieldType()\n self.ftmeta = FieldType()\n # IndexSearcher will return value of the field\n self.ftdata.setStored(False)\n self.ftmeta.setStored(True)\n # will be analyzed by Analyzer\n self.ftdata.setTokenized(True)\n self.ftmeta.setTokenized(False)\n # what information are stored (probably DOCS would be sufficient)\n # DOCS: Only documents are indexed: term frequencies and positions are omitted.\n # Phrase and other positional queries on the field will throw an exception,\n # and scoring will behave as if any term in the document appears only once.\n # DOCS_AND_FREQS: Only documents and term frequencies are indexed: positions are\n # omitted. This enables normal scoring, except Phrase and other positional\n # queries will throw an exception.\n # DOCS_AND_FREQS_AND_POSITIONS: Indexes documents, frequencies and positions.\n # This is a typical default for full-text search: full scoring is enabled\n # and positional queries are supported.\n self.ftdata.setIndexOptions(IndexOptions.DOCS_AND_FREQS)\n self.ftmeta.setIndexOptions(IndexOptions.DOCS)\n # instantiate some reusable objects\n self.doc = Document()\n # Id cannot be reused because there is multiple values\n # I could store list of fields and add one if its not enough\n #self.fieldId = Field(\"id\", \"dummy\", self.ftmeta)\n self.fieldTitle = Field(\"title\", \"dummy\", self.ftdata)\n self.doc.add(self.fieldTitle)\n self.fieldContext = Field(\"context\", \"dummy\", self.ftdata)\n self.doc.add(self.fieldContext)\n self.fieldId = Field(\"id\", \"dummy\", self.ftmeta)\n self.doc.add(self.fieldId)","def createindexes():\n index = [{}, {}, {}, {}]\n readcorpus(index)\n buildindex4(index[2], index[3])\n writeindextofile(index)\n return index"],"string":"[\n \"def test_create_text_index_noschema(self):\\n actual = self.engine._create_text_index()\\n expected = 'TextIndex'\\n self.assertEqual(actual, expected)\",\n \"def test_creating_index_type(self):\",\n \"def test_string_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_string_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_simple_index, \\\"foobar\\\")\\n\\t)\",\n \"def create_index():\",\n \"def create_index(schema, index_name):\\n if not os.path.exists(index_name):\\n os.mkdir(index_name)\\n ix = index.create_in(index_name, schema)\\n print(f\\\"index {index_name} created successfully\\\")\\n return ix\",\n \"def test_create_index_is_called(self):\\r\\n assert len(self.index_calls) == 0\\r\\n\\r\\n connection._index_all_fields = False\\r\\n \\r\\n class TestIndexCreationCallTestVertex(Vertex):\\r\\n col1 = properties.Text(index=True)\\r\\n col2 = properties.Text(index=True, db_field='____column')\\r\\n col3 = properties.Text(db_field='____column3')\\r\\n\\r\\n assert len(self.index_calls) == 2\\r\\n assert 'vid' not in self.index_calls\\r\\n assert 'col1' in self.index_calls\\r\\n assert '____column' in self.index_calls\\r\\n assert '____column3' not in self.index_calls\\r\\n\\r\\n connection._index_all_fields = True\\r\\n self.index_calls = []\\r\\n\\r\\n class TestIndexCreationCallTestVertex2(Vertex):\\r\\n col1 = properties.Text()\\r\\n col2 = properties.Text(db_field='____column')\\r\\n\\r\\n assert len(self.index_calls) == 3\\r\\n assert 'vid' in self.index_calls\\r\\n assert 'col1' in self.index_calls\\r\\n assert '____column' in self.index_calls\",\n \"def test_works_index_name(self):\\n assert \\\"test_index-v4\\\" == self.search.works_index_name(self._db)\",\n \"def test_create_index_fail_test(tcex: TcEx, monkeypatch: MonkeyPatch):\\n\\n # monkeypatch method\\n def mp_post(*args, **kwargs): # pylint: disable=unused-argument\\n return MockPost({}, ok=False)\\n\\n monkeypatch.setattr(tcex.session.tc, 'post', mp_post)\\n\\n # create index\\n key = str(uuid.uuid4())\\n try:\\n tcex.api.tc.v2.datastore('local', key)\\n assert False, 'Failed to catch error on ok=False'\\n except RuntimeError:\\n assert True\",\n \"def test_string_list_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_string_list_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_list_index, \\\"foobar\\\")\\n\\t)\",\n \"def idx_createFieldIndex(plominoIndex, fieldname, fieldtype='TEXT', **args):\\n \\n if not hasattr(plominoIndex, 'createFieldIndex'):\\n plominoIndex = plominoIndex.getParentDatabase().getIndex()\\n \\n if not fieldname in plominoIndex.indexes():\\n plominoIndex.createFieldIndex(fieldname, fieldtype, **args)\",\n \"def ensure_index(self, schema, language, mapping, return_index='external'):\\n assert schema and language and mapping\\n assert len(language) == 2\\n assert return_index == 'external' or return_index == 'internal'\\n\\n external = self.get_external_index_name(schema, language, mapping.name)\\n internal = self.get_internal_index_name(\\n schema, language, mapping.name, mapping.version)\\n\\n return_value = return_index == 'external' and external or internal\\n\\n if internal in self.created_indices:\\n return return_value\\n\\n if self.es_client.indices.exists(index=internal):\\n self.created_indices.add(internal)\\n return return_value\\n\\n # create the index\\n self.es_client.indices.create(\\n index=internal,\\n mappings={\\n 'properties': mapping.for_language(language)\\n },\\n settings={\\n 'analysis': ANALYSIS_CONFIG,\\n 'index': {\\n 'number_of_shards': 1,\\n 'number_of_replicas': 0,\\n 'refresh_interval': '5s'\\n }\\n }\\n )\\n\\n # point the alias to the new index\\n self.es_client.indices.put_alias(name=external, index=internal)\\n\\n # cache the result\\n self.created_indices.add(internal)\\n\\n return return_value\",\n \"def createIndex(schemaName, tableName, index):\\n return \\\"\\\"\\\"CREATE {unique} INDEX {name} ON \\\\\\\"{schema_name}\\\\\\\".\\\\\\\"{tableName}\\\\\\\" ({fields})\\\"\\\"\\\".format(\\n unique = \\\"UNIQUE\\\" if index.uniqueness else \\\"\\\",\\n schema_name = schemaName,\\n name = \\\"\\\\\\\"\\\" + tableName + \\\"__\\\" + index.name + \\\"\\\\\\\"\\\" if index.name is not None else \\\"\\\",\\n tableName = tableName,\\n fields = \\\", \\\".join(map(lambda index: '\\\"' + str(index.name) + '\\\"', index.fields))\\n )\",\n \"def test_create_index_swift(self):\\n\\n indexfile = tempfile.mktemp()\\n self.addCleanup(os.unlink, indexfile)\\n\\n index = TroveSwiftIndexBuilder(\\\"short.dat\\\", out=indexfile)\\n\\n # read the index file that was created\\n with open(indexfile, 'r+b') as fd:\\n indextext = fd.read()\\n indexlines = indextext.split('\\\\n')\\n\\n # 11 lines includes on blank line at the end\\n self.assertEquals(11, len(indexlines))\\n del indexlines[10]\\n\\n # check the first character of each line\\n docs = [line[0] for line in indexlines]\\n self.assertEquals(['1', '2', '3', '4', '5', '6', '7', '8', '9', '1'], docs)\\n\\n # check some lines from the index\\n ref = \\\"1, 0, 31, short.dat\\\"\\n self.assertEqual(ref, indexlines[0])\\n ref = \\\"10, 279, 32, short.dat\\\"\\n self.assertEqual(ref, indexlines[9])\",\n \"def create_index(self):\\r\\n\\r\\n #### Begin functionality here\\r\\n\\r\\n return()\",\n \"def create_index(self, table_name, index, timeout):\\n _abstract()\",\n \"def create_index(self, table_name, index, timeout):\\n _abstract()\",\n \"def test_create_index(self, collection):\\n collection.create_index(\\\"hello\\\")\\n assert collection._indexes == {\\\"_id_\\\": ((\\\"_id\\\",), {(1,)})}\\n\\n collection.create_index(\\\"hello\\\", unique=True)\\n assert collection._indexes == {\\n \\\"_id_\\\": ((\\\"_id\\\",), {(1,)}),\\n \\\"hello_1\\\": ((\\\"hello\\\",), {(\\\"there\\\",)}),\\n }\",\n \"def schema_index(schema, version):\\n if schema.abstract:\\n raise InvalidData(\\\"Cannot index abstract schema: %s\\\" % schema)\\n name = \\\"entity-%s\\\" % schema.name.lower()\\n return index_name(name, version=version)\",\n \"def test_invalid_text_search(aquarius_instance):\\n text = \\\"foo_text\\\"\\n with pytest.raises(ValueError):\\n aquarius_instance.text_search(text=text, sort=\\\"foo_sort\\\")\",\n \"def test_create_index(self):\\n\\n class Comment(Document):\\n message = StringField()\\n meta = {\\\"allow_inheritance\\\": True}\\n\\n Comment.create_index(\\\"message\\\")\\n\\n info = Comment.objects._collection.index_information()\\n info = [\\n (value[\\\"key\\\"], value.get(\\\"unique\\\", False), value.get(\\\"sparse\\\", False))\\n for key, value in info.items()\\n ]\\n assert ([(\\\"_cls\\\", 1), (\\\"message\\\", 1)], False, False) in info\",\n \"def test_creation(self):\\n config = config_factory()\\n db = config.arango_db\\n\\n # Create analyzer\\n analyzer = ArangoAnalyzer(\\\"analyzer_sample\\\")\\n analyzer.set_stopwords(\\n language=\\\"english\\\",\\n custom_stopwords=[\\\"stop\\\", \\\"word\\\"],\\n include_default=False,\\n )\\n analyzer.type = ArangoAnalyzer._TYPE_TEXT\\n\\n analyzer.create(db)\\n assert analyzer.__dict__ == {\\n \\\"name\\\": \\\"analyzer_sample\\\",\\n \\\"type\\\": \\\"text\\\",\\n \\\"features\\\": [\\\"frequency\\\", \\\"norm\\\", \\\"position\\\"],\\n \\\"locale\\\": \\\"en\\\",\\n \\\"case\\\": \\\"lower\\\",\\n \\\"stopwords\\\": [\\\"stop\\\", \\\"word\\\"],\\n \\\"accent\\\": False,\\n \\\"stemming\\\": True,\\n \\\"edge_ngram\\\": None,\\n \\\"delimiter\\\": \\\",\\\",\\n \\\"min\\\": 2,\\n \\\"max\\\": 5,\\n \\\"preserve_original\\\": False,\\n \\\"start_marker\\\": \\\"\\\",\\n \\\"end_marker\\\": \\\"\\\",\\n \\\"stem_type\\\": \\\"binary\\\",\\n }\\n assert isinstance(db.analyzer(\\\"analyzer_sample\\\"), dict)\",\n \"def create_schema():\\n schema = Schema(idx=ID(stored=True),\\n data=STORED,\\n body=TEXT(analyzer=StemmingAnalyzer()),\\n )\\n print(\\\"schema creation successful\\\")\\n return schema\",\n \"def test_text(self):\\n conn = self.database.connection()\\n cursor = conn.cursor()\\n dialect = self.database.dialect()\\n dbapi = self.database.dbapi()\\n query = dialect.translate('DROP TABLE test_text')\\n try:\\n cursor.execute(query)\\n except dbapi.Error:\\n conn.rollback()\\n query = dialect.translate('CREATE TABLE test_text ' \\\\\\n '( value TEXT NOT NULL )')\\n cursor.execute(query)\\n data = []\\n query = 'INSERT INTO test_text VALUES (%s)'\\n for i in range(10):\\n item = random_string(100000)\\n data.append(item)\\n cursor.execute(query, (item,))\\n query = 'SELECT * FROM test_text'\\n cursor.execute(query)\\n result = cursor.fetchall()\\n for row in result:\\n item = row[0]\\n assert isinstance(item, unicode)\\n assert item in data\\n data.remove(item)\\n query = dialect.translate('DELETE FROM test_text')\\n cursor.execute(query)\\n query = dialect.translate('DROP TABLE test_text')\\n cursor.execute(query)\\n conn.commit()\",\n \"def test_index(self):\",\n \"def setUp(self):\\n body = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 0\\n }\\n }\\n self.assertTrue(self.es.create_index('contacts_esclient_test', body))\\n self.assertFalse(self.es.create_index('contacts_esclient_test', body))\\n\\n self.assertTrue(self.es.create_index('contacts_esclient_test2', body))\\n self.assertFalse(self.es.create_index('contacts_esclient_test2', body))\\n\\n\\n \\\"\\\"\\\" Index some test data \\\"\\\"\\\"\\n data = {\\\"name\\\": \\\"Joe Tester\\\",\\\"age\\\": 21, \\\"sex\\\": \\\"male\\\"}\\n self.assertTrue(self.es.index(\\\"contacts_esclient_test\\\", \\\"person\\\", body=data,\\n docid=1))\\n data = {\\\"name\\\": \\\"Joe Schmoe\\\",\\\"age\\\": 17, \\\"sex\\\": \\\"male\\\"}\\n self.assertTrue(self.es.index(\\\"contacts_esclient_test\\\", \\\"person\\\", body=data,\\n docid=2))\\n\\n self.assertTrue(self.es.refresh('contacts_esclient_test'))\",\n \"def test_get_document_inexistent(empty_index):\\n with pytest.raises(Exception):\\n empty_index().get_document(\\\"123\\\")\",\n \"def create_index(self, db_name):\\n\\t\\tindex_func_path = self._get_index_func_filepath(db_name)\\n\\t\\t\\n\\t\\tif os.path.isfile(index_func_path):\\n\\t\\t\\t# create index request payload from predefined file\\t\\n\\t\\t\\twith open(index_func_path, 'r') as content_file:\\n\\t\\t\\t\\tpayload = content_file.read()\\n\\t\\t\\n\\t\\t\\tprint (\\\"Create index using function in: {}\\\".format(index_func_path))\\n\\t\\t\\turl = \\\"https://{}/{}/_design/view\\\".format(\\n\\t\\t\\t\\tself.cloudanthost, db_name)\\n\\t\\t\\tresponse = self.r.put(url, data=payload)\\n\\t\\t\\tassert response.status_code == 201\",\n \"def test_add_documents_empty(empty_index):\\n index = empty_index()\\n with pytest.raises(Exception) as e_info:\\n index.add_documents(\\\"\\\")\\n assert e_info.value.code == \\\"missing_payload\\\"\\n assert e_info.value.type == \\\"invalid_request\\\"\",\n \"def create_new_index(self, dict_pg_info):\\n # ! Setting if fun can use default setting\\n ruler = Rules()\\n str_conn = ruler.pg_info_rules(dict_pg_info)\\n conn = psycopg2.connect(str_conn)\\n\\n with conn:\\n with conn.cursor() as cur:\\n str_create_table = \\\"CREATE TABLE \\\" + dict_pg_info['table'] + \\\" (path varchar PRIMARY KEY);\\\"\\n # ! Check if table already exit\\n cur.execute(str_create_table)\\n cur.close()\\n\\n conn.close()\",\n \"def test_export_index(self):\",\n \"def create_index(self):\\n self.send_robust(self.es_index, data=self.es_meta)\\n self.set_index_normal_settings()\",\n \"def build_index():\\n pass\",\n \"def test_should_return_correct_gremlin_for_key_index_creation(self):\\r\\n expected = 'g.createKeyIndex(\\\"email\\\", Vertex.class)'\\r\\n key_index = self.spec_parser.parse_key_index(self.key_index_spec)\\r\\n assert key_index.gremlin == expected\",\n \"def _apply_index_op(db, op):\\n if 'createIndexes' not in op['o']:\\n return\\n o = op['o']\\n coll_name = o['createIndexes']\\n key = list(o['key'].items())\\n name = o['name']\\n return db[coll_name].create_index(key, name=name)\",\n \"def get_index_text(self, crate, module, impl, name):\\n raise NotImplementedError\",\n \"def create_indices():\\n destroy_indices()\\n\\n ActionDocument._index.create(ignore=[400, 404])\\n ClassificationDocument._index.create(ignore=[400, 404])\\n FunctionDocument._index.create(ignore=[400, 404])\\n PhaseDocument._index.create(ignore=[400, 404])\\n RecordDocument._index.create(ignore=[400, 404])\\n\\n yield\\n\\n destroy_indices()\",\n \"def create_index(es_object, index_name):\\n created = False\\n \\\"\\\"\\\" index settings \\\"\\\"\\\"\\n settings = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 0\\n },\\n \\\"mappings\\\": {\\n \\\"physicians\\\": {\\n \\\"dynamic\\\": \\\"strict\\\",\\n \\\"properties\\\": {\\n \\\"overview\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"full_name\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"years_of_practice\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"language\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"office_location\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"hospital_affiliation\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"specialties\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"education_and_medical_training\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"certification_and_licensure\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n }\\n }\\n }\\n }\\n\\n try:\\n if not es_object.indices.exists(index_name):\\n # Ignore 400 means to ignore \\\"Index Already Exist\\\" error.\\n es_object.indices.create(index=index_name, ignore=400, body=settings)\\n print('Created Index')\\n created = True\\n except Exception as ex:\\n print(str(ex))\\n finally:\\n return created\",\n \"def test_geo_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_geo_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_geo_index, (0.0, 0.0))\\n\\t)\",\n \"def __init__(self, index_name, doc_type='string'):\\n super(ESMigration, self).__init__()\\n self.index_name = index_name\\n self.doc_type = doc_type\",\n \"def create_index(cls, engine):\\n\\n reg_imei = db.Index('reg_imei_index', cls.imei, postgresql_concurrently=True)\\n reg_imei.create(bind=engine)\\n\\n reg_normalized_imei = db.Index('reg_normalized_imei_index', cls.normalized_imei, postgresql_concurrently=True)\\n reg_normalized_imei.create(bind=engine)\",\n \"def test_read_index(self):\\n\\n indexfile = tempfile.mktemp()\\n self.addCleanup(os.unlink, indexfile)\\n\\n TroveIndexBuilder(\\\"test/short.dat\\\", out=indexfile)\\n\\n index = TroveIndex()\\n index.reload(indexfile)\\n\\n docs = sorted([doc for doc in index.documents])\\n self.assertEquals(10, len(docs))\\n\\n self.assertEquals([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], docs)\\n\\n doc = index.get_document(1)\\n ref = {u\\\"id\\\":\\\"1\\\",u\\\"titleName\\\":u\\\"Hello\\\"}\\n self.assertNotEquals(None, doc, \\\"Document not found for id 1\\\")\\n self.assertDictEqual(ref, doc)\\n\\n doc = index.get_document(10)\\n ref = {\\\"id\\\":\\\"10\\\",\\\"titleName\\\":\\\"Hello\\\"}\\n self.assertNotEquals(None, doc)\\n self.assertDictEqual(ref, doc)\",\n \"def test_string_map_key_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_string_map_key_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_map_key_index, \\\"foobar\\\")\\n\\t)\",\n \"def test_integer_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_integer_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_simple_index, 12345)\\n\\t)\",\n \"def create(\\n self,\\n index: IO,\\n request_options: Optional[_models.RequestOptions] = None,\\n *,\\n content_type: str = \\\"application/json\\\",\\n **kwargs: Any\\n ) -> _models.SearchIndex:\",\n \"def test_index_table(self, mapping_cls, bulk_mock):\\n indices = self.elasticsearch_cls().indices\\n mapping = mapping_cls()\\n rows = [\\n {'id': 1, 'number': '123456789'},\\n {'id': 2, 'number': '234567890'},\\n {'id': 3, 'number': '345678901'},\\n ]\\n bulk_mock.return_value = (len(rows), [])\\n\\n db_path = 'some path'\\n table_name = 'calls'\\n table_reader = Mock()\\n table_reader.rows.return_value = rows\\n table_reader.database.db_filename = db_path\\n table_reader.table.name = table_name\\n documents_indexed = self.client._index_table(table_reader)\\n indices.put_mapping.assert_called_once_with(\\n index=self.client.INDEX_NAME,\\n doc_type=hashlib.md5(\\n '{}:{}'.format(db_path, table_name)).hexdigest(),\\n body=mapping.mapping)\\n self.assertEqual(documents_indexed, len(rows))\",\n \"def __call__(self, index, checkFirst=False):\\n schema_name = index.schema().dbname()\\n index_name = index.dbname()\\n cmd = 'CREATE' if not index.testFlag(index.Flags.Unique) else 'CREATE UNIQUE'\\n\\n cols = ['`{0}`'.format(col.field()) if col.testFlag(col.Flags.CaseSensitive)\\n else '`{0}` COLLATE NOCASE'.format(col.field())\\n for col in index.columns()]\\n\\n cmd = '{0} INDEX `{1}` ON `{2}` ({3})'.format(cmd, index_name, schema_name, ', '.join(cols))\\n\\n if checkFirst:\\n cmd = \\\"\\\"\\\"\\\\\\n DO $$\\n BEGIN\\n IF NOT EXISTS (\\n SELECT 1\\n FROM pg_indexes\\n WHERE schemaname = 'public'\\n AND indexname = '{0}'\\n ) THEN {1};\\n END IF;\\n END$$;\\n \\\"\\\"\\\".format(index_name, cmd)\\n else:\\n cmd += ';'\\n\\n return cmd, {}\",\n \"def _create_indices(cls):\\r\\n from thunderdome.connection import _hosts, _index_all_fields, create_key_index\\r\\n \\r\\n if not _hosts: return\\r\\n for column in cls._columns.values():\\r\\n if column.index or _index_all_fields:\\r\\n create_key_index(column.db_field_name)\",\n \"def create_schema(self, schema: str):\\n return\",\n \"def test_secondary_index(self):\\n session = self.prepare()\\n\\n session.execute(\\\"CREATE TABLE t (id int PRIMARY KEY, v int, v2 text, v3 decimal)\\\")\\n session.execute((\\\"CREATE MATERIALIZED VIEW t_by_v AS SELECT * FROM t \\\"\\n \\\"WHERE v IS NOT NULL AND id IS NOT NULL PRIMARY KEY (v, id)\\\"))\\n assert_invalid(session, \\\"CREATE INDEX ON t_by_v (v2)\\\",\\n \\\"Secondary indexes are not supported on materialized views\\\")\",\n \"def create_index(es_object, index_name):\\n created = False\\n # index settings\\n # the reason why we need mappings is avoid corrupting your data\\n settings = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 0\\n },\\n \\\"mappings\\\": {\\n # custom type called foods\\n \\\"foods\\\": {\\n \\\"properties\\\": {\\n # Specify that the food_name field contains text values.\\n \\\"food_name\\\": {\\n \\\"type\\\": \\\"text\\\",\\n \\\"fields\\\": {\\n \\\"raw\\\":{ \\n \\\"type\\\": \\\"keyword\\\"\\n } # The food_name.raw field can be used for sorting and aggregations\\n }\\n },\\n # Specify that the categories field contains text values.\\n \\\"categories\\\": {\\n \\\"type\\\": \\\"text\\\",\\n \\\"fields\\\": {\\n \\\"raw\\\":{ \\n \\\"type\\\": \\\"keyword\\\"\\n } # The categories.raw field can be used for sorting and aggregations\\n }\\n },\\n # Specify that the calories field contains integer values.\\n \\\"calories\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"protein\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"carbs\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"fat\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n }\\n \\n },\\n }\\n }\\n }\\n try:\\n if not es_object.indices.exists(index_name):\\n # Ignore 400 means to ignore \\\"Index Already Exist\\\" error.\\n es_object.indices.create(index=index_name, ignore=400, body=settings)\\n print('Created Index')\\n created = True\\n except Exception as ex:\\n print(str(ex))\\n finally:\\n return created\",\n \"def test_create_bad_sample_names(self):\\n # set a horrible list of sample names\\n self.metadata.index = ['o()xxxx[{::::::::>', 'sample.1', 'sample.3']\\n with self.assertRaises(QiitaDBColumnError):\\n SampleTemplate.create(self.metadata, self.new_study)\",\n \"def get_index_text(self, name):\\n raise NotImplementedError('must be implemented in subclasses')\",\n \"def create_indexes(create_func):\\n\\tfor set_name, index_path, index_name in zip(SET_NAMES, INDEX_PATHS, INDEX_NAMES):\\n\\t\\tcreate_func(set_name, index_path, index_name)\",\n \"def set_index(self, idx, rel, attrs):\\n\\n query = 'CREATE INDEX {} ON {} ({})'.format(idx, rel, ','.join(attrs))\\n\\n with self.tpch_cxn.cursor() as curs:\\n try:\\n curs.execute(query)\\n except pg.ProgrammingError as e:\\n print(e)\",\n \"def test_read_text_of_text_indexed_pdf(allow_ocr, monkeypatch):\\n # This is a PDF which contains indexed text\\n pdf_path = Path(__file__).parent / 'data' / 'matmod_exam_des_2017.pdf'\\n\\n # So the OCR method should never be called\\n monkeypatch.delattr('examiner.pdf.PdfReader.ocr_text')\\n\\n # Now we read the indexed text\\n pdf = PdfReader(path=pdf_path)\\n text = pdf.read_text(allow_ocr=allow_ocr)\\n\\n # Ensure unicode string\\n assert isinstance(text, str)\\n\\n # Check content\\n assert 'Rottman' in text\\n assert 'population model' in text\\n assert 'this is not in the exam' not in text\",\n \"def test_index_database(self, db_reader_cls):\\n database = Mock()\\n db_reader = db_reader_cls()\\n db_reader.tables.return_value = ('calls', 'messages', 'pictures')\\n documents_indexed_per_table = (1, 2, 3)\\n self.client._index_table = Mock(\\n side_effect=documents_indexed_per_table)\\n\\n documents_indexed = self.client._index_database(database)\\n db_reader_cls.assert_called_with(database)\\n self.assertEqual(documents_indexed, sum(documents_indexed_per_table))\",\n \"def _SetupIndexes(self, _open=open):\\n pass\",\n \"def test_get_index_action(self):\\n index_name = 'index'\\n document_type = 'message'\\n document = {'text': 'some message'}\\n action = get_index_action(index_name, document_type, document)\\n self.assertDictEqual(\\n action,\\n {\\n '_index': 'index',\\n '_type': 'message',\\n '_source': {\\n 'text': 'some message',\\n },\\n },\\n )\",\n \"def checkCatalogIndexes(self, trans):\\n for lang in trans.keys():\\n index_id = 'objecttrans_%s' % lang\\n if not index_id in self.indexes():\\n self.__createObjecttransIndex(index_id)\\n index_id = 'objectname_%s' % lang\\n if not index_id in self.indexes():\\n self.__createObjectnameIndex(index_id)\",\n \"def define_index_field(DomainName=None, IndexField=None):\\n pass\",\n \"def save(self, *args, **kwargs):\\n # create the index string\\n message = str()\\n data = kwargs.get(\\\"data\\\")\\n ts = kwargs.get(\\\"ts\\\")\\n # prepare the doc for indexing\\n doc = dict()\\n doc['_type'] = kwargs.get(\\\"type\\\")\\n doc['info'] = data\\n doc['@timestamp'] = datetime.datetime(*ts[:6])\\n try:\\n exists = self.es.indices.exists(kwargs.get(\\\"index\\\"))\\n if not exists:\\n map_st, map_msg = self.build_mapping(**kwargs)\\n if not map_st:\\n return map_st, map_msg\\n res = self.es.index(\\n index=kwargs.get(\\\"index\\\"),\\n doc_type=doc['_type'],\\n body=doc, # message\\n timestamp=datetime.datetime.utcnow(), # set to current time\\n consistency='one', # do not wait for quorum / all shards\\n replication='async', # async\\n ttl=ELASTICSEARCH_TTL) # as defined in settings\\n return True, res\\n except TransportError, e:\\n # fail silently - just log and die ...\\n message = 'Error in indexing, host: {}, unable to index'.format(\\n ELASTICSEARCH_CONN)\\n if ADD_LOG_FAILURES:\\n LOGGER.exception(e)\\n return False, message\",\n \"def create_index(self, *columns):\\n # Calling super() with older convention to support Python 2.7 & 2.6.\\n super(SqliteSource, self).create_index(*columns)\",\n \"def create_index(self, *columns):\\n # Calling super() with older convention to support Python 2.7 & 2.6.\\n super(SqliteSource, self).create_index(*columns)\",\n \"def __create_new_index(self, index, index_id, begin_timestamp):\\n index_name = index + \\\"_\\\" + str(index_id)\\n target_config = index + \\\".target_config\\\"\\n pipeline_id = index + \\\".pipeline\\\"\\n mapping = {\\n \\\"user_id\\\": {\\\"type\\\": \\\"long\\\"},\\n \\\"client_id\\\": {\\\"type\\\": \\\"long\\\"},\\n \\\"partner_id\\\": {\\\"type\\\": \\\"long\\\"},\\n \\\"module\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"page\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"uri\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"app_type\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"created_at\\\": {\\\"type\\\": \\\"date\\\"},\\n \\\"request_time\\\": {\\\"type\\\": \\\"date\\\"},\\n \\\"duration\\\": {\\\"type\\\": \\\"long\\\"},\\n }\\n body = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 1,\\n },\\n \\\"mappings\\\": {\\n \\\"docs\\\": {\\n \\\"properties\\\": mapping\\n }\\n },\\n }\\n self.client.indices.create(index=index_name, body=body)\\n self.client.index(\\n index=target_config,\\n doc_type=\\\"config\\\",\\n id=index_id,\\n body={\\\"index_id\\\": index_id, \\\"begin_timestamp\\\": begin_timestamp},\\n )\\n\\n return index_id\",\n \"def get_sql_create_index(tablename, schema, if_not_exists=False):\\n if not schema.get(\\\"columns\\\"):\\n raise ValueError(\\\"no columns defined\\\")\\n if len(schema[\\\"columns\\\"]) != len(set(schema[\\\"columns\\\"])):\\n raise ValueError(\\\"same column given more than once\\\")\\n sql = [\\\"CREATE\\\"]\\n if schema.get(\\\"unique\\\"):\\n sql.append(\\\"UNIQUE\\\")\\n sql.append(\\\"INDEX\\\")\\n if if_not_exists:\\n sql.append(\\\"IF NOT EXISTS\\\")\\n sql.append('\\\"%s\\\" ON \\\"%s\\\"' % (schema[\\\"name\\\"], tablename))\\n sql.append(\\\"(%s)\\\" % \\\",\\\".join([f'\\\"{c}\\\"' for c in schema[\\\"columns\\\"]]))\\n return \\\" \\\".join(sql)\",\n \"def test_recreate_index_that_exists(self):\\n indices = self.elasticsearch_cls().indices\\n indices.exists.return_value = True\\n\\n index_name = 'abcd'\\n self.client._recreate_index(index_name)\\n indices.delete.assert_called_once_with(index_name)\\n indices.create.assert_called_once_with(index_name)\",\n \"def entities_write_index(schema):\\n schema = model.get(schema)\\n return schema_index(schema, SETTINGS.INDEX_WRITE)\",\n \"def post(self):\\n args = table_index_create.parse_args(req=None, strict=False)\\n return DdlServices.create_table_index(**args)\",\n \"def test_string_map_value_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_string_map_value_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_map_value_index, \\\"foobar\\\")\\n\\t)\",\n \"def ParseIndex(self, text):\\n taxonStart = 0\\n taxonStop = len(self.alignment) - 1\\n columnStart = 0\\n columnStop = self.alignment.get_alignment_length() - 1\\n if (',' not in text):\\n self.AlertMessage('Invalid index format. (taxa or columns missing)', 'high')\\n return (-1,-1,-1,-1)\\n else:\\n text = text.strip()\\n indices = text.split(',')\\n if (len(indices) > 2):\\n self.AlertMessage('Invalid index format. (too many fields)', 'high')\\n return (-1,-1,-1,-1)\\n else:\\n if (':' in indices[0]): #there is a range specified in the taxon index\\n taxonIndices = indices[0].split(':')\\n if (taxonIndices[0]): #a start taxon is specified\\n try:\\n taxonStart = int(taxonIndices[0].strip())\\n except:\\n self.AlertMessage('Invalid index format. (taxon start index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n if (taxonIndices[1]): #a stop taxon is specified\\n try:\\n taxonStop = int(taxonIndices[1].strip())\\n except:\\n self.AlertMessage('Invalid index format. (taxon stop index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n elif (indices[0]): #a single taxon is specified\\n try:\\n taxonStart = int(indices[0].strip())\\n taxonStop = int(indices[0].strip())\\n except:\\n self.AlertMessage('Invalid index format. (taxon start or stop index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n if (':' in indices[1]): #there is a range specified in the taxon index\\n columnIndices = indices[1].split(':')\\n if (columnIndices[0]): #a start taxon is specified\\n try:\\n columnStart = int(columnIndices[0].strip())\\n except:\\n self.AlertMessage('Invalid index format. (column start index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n if (columnIndices[1]): #a stop taxon is specified\\n try:\\n columnStop = int(columnIndices[1].strip())\\n except:\\n self.AlertMessage('Invalid index format. (column stop index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n elif (indices[1]): #a single taxon is specified\\n try:\\n columnStart = int(indices[1].strip())\\n columnStop = int(indices[1].strip())\\n except:\\n self.AlertMessage('Invalid index format. (column start or stop index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n if ((0 <= taxonStart <= taxonStop) & (0 <= columnStart <= columnStop)):\\n return (taxonStart, taxonStop, columnStart, columnStop)\\n else:\\n self.AlertMessage('Invalid index range. (start > stop or index < 0)', 'high')\\n return (-1,-1,-1,-1)\",\n \"def validate_index(self):\\n schema_path = pkg_resources.resource_filename(\\n \\\"FLIR.conservator\\\", \\\"index_schema.json\\\"\\n )\\n with open(schema_path) as o:\\n schema = json.load(o)\\n\\n try:\\n with open(self.index_path) as index:\\n index_data = json.load(index)\\n jsonschema.validate(index_data, schema)\\n return True\\n except jsonschema.exceptions.ValidationError as e:\\n logger.error(e.message)\\n logger.debug(e)\\n return False\",\n \"def create_index(self):\\n\\n indice = client.IndicesClient(self.es)\\n\\n if not indice.exists(self.es_main_index):\\n indice.create(\\n index=self.es_main_index\\n )\\n\\n return True\",\n \"def test_index_has_status_in_title(self):\",\n \"def test_stochatreat_input_idx_col_str(correct_params):\\n idx_col_not_str = 0\\n with pytest.raises(TypeError):\\n stochatreat(\\n data=correct_params[\\\"data\\\"],\\n block_cols=[\\\"block\\\"],\\n treats=correct_params[\\\"treat\\\"],\\n idx_col=idx_col_not_str,\\n probs=correct_params[\\\"probs\\\"],\\n )\",\n \"def submit_data_to_es(client, doc_path, doc_contents):\\n try:\\n doc_text = str(doc_contents.decode('utf8'))\\n body = {'text': doc_text}\\n client.index(index=ES_INDEX, id=doc_path, doc_type='_doc', body=body)\\n except Exception as error: # pylint: disable=broad-except\\n logging.error('%s: %s', type(error), error)\\n sys.exit(1)\",\n \"def ensure_index(cls, *a, **ka):\\n cls._dbag.ensure_index(*a, **ka)\",\n \"def test_index(self):\\n es = elasticsearch.ElasticSearch(server='8.8.8.8',\\n user='alice',\\n password='iLoveDogs',\\n doc_type='someLogCategory')\\n\\n index = es.index\\n expected = time.strftime('logs-%Y.%m.%d')\\n\\n self.assertEqual(index, expected)\",\n \"def create_index(self):\\n if self.index_exists():\\n logger.info('Index {} already exists'.format(self.index_name))\\n logger.info('Deleting existing index')\\n self.indices_client.delete(index=self.index_name)\\n self.create_index_if_not_exist()\",\n \"def index_schema(schema, path):\\n for section in path.split(\\\"/\\\"):\\n if schema[\\\"type\\\"] != \\\"object\\\":\\n raise ValueError(\\n \\\"Only object types are supported in the schema structure, \\\"\\n \\\"but saw type %s\\\" % schema[\\\"type\\\"]\\n )\\n properties = schema[\\\"properties\\\"]\\n if section not in properties:\\n raise ValueError(\\\"Invalid path %s in user options\\\" % path)\\n schema = properties[section]\\n return schema\",\n \"def create_index(self):\\r\\n\\r\\n #### Begin functionality here\\r\\n if self.session is not None:\\r\\n self.disconnect()\\r\\n self.create_database()\\r\\n return(True)\",\n \"def test_get_table_indices(self):\\n indices = self.connection.get_table_indices(self.TABLE_NAME)\\n print(indices)\\n\\n self.assertEqual(indices[0].name, 'PRIMARY')\\n self.assertEqual(indices[1].name, 'idx_foo')\\n\\n self.assertListEqual(indices[0].columns, ['id', 'foo'])\\n self.assertListEqual(indices[1].columns, ['foo'])\\n\\n self.assertTrue(indices[0].is_primary)\\n self.assertTrue(indices[0].is_unique)\\n self.assertFalse(indices[1].is_primary)\\n self.assertFalse(indices[1].is_unique)\\n\\n # assert False\",\n \"def __init__(self):\\n super().__init__()\\n self.index_dir = self.base_dir + \\\"ticket/\\\"\\n self.index_schema = self.__get_index_schema()\\n if not os.path.exists(self.index_dir):\\n os.makedirs(self.index_dir)\\n self.indexer = index.create_in(self.index_dir, self.index_schema)\\n else:\\n self.indexer = index.open_dir(self.index_dir)\",\n \"def __init__(self):\\n super().__init__()\\n self.index_dir = self.base_dir + \\\"user/\\\"\\n self.index_schema = self.__get_index_schema()\\n if not os.path.exists(self.index_dir):\\n os.makedirs(self.index_dir)\\n self.indexer = index.create_in(self.index_dir, self.index_schema) # creates the index\\n else:\\n self.indexer = index.open_dir(self.index_dir) # opens the index if it already exists\",\n \"def create_index(self, *columns):\\n self._assert_columns_exist(columns)\\n\\n # Build index name.\\n whitelist = lambda col: ''.join(x for x in col if x.isalnum())\\n idx_name = '_'.join(whitelist(col) for col in columns)\\n idx_name = 'idx_{0}_{1}'.format(self._table, idx_name)\\n\\n # Build column names.\\n col_names = [self._normalize_column(x) for x in columns]\\n col_names = ', '.join(col_names)\\n\\n # Prepare statement.\\n statement = 'CREATE INDEX IF NOT EXISTS {0} ON {1} ({2})'\\n statement = statement.format(idx_name, self._table, col_names)\\n\\n # Create index.\\n cursor = self._connection.cursor()\\n cursor.execute(statement)\",\n \"def testCreate(self):\\n kinds = ('tiny', 'small', 'medium', 'large')\\n ft = (False, True)\\n in_table = self.in_tables_v1[0]\\n for kind, binary, zipped in product(kinds, ft, ft):\\n dirs = self.get_dataset('create[_r]', kind, binary, zipped)[1]\\n ds, ds_dir = self.get_dataset('create', kind, binary, zipped)\\n ds_r, ds_dir_r = self.get_dataset('create_r', kind, binary, zipped)\\n where = str.format('({0}, {1}, {2}); see {3}, {4} ' +\\n 'for log files and index/chunk files',\\n kind, 'binary' if binary else 'text',\\n 'zipped' if zipped else 'unzipped',\\n os.path.join(self.out_root, 'test*.log'), dirs)\\n remove_dirs(ds_dir, ds_dir_r)\\n cleaner = FileCleaner(preserve=self.preserve)\\n try:\\n cleaner.add_local(ds_dir, on_err=False, on_success=True)\\n self._create(in_table, ds, kind, binary, zipped, False)\\n if not self.local:\\n cleaner.add_local(ds_dir_r, on_err=False, on_success=True)\\n self._create(in_table, ds_r, kind, binary, zipped, True)\\n # Compare resulting indexes\\n msg = chunk_index_diff(os.path.join(ds_dir, 'index.ci'),\\n os.path.join(ds_dir_r, 'index.ci'))\\n if msg != None:\\n cleaner.clear()\\n self.fail('Locally/cluster generated indexes differ ' +\\n where + ': ' + msg)\\n except subprocess.CalledProcessError:\\n self.fail('Chunk index creation failed ' + where + '.')\\n cleaner.cleanup(after_error=False)\",\n \"def __init__(self, create_index=True, online=True):\\n self.online = online\\n index_exists = self.index_exists()\\n if create_index and not index_exists:\\n self.create_index()\",\n \"def from_texts(\\n cls,\\n texts: List[str],\\n embedding: Embeddings,\\n metadatas: Optional[List[dict]] = None,\\n **kwargs: Any,\\n ) -> ElasticVectorSearch:\\n elasticsearch_url = get_from_dict_or_env(\\n kwargs, \\\"elasticsearch_url\\\", \\\"ELASTICSEARCH_URL\\\"\\n )\\n try:\\n import elasticsearch\\n from elasticsearch.exceptions import NotFoundError\\n from elasticsearch.helpers import bulk\\n except ImportError:\\n raise ValueError(\\n \\\"Could not import elasticsearch python package. \\\"\\n \\\"Please install it with `pip install elasticsearch`.\\\"\\n )\\n try:\\n client = elasticsearch.Elasticsearch(elasticsearch_url)\\n except ValueError as e:\\n raise ValueError(\\n \\\"Your elasticsearch client string is misformatted. \\\" f\\\"Got error: {e} \\\"\\n )\\n index_name = kwargs.get(\\\"index_name\\\", uuid.uuid4().hex)\\n embeddings = embedding.embed_documents(texts)\\n dim = len(embeddings[0])\\n mapping = _default_text_mapping(dim)\\n\\n # check to see if the index already exists\\n try:\\n client.indices.get(index=index_name)\\n except NotFoundError:\\n # TODO would be nice to create index before embedding,\\n # just to save expensive steps for last\\n client.indices.create(index=index_name, mappings=mapping)\\n\\n requests = []\\n for i, text in enumerate(texts):\\n metadata = metadatas[i] if metadatas else {}\\n request = {\\n \\\"_op_type\\\": \\\"index\\\",\\n \\\"_index\\\": index_name,\\n \\\"vector\\\": embeddings[i],\\n \\\"text\\\": text,\\n \\\"metadata\\\": metadata,\\n }\\n requests.append(request)\\n bulk(client, requests)\\n client.indices.refresh(index=index_name)\\n return cls(elasticsearch_url, index_name, embedding)\",\n \"def create_index(self, *columns):\\n self._assert_columns_exist(columns)\\n\\n # Build index name.\\n whitelist = lambda col: ''.join(x for x in col if x.isalnum())\\n idx_name = '_'.join(whitelist(col) for col in columns)\\n idx_name = 'idx_{0}_{1}'.format(self._table, idx_name)\\n\\n # Build column names.\\n col_names = [self._normalize_column(x) for x in columns]\\n col_names = ', '.join(col_names)\\n\\n # Prepare statement.\\n statement = 'CREATE INDEX IF NOT EXISTS {0} ON {1} ({2})'\\n statement = statement.format(idx_name, self._table, col_names)\\n\\n # Create index.\\n cursor = self._connection.cursor()\\n cursor.execute('PRAGMA synchronous=OFF')\\n cursor.execute(statement)\",\n \"def test_schema():\\n return 'test_schema'\",\n \"def test_index_includes_practice(self):\\n self.normal_api.create(\\n 'Practice',\\n name=u'Indexed Practice',\\n summary=u\\\"R\\\\xf8ckin'\\\",\\n tags=['super', u'c\\\\xf8\\\\xf8l', 'tagone'],\\n subjects=['math', 'history', 'reading'],\\n min_grade=0,\\n max_grade=13,\\n type='text',\\n body=u\\\"R\\\\xf8ckin'\\\",\\n youtube_id='https://www.youtube.com/watch?v=6sJqTDaOrTg',\\n has_files=True,\\n pending=False,\\n listed=True,\\n )\\n\\n result_dicts = [util.search_document_to_dict(doc)\\n for doc in self.search_index.get_range()]\\n result_kinds = [Model.get_kind(d['uid']) for d in result_dicts]\\n self.assertIn('Practice', result_kinds)\",\n \"def index_writer_init(idx_dir=\\\"Wiki_index\\\"):\\n try:\\n assert type(idx_dir) is str\\n except AssertionError:\\n raise TypeError\\n\\n try:\\n assert idx_dir != \\\"\\\"\\n except AssertionError:\\n raise ValueError\\n\\n # Creazione dello schema dei documenti da indicizzare\\n schema: Schema = Schema(title=TEXT(stored=True),\\n identifier=ID(stored=True, unique=True),\\n content=TEXT(stored=True, analyzer=StemmingAnalyzer()))\\n\\n # Verifica dell'esistenza della cartella dell'indice\\n if not path.exists(idx_dir):\\n # In caso la cartella non esista viene creata\\n mkdir(idx_dir)\\n\\n # Creazione dell'indice all'interno della cartella designata\\n index = create_in(idx_dir, schema)\\n\\n # La funzione restituisce un oggetto in grado di inserire (scrivere) documenti all'interno dell'indice\\n return index.writer()\",\n \"def create_indices(self) -> None:\\n self.client.indices.create(\\n index=\\\"business\\\",\\n body=BUSINESS_MAPPINGS\\n )\\n self.client.indices.create(\\n index=\\\"review\\\",\\n body=REVIEW_MAPPINGS\\n )\\n self.client.indices.create(\\n index=\\\"tip\\\",\\n body=TIP_MAPPINGS\\n )\",\n \"def T1(request):\\n T = _get_test_table()\\n if request.param:\\n T.add_index(\\\"a\\\")\\n return T\",\n \"def test_spanner_indexer_implementation_basic(testing_indexer):\\n codec = IdCodec()\\n\\n record = {\\\"org_id\\\": 55555, \\\"string\\\": get_random_string(10)}\\n testing_indexer.record(\\n use_case_id=UseCaseKey.PERFORMANCE, org_id=record[\\\"org_id\\\"], string=record[\\\"string\\\"]\\n )\\n\\n with testing_indexer.database.snapshot() as snapshot:\\n result = snapshot.read(\\n testing_indexer._get_table_name(UseCaseKey.PERFORMANCE),\\n columns=[\\\"id\\\"],\\n keyset=spanner.KeySet(keys=[[record[\\\"org_id\\\"], record[\\\"string\\\"]]]),\\n index=testing_indexer._get_unique_org_string_index_name(UseCaseKey.PERFORMANCE),\\n )\\n\\n all_results = list(result)\\n encoded_id = all_results[0][0]\\n decoded_id = codec.decode(all_results[0][0])\\n assert len(all_results) == 1\\n\\n indexer_resolved_id = testing_indexer.resolve(\\n use_case_id=UseCaseKey.PERFORMANCE, org_id=record[\\\"org_id\\\"], string=record[\\\"string\\\"]\\n )\\n assert indexer_resolved_id is not None\\n assert indexer_resolved_id == decoded_id\\n\\n indexer_reverse_resolved_string = testing_indexer.reverse_resolve(\\n use_case_id=UseCaseKey.PERFORMANCE, id=encoded_id\\n )\\n assert indexer_reverse_resolved_string is not None\\n assert indexer_reverse_resolved_string == record[\\\"string\\\"]\",\n \"def configure_index(client):\\n index_name = client.index + \\\"-\\\" + _random_id()\\n mappings = ANNOTATION_MAPPING\\n\\n if client.server_version < Version(\\\"7.0.0\\\"):\\n mappings = {client.mapping_type: mappings}\\n\\n client.conn.indices.create(\\n index_name,\\n body={\\n \\\"mappings\\\": mappings,\\n \\\"settings\\\": {\\\"analysis\\\": ANALYSIS_SETTINGS},\\n },\\n )\\n\\n return index_name\",\n \"def test_index_containers(self):\\n pass\",\n \"def test_indexes_arent_allowed_on_models_with_multiple_primary_keys(self):\",\n \"def create(\\n self,\\n index: _models.SearchIndex,\\n request_options: Optional[_models.RequestOptions] = None,\\n *,\\n content_type: str = \\\"application/json\\\",\\n **kwargs: Any\\n ) -> _models.SearchIndex:\",\n \"def __init__(self, lang: str, db: PassageDB, analyzer: AnyStr, index_dir=None, ram_size=2048):\\n super().__init__()\\n lucene.initVM(vmargs=['-Djava.awt.headless=true'])\\n\\n self.idx_dir = self.get_index_name(lang, index_dir)\\n logging.info(f\\\"Creating index in {self.idx_dir}!\\\")\\n\\n self.db = db\\n\\n # stores index files, poor concurency try NIOFSDirectory instead\\n store = SimpleFSDirectory(Paths.get(self.idx_dir))\\n # limit max. number of tokens per document.\\n # analyzer will not consume more tokens than that\\n # analyzer = LimitTokenCountAnalyzer(analyzer, 1048576)\\n # configuration for index writer\\n config = IndexWriterConfig(analyzers[analyzer]())\\n # creates or overwrites index\\n config.setOpenMode(IndexWriterConfig.OpenMode.CREATE)\\n # setting similarity BM25Similarity(k1=1.2,b=0.75)\\n similarity = BM25Similarity(self.k1, self.b)\\n config.setSimilarity(similarity)\\n config.setRAMBufferSizeMB(float(ram_size))\\n # create index writer\\n self.writer = IndexWriter(store, config)\\n\\n self.ftdata = FieldType()\\n self.ftmeta = FieldType()\\n # IndexSearcher will return value of the field\\n self.ftdata.setStored(False)\\n self.ftmeta.setStored(True)\\n # will be analyzed by Analyzer\\n self.ftdata.setTokenized(True)\\n self.ftmeta.setTokenized(False)\\n # what information are stored (probably DOCS would be sufficient)\\n # DOCS: Only documents are indexed: term frequencies and positions are omitted.\\n # Phrase and other positional queries on the field will throw an exception,\\n # and scoring will behave as if any term in the document appears only once.\\n # DOCS_AND_FREQS: Only documents and term frequencies are indexed: positions are\\n # omitted. This enables normal scoring, except Phrase and other positional\\n # queries will throw an exception.\\n # DOCS_AND_FREQS_AND_POSITIONS: Indexes documents, frequencies and positions.\\n # This is a typical default for full-text search: full scoring is enabled\\n # and positional queries are supported.\\n self.ftdata.setIndexOptions(IndexOptions.DOCS_AND_FREQS)\\n self.ftmeta.setIndexOptions(IndexOptions.DOCS)\\n # instantiate some reusable objects\\n self.doc = Document()\\n # Id cannot be reused because there is multiple values\\n # I could store list of fields and add one if its not enough\\n #self.fieldId = Field(\\\"id\\\", \\\"dummy\\\", self.ftmeta)\\n self.fieldTitle = Field(\\\"title\\\", \\\"dummy\\\", self.ftdata)\\n self.doc.add(self.fieldTitle)\\n self.fieldContext = Field(\\\"context\\\", \\\"dummy\\\", self.ftdata)\\n self.doc.add(self.fieldContext)\\n self.fieldId = Field(\\\"id\\\", \\\"dummy\\\", self.ftmeta)\\n self.doc.add(self.fieldId)\",\n \"def createindexes():\\n index = [{}, {}, {}, {}]\\n readcorpus(index)\\n buildindex4(index[2], index[3])\\n writeindextofile(index)\\n return index\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8399966","0.68847084","0.6653272","0.65279675","0.6232708","0.61420846","0.6077263","0.5866493","0.57903063","0.57808346","0.5726722","0.57109994","0.5680334","0.5675512","0.56721723","0.56721723","0.5643351","0.5582189","0.5579941","0.55570096","0.55531275","0.5527116","0.54886883","0.54877126","0.5487446","0.5478212","0.54751813","0.546784","0.5466714","0.54482996","0.5405622","0.5355258","0.5355212","0.5324958","0.53229547","0.53022987","0.53002053","0.52869326","0.52845544","0.5283613","0.52827203","0.5273517","0.5254044","0.52389497","0.5233348","0.52109796","0.5198905","0.519187","0.51894325","0.5188682","0.51709056","0.5166053","0.51609993","0.51575303","0.515481","0.51547974","0.51489407","0.5146393","0.51449645","0.5141715","0.5139209","0.5129518","0.5129518","0.5119955","0.5119763","0.5107558","0.5103018","0.5092141","0.5081611","0.50731593","0.506154","0.5056314","0.5042877","0.50380725","0.5033304","0.50268","0.5025925","0.50231546","0.5019425","0.5017786","0.501659","0.5008048","0.49970362","0.49918947","0.49839306","0.4967724","0.49662817","0.49643904","0.4953931","0.49439666","0.49430078","0.49406797","0.4928685","0.49265137","0.49260044","0.49253407","0.49243945","0.4913686","0.4908906","0.49044022"],"string":"[\n \"0.8399966\",\n \"0.68847084\",\n \"0.6653272\",\n \"0.65279675\",\n \"0.6232708\",\n \"0.61420846\",\n \"0.6077263\",\n \"0.5866493\",\n \"0.57903063\",\n \"0.57808346\",\n \"0.5726722\",\n \"0.57109994\",\n \"0.5680334\",\n \"0.5675512\",\n \"0.56721723\",\n \"0.56721723\",\n \"0.5643351\",\n \"0.5582189\",\n \"0.5579941\",\n \"0.55570096\",\n \"0.55531275\",\n \"0.5527116\",\n \"0.54886883\",\n \"0.54877126\",\n \"0.5487446\",\n \"0.5478212\",\n \"0.54751813\",\n \"0.546784\",\n \"0.5466714\",\n \"0.54482996\",\n \"0.5405622\",\n \"0.5355258\",\n \"0.5355212\",\n \"0.5324958\",\n \"0.53229547\",\n \"0.53022987\",\n \"0.53002053\",\n \"0.52869326\",\n \"0.52845544\",\n \"0.5283613\",\n \"0.52827203\",\n \"0.5273517\",\n \"0.5254044\",\n \"0.52389497\",\n \"0.5233348\",\n \"0.52109796\",\n \"0.5198905\",\n \"0.519187\",\n \"0.51894325\",\n \"0.5188682\",\n \"0.51709056\",\n \"0.5166053\",\n \"0.51609993\",\n \"0.51575303\",\n \"0.515481\",\n \"0.51547974\",\n \"0.51489407\",\n \"0.5146393\",\n \"0.51449645\",\n \"0.5141715\",\n \"0.5139209\",\n \"0.5129518\",\n \"0.5129518\",\n \"0.5119955\",\n \"0.5119763\",\n \"0.5107558\",\n \"0.5103018\",\n \"0.5092141\",\n \"0.5081611\",\n \"0.50731593\",\n \"0.506154\",\n \"0.5056314\",\n \"0.5042877\",\n \"0.50380725\",\n \"0.5033304\",\n \"0.50268\",\n \"0.5025925\",\n \"0.50231546\",\n \"0.5019425\",\n \"0.5017786\",\n \"0.501659\",\n \"0.5008048\",\n \"0.49970362\",\n \"0.49918947\",\n \"0.49839306\",\n \"0.4967724\",\n \"0.49662817\",\n \"0.49643904\",\n \"0.4953931\",\n \"0.49439666\",\n \"0.49430078\",\n \"0.49406797\",\n \"0.4928685\",\n \"0.49265137\",\n \"0.49260044\",\n \"0.49253407\",\n \"0.49243945\",\n \"0.4913686\",\n \"0.4908906\",\n \"0.49044022\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8753069"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74579,"cells":{"query":{"kind":"string","value":"Tests the _create_text_index() method when a schema is not provided."},"document":{"kind":"string","value":"def test_create_text_index_noschema(self):\n actual = self.engine._create_text_index()\n expected = 'TextIndex'\n self.assertEqual(actual, expected)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_create_text_index_w_schema(self):\n actual = self.engine._create_text_index(self.engine.schema)\n expected = 'TextIndex'\n self.assertEqual(actual, expected)","def test_creating_index_type(self):","def test_string_index():\n\tlib.backup_and_restore(\n\t\tlambda context: create_indexes(lib.create_string_index),\n\t\tNone,\n\t\tlambda context: check_indexes(lib.check_simple_index, \"foobar\")\n\t)","def create_index():","def test_get_document_inexistent(empty_index):\n with pytest.raises(Exception):\n empty_index().get_document(\"123\")","def create_index(schema, index_name):\n if not os.path.exists(index_name):\n os.mkdir(index_name)\n ix = index.create_in(index_name, schema)\n print(f\"index {index_name} created successfully\")\n return ix","def test_add_documents_empty(empty_index):\n index = empty_index()\n with pytest.raises(Exception) as e_info:\n index.add_documents(\"\")\n assert e_info.value.code == \"missing_payload\"\n assert e_info.value.type == \"invalid_request\"","def test_create_index_fail_test(tcex: TcEx, monkeypatch: MonkeyPatch):\n\n # monkeypatch method\n def mp_post(*args, **kwargs): # pylint: disable=unused-argument\n return MockPost({}, ok=False)\n\n monkeypatch.setattr(tcex.session.tc, 'post', mp_post)\n\n # create index\n key = str(uuid.uuid4())\n try:\n tcex.api.tc.v2.datastore('local', key)\n assert False, 'Failed to catch error on ok=False'\n except RuntimeError:\n assert True","def test_create_index_is_called(self):\r\n assert len(self.index_calls) == 0\r\n\r\n connection._index_all_fields = False\r\n \r\n class TestIndexCreationCallTestVertex(Vertex):\r\n col1 = properties.Text(index=True)\r\n col2 = properties.Text(index=True, db_field='____column')\r\n col3 = properties.Text(db_field='____column3')\r\n\r\n assert len(self.index_calls) == 2\r\n assert 'vid' not in self.index_calls\r\n assert 'col1' in self.index_calls\r\n assert '____column' in self.index_calls\r\n assert '____column3' not in self.index_calls\r\n\r\n connection._index_all_fields = True\r\n self.index_calls = []\r\n\r\n class TestIndexCreationCallTestVertex2(Vertex):\r\n col1 = properties.Text()\r\n col2 = properties.Text(db_field='____column')\r\n\r\n assert len(self.index_calls) == 3\r\n assert 'vid' in self.index_calls\r\n assert 'col1' in self.index_calls\r\n assert '____column' in self.index_calls","def test_works_index_name(self):\n assert \"test_index-v4\" == self.search.works_index_name(self._db)","def test_invalid_text_search(aquarius_instance):\n text = \"foo_text\"\n with pytest.raises(ValueError):\n aquarius_instance.text_search(text=text, sort=\"foo_sort\")","def idx_createFieldIndex(plominoIndex, fieldname, fieldtype='TEXT', **args):\n \n if not hasattr(plominoIndex, 'createFieldIndex'):\n plominoIndex = plominoIndex.getParentDatabase().getIndex()\n \n if not fieldname in plominoIndex.indexes():\n plominoIndex.createFieldIndex(fieldname, fieldtype, **args)","def ensure_index(self, schema, language, mapping, return_index='external'):\n assert schema and language and mapping\n assert len(language) == 2\n assert return_index == 'external' or return_index == 'internal'\n\n external = self.get_external_index_name(schema, language, mapping.name)\n internal = self.get_internal_index_name(\n schema, language, mapping.name, mapping.version)\n\n return_value = return_index == 'external' and external or internal\n\n if internal in self.created_indices:\n return return_value\n\n if self.es_client.indices.exists(index=internal):\n self.created_indices.add(internal)\n return return_value\n\n # create the index\n self.es_client.indices.create(\n index=internal,\n mappings={\n 'properties': mapping.for_language(language)\n },\n settings={\n 'analysis': ANALYSIS_CONFIG,\n 'index': {\n 'number_of_shards': 1,\n 'number_of_replicas': 0,\n 'refresh_interval': '5s'\n }\n }\n )\n\n # point the alias to the new index\n self.es_client.indices.put_alias(name=external, index=internal)\n\n # cache the result\n self.created_indices.add(internal)\n\n return return_value","def create_index(self):\r\n\r\n #### Begin functionality here\r\n\r\n return()","def create_index(self, table_name, index, timeout):\n _abstract()","def create_index(self, table_name, index, timeout):\n _abstract()","def createIndex(schemaName, tableName, index):\n return \"\"\"CREATE {unique} INDEX {name} ON \\\"{schema_name}\\\".\\\"{tableName}\\\" ({fields})\"\"\".format(\n unique = \"UNIQUE\" if index.uniqueness else \"\",\n schema_name = schemaName,\n name = \"\\\"\" + tableName + \"__\" + index.name + \"\\\"\" if index.name is not None else \"\",\n tableName = tableName,\n fields = \", \".join(map(lambda index: '\"' + str(index.name) + '\"', index.fields))\n )","def test_string_list_index():\n\tlib.backup_and_restore(\n\t\tlambda context: create_indexes(lib.create_string_list_index),\n\t\tNone,\n\t\tlambda context: check_indexes(lib.check_list_index, \"foobar\")\n\t)","def test_create_bad_sample_names(self):\n # set a horrible list of sample names\n self.metadata.index = ['o()xxxx[{::::::::>', 'sample.1', 'sample.3']\n with self.assertRaises(QiitaDBColumnError):\n SampleTemplate.create(self.metadata, self.new_study)","def create_index(self):\n self.send_robust(self.es_index, data=self.es_meta)\n self.set_index_normal_settings()","def test_create_index(self, collection):\n collection.create_index(\"hello\")\n assert collection._indexes == {\"_id_\": ((\"_id\",), {(1,)})}\n\n collection.create_index(\"hello\", unique=True)\n assert collection._indexes == {\n \"_id_\": ((\"_id\",), {(1,)}),\n \"hello_1\": ((\"hello\",), {(\"there\",)}),\n }","def create_schema():\n schema = Schema(idx=ID(stored=True),\n data=STORED,\n body=TEXT(analyzer=StemmingAnalyzer()),\n )\n print(\"schema creation successful\")\n return schema","def schema_index(schema, version):\n if schema.abstract:\n raise InvalidData(\"Cannot index abstract schema: %s\" % schema)\n name = \"entity-%s\" % schema.name.lower()\n return index_name(name, version=version)","def test_create_index_swift(self):\n\n indexfile = tempfile.mktemp()\n self.addCleanup(os.unlink, indexfile)\n\n index = TroveSwiftIndexBuilder(\"short.dat\", out=indexfile)\n\n # read the index file that was created\n with open(indexfile, 'r+b') as fd:\n indextext = fd.read()\n indexlines = indextext.split('\\n')\n\n # 11 lines includes on blank line at the end\n self.assertEquals(11, len(indexlines))\n del indexlines[10]\n\n # check the first character of each line\n docs = [line[0] for line in indexlines]\n self.assertEquals(['1', '2', '3', '4', '5', '6', '7', '8', '9', '1'], docs)\n\n # check some lines from the index\n ref = \"1, 0, 31, short.dat\"\n self.assertEqual(ref, indexlines[0])\n ref = \"10, 279, 32, short.dat\"\n self.assertEqual(ref, indexlines[9])","def test_create_index(self):\n\n class Comment(Document):\n message = StringField()\n meta = {\"allow_inheritance\": True}\n\n Comment.create_index(\"message\")\n\n info = Comment.objects._collection.index_information()\n info = [\n (value[\"key\"], value.get(\"unique\", False), value.get(\"sparse\", False))\n for key, value in info.items()\n ]\n assert ([(\"_cls\", 1), (\"message\", 1)], False, False) in info","def create_new_index(self, dict_pg_info):\n # ! Setting if fun can use default setting\n ruler = Rules()\n str_conn = ruler.pg_info_rules(dict_pg_info)\n conn = psycopg2.connect(str_conn)\n\n with conn:\n with conn.cursor() as cur:\n str_create_table = \"CREATE TABLE \" + dict_pg_info['table'] + \" (path varchar PRIMARY KEY);\"\n # ! Check if table already exit\n cur.execute(str_create_table)\n cur.close()\n\n conn.close()","def create_indices():\n destroy_indices()\n\n ActionDocument._index.create(ignore=[400, 404])\n ClassificationDocument._index.create(ignore=[400, 404])\n FunctionDocument._index.create(ignore=[400, 404])\n PhaseDocument._index.create(ignore=[400, 404])\n RecordDocument._index.create(ignore=[400, 404])\n\n yield\n\n destroy_indices()","def test_get_algorithm_NotFound(self):\n searched_id = 'xyz1'\n right_list = []\n create_test_algorithm_list(right_list, 101)\n documents = []\n create_test_documents_list(right_list, documents, 101)\n index = search.Index(name=search_algorithm._INDEX_STRING)\n index.put(documents)\n # end of preparing data\n result = index.get_range(ids_only=True)\n self.assertLess(0, len(result.results), msg='The database is empty')\n algorithm = search_algorithm.get_algorithm(index, searched_id)\n self.assertEqual(1, algorithm, msg='Nonexistent Algorithm was found in 101 algorithms database')","def test_search_table_fail_if_no_page_index(self) -> None:\n with local_app.test_client() as test:\n response = test.get('/api/search/v0/table', query_string=dict(query='test'))\n self.assertEqual(response.status_code, HTTPStatus.INTERNAL_SERVER_ERROR)","def validate(self):\n\n if self.indexer_table is None:\n raise Exception('Indexed words table is not set')","def test_text(self):\n conn = self.database.connection()\n cursor = conn.cursor()\n dialect = self.database.dialect()\n dbapi = self.database.dbapi()\n query = dialect.translate('DROP TABLE test_text')\n try:\n cursor.execute(query)\n except dbapi.Error:\n conn.rollback()\n query = dialect.translate('CREATE TABLE test_text ' \\\n '( value TEXT NOT NULL )')\n cursor.execute(query)\n data = []\n query = 'INSERT INTO test_text VALUES (%s)'\n for i in range(10):\n item = random_string(100000)\n data.append(item)\n cursor.execute(query, (item,))\n query = 'SELECT * FROM test_text'\n cursor.execute(query)\n result = cursor.fetchall()\n for row in result:\n item = row[0]\n assert isinstance(item, unicode)\n assert item in data\n data.remove(item)\n query = dialect.translate('DELETE FROM test_text')\n cursor.execute(query)\n query = dialect.translate('DROP TABLE test_text')\n cursor.execute(query)\n conn.commit()","def test_creation(self):\n config = config_factory()\n db = config.arango_db\n\n # Create analyzer\n analyzer = ArangoAnalyzer(\"analyzer_sample\")\n analyzer.set_stopwords(\n language=\"english\",\n custom_stopwords=[\"stop\", \"word\"],\n include_default=False,\n )\n analyzer.type = ArangoAnalyzer._TYPE_TEXT\n\n analyzer.create(db)\n assert analyzer.__dict__ == {\n \"name\": \"analyzer_sample\",\n \"type\": \"text\",\n \"features\": [\"frequency\", \"norm\", \"position\"],\n \"locale\": \"en\",\n \"case\": \"lower\",\n \"stopwords\": [\"stop\", \"word\"],\n \"accent\": False,\n \"stemming\": True,\n \"edge_ngram\": None,\n \"delimiter\": \",\",\n \"min\": 2,\n \"max\": 5,\n \"preserve_original\": False,\n \"start_marker\": \"\",\n \"end_marker\": \"\",\n \"stem_type\": \"binary\",\n }\n assert isinstance(db.analyzer(\"analyzer_sample\"), dict)","def __init__(self, index_name, doc_type='string'):\n super(ESMigration, self).__init__()\n self.index_name = index_name\n self.doc_type = doc_type","def create_index(self):\n if self.index_exists():\n logger.info('Index {} already exists'.format(self.index_name))\n logger.info('Deleting existing index')\n self.indices_client.delete(index=self.index_name)\n self.create_index_if_not_exist()","def get_index_text(self, crate, module, impl, name):\n raise NotImplementedError","def __call__(self, index, checkFirst=False):\n schema_name = index.schema().dbname()\n index_name = index.dbname()\n cmd = 'CREATE' if not index.testFlag(index.Flags.Unique) else 'CREATE UNIQUE'\n\n cols = ['`{0}`'.format(col.field()) if col.testFlag(col.Flags.CaseSensitive)\n else '`{0}` COLLATE NOCASE'.format(col.field())\n for col in index.columns()]\n\n cmd = '{0} INDEX `{1}` ON `{2}` ({3})'.format(cmd, index_name, schema_name, ', '.join(cols))\n\n if checkFirst:\n cmd = \"\"\"\\\n DO $$\n BEGIN\n IF NOT EXISTS (\n SELECT 1\n FROM pg_indexes\n WHERE schemaname = 'public'\n AND indexname = '{0}'\n ) THEN {1};\n END IF;\n END$$;\n \"\"\".format(index_name, cmd)\n else:\n cmd += ';'\n\n return cmd, {}","def build_index():\n pass","def test_search_table_fail_if_no_query(self) -> None:\n with local_app.test_client() as test:\n response = test.get('/api/search/v0/table', query_string=dict(page_index='0'))\n self.assertEqual(response.status_code, HTTPStatus.INTERNAL_SERVER_ERROR)","def create_index(es_object, index_name):\n created = False\n \"\"\" index settings \"\"\"\n settings = {\n \"settings\": {\n \"number_of_shards\": 1,\n \"number_of_replicas\": 0\n },\n \"mappings\": {\n \"physicians\": {\n \"dynamic\": \"strict\",\n \"properties\": {\n \"overview\": {\n \"type\": \"text\"\n },\n \"full_name\": {\n \"type\": \"text\"\n },\n \"years_of_practice\": {\n \"type\": \"text\"\n },\n \"language\": {\n \"type\": \"text\"\n },\n \"office_location\": {\n \"type\": \"text\"\n },\n \"hospital_affiliation\": {\n \"type\": \"text\"\n },\n \"specialties\": {\n \"type\": \"text\"\n },\n \"education_and_medical_training\": {\n \"type\": \"text\"\n },\n \"certification_and_licensure\": {\n \"type\": \"text\"\n },\n }\n }\n }\n }\n\n try:\n if not es_object.indices.exists(index_name):\n # Ignore 400 means to ignore \"Index Already Exist\" error.\n es_object.indices.create(index=index_name, ignore=400, body=settings)\n print('Created Index')\n created = True\n except Exception as ex:\n print(str(ex))\n finally:\n return created","def _create_indices(cls):\r\n from thunderdome.connection import _hosts, _index_all_fields, create_key_index\r\n \r\n if not _hosts: return\r\n for column in cls._columns.values():\r\n if column.index or _index_all_fields:\r\n create_key_index(column.db_field_name)","def __init__(self, create_index=True, online=True):\n self.online = online\n index_exists = self.index_exists()\n if create_index and not index_exists:\n self.create_index()","def test_local_index_no_range_key(engine):\n with pytest.raises(ValueError):\n class Model(engine.model):\n id = Column(UUID, hash_key=True)\n another = Column(UUID)\n by_another = LocalSecondaryIndex(range_key=\"another\")","def test_word_not_found_in_file(self):\n\n # create indexer object\n indexer = indexing_module.IndexModule()\n\n # index the location (storage/data/test/word_not_found)\n indexer.index(_path_prefix + 'word_not_found')\n\n # search for few words and check that the result is empty\n result = indexer.search(\"hello\")\n print(result)\n self.assertTrue(result == {})\n\n result = indexer.search(\"world\")\n self.assertTrue(result == {})","def create_index(self):\n\n indice = client.IndicesClient(self.es)\n\n if not indice.exists(self.es_main_index):\n indice.create(\n index=self.es_main_index\n )\n\n return True","def get_sql_create_index(tablename, schema, if_not_exists=False):\n if not schema.get(\"columns\"):\n raise ValueError(\"no columns defined\")\n if len(schema[\"columns\"]) != len(set(schema[\"columns\"])):\n raise ValueError(\"same column given more than once\")\n sql = [\"CREATE\"]\n if schema.get(\"unique\"):\n sql.append(\"UNIQUE\")\n sql.append(\"INDEX\")\n if if_not_exists:\n sql.append(\"IF NOT EXISTS\")\n sql.append('\"%s\" ON \"%s\"' % (schema[\"name\"], tablename))\n sql.append(\"(%s)\" % \",\".join([f'\"{c}\"' for c in schema[\"columns\"]]))\n return \" \".join(sql)","def checkCatalogIndexes(self, trans):\n for lang in trans.keys():\n index_id = 'objecttrans_%s' % lang\n if not index_id in self.indexes():\n self.__createObjecttransIndex(index_id)\n index_id = 'objectname_%s' % lang\n if not index_id in self.indexes():\n self.__createObjectnameIndex(index_id)","def setUp(self):\n body = {\n \"settings\": {\n \"number_of_shards\": 1,\n \"number_of_replicas\": 0\n }\n }\n self.assertTrue(self.es.create_index('contacts_esclient_test', body))\n self.assertFalse(self.es.create_index('contacts_esclient_test', body))\n\n self.assertTrue(self.es.create_index('contacts_esclient_test2', body))\n self.assertFalse(self.es.create_index('contacts_esclient_test2', body))\n\n\n \"\"\" Index some test data \"\"\"\n data = {\"name\": \"Joe Tester\",\"age\": 21, \"sex\": \"male\"}\n self.assertTrue(self.es.index(\"contacts_esclient_test\", \"person\", body=data,\n docid=1))\n data = {\"name\": \"Joe Schmoe\",\"age\": 17, \"sex\": \"male\"}\n self.assertTrue(self.es.index(\"contacts_esclient_test\", \"person\", body=data,\n docid=2))\n\n self.assertTrue(self.es.refresh('contacts_esclient_test'))","def create(\n self,\n index: IO,\n request_options: Optional[_models.RequestOptions] = None,\n *,\n content_type: str = \"application/json\",\n **kwargs: Any\n ) -> _models.SearchIndex:","def test_get_algorithm_Empty(self):\n searched_id = 'xyz1'\n index = search.Index(name=search_algorithm._INDEX_STRING)\n result = index.get_range(ids_only=True)\n self.assertEqual(0, len(result.results), msg='The database is not empty')\n algorithm = search_algorithm.get_algorithm(index, searched_id)\n self.assertEqual(1, algorithm, msg='Non existent Algorithm was found in empty database')","def get_index_text(self, name):\n raise NotImplementedError('must be implemented in subclasses')","def test_stochatreat_input_idx_col_str(correct_params):\n idx_col_not_str = 0\n with pytest.raises(TypeError):\n stochatreat(\n data=correct_params[\"data\"],\n block_cols=[\"block\"],\n treats=correct_params[\"treat\"],\n idx_col=idx_col_not_str,\n probs=correct_params[\"probs\"],\n )","def create_index(cls, engine):\n\n reg_imei = db.Index('reg_imei_index', cls.imei, postgresql_concurrently=True)\n reg_imei.create(bind=engine)\n\n reg_normalized_imei = db.Index('reg_normalized_imei_index', cls.normalized_imei, postgresql_concurrently=True)\n reg_normalized_imei.create(bind=engine)","def create_index(self, db_name):\n\t\tindex_func_path = self._get_index_func_filepath(db_name)\n\t\t\n\t\tif os.path.isfile(index_func_path):\n\t\t\t# create index request payload from predefined file\t\n\t\t\twith open(index_func_path, 'r') as content_file:\n\t\t\t\tpayload = content_file.read()\n\t\t\n\t\t\tprint (\"Create index using function in: {}\".format(index_func_path))\n\t\t\turl = \"https://{}/{}/_design/view\".format(\n\t\t\t\tself.cloudanthost, db_name)\n\t\t\tresponse = self.r.put(url, data=payload)\n\t\t\tassert response.status_code == 201","def __init__(self):\n super().__init__()\n self.index_dir = self.base_dir + \"user/\"\n self.index_schema = self.__get_index_schema()\n if not os.path.exists(self.index_dir):\n os.makedirs(self.index_dir)\n self.indexer = index.create_in(self.index_dir, self.index_schema) # creates the index\n else:\n self.indexer = index.open_dir(self.index_dir) # opens the index if it already exists","def create_index(self, *columns):\n # Calling super() with older convention to support Python 2.7 & 2.6.\n super(SqliteSource, self).create_index(*columns)","def create_index(self, *columns):\n # Calling super() with older convention to support Python 2.7 & 2.6.\n super(SqliteSource, self).create_index(*columns)","def _apply_index_op(db, op):\n if 'createIndexes' not in op['o']:\n return\n o = op['o']\n coll_name = o['createIndexes']\n key = list(o['key'].items())\n name = o['name']\n return db[coll_name].create_index(key, name=name)","def test_export_index(self):","def test_raise_exception_bad_indices(self):\n print(\"Testing exception is raised if indices are bad\")\n\n with self.assertRaises(Exception) as no_index:\n get_region_data(self.wmo_boxes, self.float_name, self.config,\n [], self.pres)\n\n self.assertTrue('NO DATA FOUND' in str(no_index.exception))\n\n with self.assertRaises(Exception) as big_index:\n get_region_data(self.wmo_boxes, self.float_name, self.config,\n [99999999999999999], self.pres)\n\n self.assertTrue('NO DATA FOUND' in str(big_index.exception))","def validate_index(self):\n schema_path = pkg_resources.resource_filename(\n \"FLIR.conservator\", \"index_schema.json\"\n )\n with open(schema_path) as o:\n schema = json.load(o)\n\n try:\n with open(self.index_path) as index:\n index_data = json.load(index)\n jsonschema.validate(index_data, schema)\n return True\n except jsonschema.exceptions.ValidationError as e:\n logger.error(e.message)\n logger.debug(e)\n return False","def define_index_field(DomainName=None, IndexField=None):\n pass","def __init__(self):\n super().__init__()\n self.index_dir = self.base_dir + \"ticket/\"\n self.index_schema = self.__get_index_schema()\n if not os.path.exists(self.index_dir):\n os.makedirs(self.index_dir)\n self.indexer = index.create_in(self.index_dir, self.index_schema)\n else:\n self.indexer = index.open_dir(self.index_dir)","def ensure_index(cls, *a, **ka):\n cls._dbag.ensure_index(*a, **ka)","def init_index(self):\n raise NotImplementedError","def test_should_return_correct_gremlin_for_key_index_creation(self):\r\n expected = 'g.createKeyIndex(\"email\", Vertex.class)'\r\n key_index = self.spec_parser.parse_key_index(self.key_index_spec)\r\n assert key_index.gremlin == expected","def ensure_indexes(self):\n self.db_connection.ensure_indexes()","def test_AlgorithmsIdHandler_GET_NotFound(self):\n searchedId='xyz1'\n right_list = []\n create_test_algorithm_list(right_list, 101)\n documents = []\n create_test_documents_list(right_list, documents, 101)\n index = search.Index(name=search_algorithm._INDEX_STRING)\n index.put(documents)\n # end of preparing data\n result = index.get_range(ids_only=True)\n self.assertLess(0, len(result.results), msg='The database is empty')\n response = self.testapp.get('/algorithms/' + searchedId, expect_errors=True)\n self.assertEqual(404, response.status_int, msg='Non existent Algorithm was found in empty database')\n self.assertEqual('application/json', response.content_type)\n self.assertIn('Algorithm Not Found', response.normal_body.decode(encoding='UTF-8'))","def test_missing_foreign_key_indices(db_session):\n\n from sqlalchemy_utils.functions import non_indexed_foreign_keys\n from kotti import metadata\n\n assert non_indexed_foreign_keys(metadata) == {}","def create_schema(self, schema: str):\n return","def test_geo_index():\n\tlib.backup_and_restore(\n\t\tlambda context: create_indexes(lib.create_geo_index),\n\t\tNone,\n\t\tlambda context: check_indexes(lib.check_geo_index, (0.0, 0.0))\n\t)","def create_index(es_object, index_name):\n created = False\n # index settings\n # the reason why we need mappings is avoid corrupting your data\n settings = {\n \"settings\": {\n \"number_of_shards\": 1,\n \"number_of_replicas\": 0\n },\n \"mappings\": {\n # custom type called foods\n \"foods\": {\n \"properties\": {\n # Specify that the food_name field contains text values.\n \"food_name\": {\n \"type\": \"text\",\n \"fields\": {\n \"raw\":{ \n \"type\": \"keyword\"\n } # The food_name.raw field can be used for sorting and aggregations\n }\n },\n # Specify that the categories field contains text values.\n \"categories\": {\n \"type\": \"text\",\n \"fields\": {\n \"raw\":{ \n \"type\": \"keyword\"\n } # The categories.raw field can be used for sorting and aggregations\n }\n },\n # Specify that the calories field contains integer values.\n \"calories\": {\n \"type\": \"integer\"\n },\n \"protein\": {\n \"type\": \"integer\"\n },\n \"carbs\": {\n \"type\": \"integer\"\n },\n \"fat\": {\n \"type\": \"integer\"\n }\n \n },\n }\n }\n }\n try:\n if not es_object.indices.exists(index_name):\n # Ignore 400 means to ignore \"Index Already Exist\" error.\n es_object.indices.create(index=index_name, ignore=400, body=settings)\n print('Created Index')\n created = True\n except Exception as ex:\n print(str(ex))\n finally:\n return created","def test_create_engine_no_adapters():\n engine = create_engine(\"shillelagh://\")\n\n with pytest.raises(ProgrammingError) as excinfo:\n Table(\"dummy://\", MetaData(bind=engine), autoload=True)\n assert str(excinfo.value) == \"Unsupported table: dummy://\"","def test_index(self):","def test_secondary_index(self):\n session = self.prepare()\n\n session.execute(\"CREATE TABLE t (id int PRIMARY KEY, v int, v2 text, v3 decimal)\")\n session.execute((\"CREATE MATERIALIZED VIEW t_by_v AS SELECT * FROM t \"\n \"WHERE v IS NOT NULL AND id IS NOT NULL PRIMARY KEY (v, id)\"))\n assert_invalid(session, \"CREATE INDEX ON t_by_v (v2)\",\n \"Secondary indexes are not supported on materialized views\")","def test_integer_index():\n\tlib.backup_and_restore(\n\t\tlambda context: create_indexes(lib.create_integer_index),\n\t\tNone,\n\t\tlambda context: check_indexes(lib.check_simple_index, 12345)\n\t)","def search_engine_index(request):\n return {'NO_INDEX': settings.NO_INDEX}","def test_table_name(self):\n with self.assertRaises(IncompetentQiitaDeveloperError):\n MetadataTemplate._table_name(self.study)","def test_recreate_index_that_exists(self):\n indices = self.elasticsearch_cls().indices\n indices.exists.return_value = True\n\n index_name = 'abcd'\n self.client._recreate_index(index_name)\n indices.delete.assert_called_once_with(index_name)\n indices.create.assert_called_once_with(index_name)","def init_index(clear=False):\n return _run_indexer_func(\"init_index\", clear)","def ensure_indexes(self, cls):\n return self.impl.ensure_indexes(cls)","def __create_new_index(self, index, index_id, begin_timestamp):\n index_name = index + \"_\" + str(index_id)\n target_config = index + \".target_config\"\n pipeline_id = index + \".pipeline\"\n mapping = {\n \"user_id\": {\"type\": \"long\"},\n \"client_id\": {\"type\": \"long\"},\n \"partner_id\": {\"type\": \"long\"},\n \"module\": {\"type\": \"keyword\"},\n \"page\": {\"type\": \"keyword\"},\n \"uri\": {\"type\": \"keyword\"},\n \"app_type\": {\"type\": \"keyword\"},\n \"created_at\": {\"type\": \"date\"},\n \"request_time\": {\"type\": \"date\"},\n \"duration\": {\"type\": \"long\"},\n }\n body = {\n \"settings\": {\n \"number_of_shards\": 1,\n \"number_of_replicas\": 1,\n },\n \"mappings\": {\n \"docs\": {\n \"properties\": mapping\n }\n },\n }\n self.client.indices.create(index=index_name, body=body)\n self.client.index(\n index=target_config,\n doc_type=\"config\",\n id=index_id,\n body={\"index_id\": index_id, \"begin_timestamp\": begin_timestamp},\n )\n\n return index_id","def test_string_map_key_index():\n\tlib.backup_and_restore(\n\t\tlambda context: create_indexes(lib.create_string_map_key_index),\n\t\tNone,\n\t\tlambda context: check_indexes(lib.check_map_key_index, \"foobar\")\n\t)","def test_existent_term_search(self):\n results = self.searcher.search(\"coach\")\n expected_results = 3\n\n self.assertEqual(results[0].indexable.docid, expected_results)","def test_index_over_non_existing_field(self, collection):\n collection.create_index(\n [(\"hello\", Database.DESCENDING), (\"idontexist\", Database.ASCENDING)],\n unique=True,\n )\n\n collection.insert_many([{\"hello\": \"here\"}, {\"hello\": 2}])\n assert collection._indexes == {\n \"_id_\": ((\"_id\",), {(1,), (2,), (3,)}),\n \"hello_1_idontexist_1\": (\n (\"hello\", \"idontexist\"),\n {(\"there\", None), (\"here\", None), (2, None)},\n ),\n }\n assert collection.find({}, selection={\"hello\": 1, \"idontexist\": 1}) == [\n {\"_id\": 1, \"hello\": \"there\", \"idontexist\": None},\n {\"_id\": 2, \"hello\": \"here\", \"idontexist\": None},\n {\"_id\": 3, \"hello\": 2, \"idontexist\": None},\n ]","def _schema_type(self) -> Optional[type]:\n return IndexSchema","def create(excludeDecls=False):\r\n return Index(conf.lib.clang_createIndex(excludeDecls, 0))","def create_index(self):\r\n\r\n #### Begin functionality here\r\n if self.session is not None:\r\n self.disconnect()\r\n self.create_database()\r\n return(True)","def test_corpus_is_not_present():\n with pytest.raises(ValueError, match=\".*not contain a text corpus.*\"):\n TextCleaner().transform(X_bin)","def _SetupIndexes(self, _open=open):\n pass","def ParseIndex(self, text):\n taxonStart = 0\n taxonStop = len(self.alignment) - 1\n columnStart = 0\n columnStop = self.alignment.get_alignment_length() - 1\n if (',' not in text):\n self.AlertMessage('Invalid index format. (taxa or columns missing)', 'high')\n return (-1,-1,-1,-1)\n else:\n text = text.strip()\n indices = text.split(',')\n if (len(indices) > 2):\n self.AlertMessage('Invalid index format. (too many fields)', 'high')\n return (-1,-1,-1,-1)\n else:\n if (':' in indices[0]): #there is a range specified in the taxon index\n taxonIndices = indices[0].split(':')\n if (taxonIndices[0]): #a start taxon is specified\n try:\n taxonStart = int(taxonIndices[0].strip())\n except:\n self.AlertMessage('Invalid index format. (taxon start index not an integer)', 'high')\n return (-1, -1, -1, -1)\n if (taxonIndices[1]): #a stop taxon is specified\n try:\n taxonStop = int(taxonIndices[1].strip())\n except:\n self.AlertMessage('Invalid index format. (taxon stop index not an integer)', 'high')\n return (-1, -1, -1, -1)\n elif (indices[0]): #a single taxon is specified\n try:\n taxonStart = int(indices[0].strip())\n taxonStop = int(indices[0].strip())\n except:\n self.AlertMessage('Invalid index format. (taxon start or stop index not an integer)', 'high')\n return (-1, -1, -1, -1)\n if (':' in indices[1]): #there is a range specified in the taxon index\n columnIndices = indices[1].split(':')\n if (columnIndices[0]): #a start taxon is specified\n try:\n columnStart = int(columnIndices[0].strip())\n except:\n self.AlertMessage('Invalid index format. (column start index not an integer)', 'high')\n return (-1, -1, -1, -1)\n if (columnIndices[1]): #a stop taxon is specified\n try:\n columnStop = int(columnIndices[1].strip())\n except:\n self.AlertMessage('Invalid index format. (column stop index not an integer)', 'high')\n return (-1, -1, -1, -1)\n elif (indices[1]): #a single taxon is specified\n try:\n columnStart = int(indices[1].strip())\n columnStop = int(indices[1].strip())\n except:\n self.AlertMessage('Invalid index format. (column start or stop index not an integer)', 'high')\n return (-1, -1, -1, -1)\n if ((0 <= taxonStart <= taxonStop) & (0 <= columnStart <= columnStop)):\n return (taxonStart, taxonStop, columnStart, columnStop)\n else:\n self.AlertMessage('Invalid index range. (start > stop or index < 0)', 'high')\n return (-1,-1,-1,-1)","def save(self, *args, **kwargs):\n # create the index string\n message = str()\n data = kwargs.get(\"data\")\n ts = kwargs.get(\"ts\")\n # prepare the doc for indexing\n doc = dict()\n doc['_type'] = kwargs.get(\"type\")\n doc['info'] = data\n doc['@timestamp'] = datetime.datetime(*ts[:6])\n try:\n exists = self.es.indices.exists(kwargs.get(\"index\"))\n if not exists:\n map_st, map_msg = self.build_mapping(**kwargs)\n if not map_st:\n return map_st, map_msg\n res = self.es.index(\n index=kwargs.get(\"index\"),\n doc_type=doc['_type'],\n body=doc, # message\n timestamp=datetime.datetime.utcnow(), # set to current time\n consistency='one', # do not wait for quorum / all shards\n replication='async', # async\n ttl=ELASTICSEARCH_TTL) # as defined in settings\n return True, res\n except TransportError, e:\n # fail silently - just log and die ...\n message = 'Error in indexing, host: {}, unable to index'.format(\n ELASTICSEARCH_CONN)\n if ADD_LOG_FAILURES:\n LOGGER.exception(e)\n return False, message","def test_get_indices_null_or_empty_search_list(self):\r\n search_list = None\r\n self.assertRaises(ValueError, _get_indices, search_list, 'item')\r\n\r\n search_list = []\r\n self.assertEqual(_get_indices(search_list, 'item'), [])\r\n\r\n search_list = ''\r\n self.assertEqual(_get_indices(search_list, 'item'), [])","def test_negative_index(self):\n # TODO: change Exception\n with self.assertRaises(Exception):\n self.test_table.change_header(Path=-1)","def test_read_index(self):\n\n indexfile = tempfile.mktemp()\n self.addCleanup(os.unlink, indexfile)\n\n TroveIndexBuilder(\"test/short.dat\", out=indexfile)\n\n index = TroveIndex()\n index.reload(indexfile)\n\n docs = sorted([doc for doc in index.documents])\n self.assertEquals(10, len(docs))\n\n self.assertEquals([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], docs)\n\n doc = index.get_document(1)\n ref = {u\"id\":\"1\",u\"titleName\":u\"Hello\"}\n self.assertNotEquals(None, doc, \"Document not found for id 1\")\n self.assertDictEqual(ref, doc)\n\n doc = index.get_document(10)\n ref = {\"id\":\"10\",\"titleName\":\"Hello\"}\n self.assertNotEquals(None, doc)\n self.assertDictEqual(ref, doc)","def init(self):\n self._es.create_index_template(\n name=DATASETS_INDEX_NAME,\n template=DATASETS_INDEX_TEMPLATE,\n force_recreate=True,\n )\n self._es.create_index(DATASETS_INDEX_NAME)","def create_index(self, *columns):\n self._assert_columns_exist(columns)\n\n # Build index name.\n whitelist = lambda col: ''.join(x for x in col if x.isalnum())\n idx_name = '_'.join(whitelist(col) for col in columns)\n idx_name = 'idx_{0}_{1}'.format(self._table, idx_name)\n\n # Build column names.\n col_names = [self._normalize_column(x) for x in columns]\n col_names = ', '.join(col_names)\n\n # Prepare statement.\n statement = 'CREATE INDEX IF NOT EXISTS {0} ON {1} ({2})'\n statement = statement.format(idx_name, self._table, col_names)\n\n # Create index.\n cursor = self._connection.cursor()\n cursor.execute('PRAGMA synchronous=OFF')\n cursor.execute(statement)","def set_index(self, idx, rel, attrs):\n\n query = 'CREATE INDEX {} ON {} ({})'.format(idx, rel, ','.join(attrs))\n\n with self.tpch_cxn.cursor() as curs:\n try:\n curs.execute(query)\n except pg.ProgrammingError as e:\n print(e)","def init_index(self, index_name):\n return Index(self, index_name)","def index_writer_init(idx_dir=\"Wiki_index\"):\n try:\n assert type(idx_dir) is str\n except AssertionError:\n raise TypeError\n\n try:\n assert idx_dir != \"\"\n except AssertionError:\n raise ValueError\n\n # Creazione dello schema dei documenti da indicizzare\n schema: Schema = Schema(title=TEXT(stored=True),\n identifier=ID(stored=True, unique=True),\n content=TEXT(stored=True, analyzer=StemmingAnalyzer()))\n\n # Verifica dell'esistenza della cartella dell'indice\n if not path.exists(idx_dir):\n # In caso la cartella non esista viene creata\n mkdir(idx_dir)\n\n # Creazione dell'indice all'interno della cartella designata\n index = create_in(idx_dir, schema)\n\n # La funzione restituisce un oggetto in grado di inserire (scrivere) documenti all'interno dell'indice\n return index.writer()","def create_index(self, *columns):\n self._assert_columns_exist(columns)\n\n # Build index name.\n whitelist = lambda col: ''.join(x for x in col if x.isalnum())\n idx_name = '_'.join(whitelist(col) for col in columns)\n idx_name = 'idx_{0}_{1}'.format(self._table, idx_name)\n\n # Build column names.\n col_names = [self._normalize_column(x) for x in columns]\n col_names = ', '.join(col_names)\n\n # Prepare statement.\n statement = 'CREATE INDEX IF NOT EXISTS {0} ON {1} ({2})'\n statement = statement.format(idx_name, self._table, col_names)\n\n # Create index.\n cursor = self._connection.cursor()\n cursor.execute(statement)"],"string":"[\n \"def test_create_text_index_w_schema(self):\\n actual = self.engine._create_text_index(self.engine.schema)\\n expected = 'TextIndex'\\n self.assertEqual(actual, expected)\",\n \"def test_creating_index_type(self):\",\n \"def test_string_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_string_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_simple_index, \\\"foobar\\\")\\n\\t)\",\n \"def create_index():\",\n \"def test_get_document_inexistent(empty_index):\\n with pytest.raises(Exception):\\n empty_index().get_document(\\\"123\\\")\",\n \"def create_index(schema, index_name):\\n if not os.path.exists(index_name):\\n os.mkdir(index_name)\\n ix = index.create_in(index_name, schema)\\n print(f\\\"index {index_name} created successfully\\\")\\n return ix\",\n \"def test_add_documents_empty(empty_index):\\n index = empty_index()\\n with pytest.raises(Exception) as e_info:\\n index.add_documents(\\\"\\\")\\n assert e_info.value.code == \\\"missing_payload\\\"\\n assert e_info.value.type == \\\"invalid_request\\\"\",\n \"def test_create_index_fail_test(tcex: TcEx, monkeypatch: MonkeyPatch):\\n\\n # monkeypatch method\\n def mp_post(*args, **kwargs): # pylint: disable=unused-argument\\n return MockPost({}, ok=False)\\n\\n monkeypatch.setattr(tcex.session.tc, 'post', mp_post)\\n\\n # create index\\n key = str(uuid.uuid4())\\n try:\\n tcex.api.tc.v2.datastore('local', key)\\n assert False, 'Failed to catch error on ok=False'\\n except RuntimeError:\\n assert True\",\n \"def test_create_index_is_called(self):\\r\\n assert len(self.index_calls) == 0\\r\\n\\r\\n connection._index_all_fields = False\\r\\n \\r\\n class TestIndexCreationCallTestVertex(Vertex):\\r\\n col1 = properties.Text(index=True)\\r\\n col2 = properties.Text(index=True, db_field='____column')\\r\\n col3 = properties.Text(db_field='____column3')\\r\\n\\r\\n assert len(self.index_calls) == 2\\r\\n assert 'vid' not in self.index_calls\\r\\n assert 'col1' in self.index_calls\\r\\n assert '____column' in self.index_calls\\r\\n assert '____column3' not in self.index_calls\\r\\n\\r\\n connection._index_all_fields = True\\r\\n self.index_calls = []\\r\\n\\r\\n class TestIndexCreationCallTestVertex2(Vertex):\\r\\n col1 = properties.Text()\\r\\n col2 = properties.Text(db_field='____column')\\r\\n\\r\\n assert len(self.index_calls) == 3\\r\\n assert 'vid' in self.index_calls\\r\\n assert 'col1' in self.index_calls\\r\\n assert '____column' in self.index_calls\",\n \"def test_works_index_name(self):\\n assert \\\"test_index-v4\\\" == self.search.works_index_name(self._db)\",\n \"def test_invalid_text_search(aquarius_instance):\\n text = \\\"foo_text\\\"\\n with pytest.raises(ValueError):\\n aquarius_instance.text_search(text=text, sort=\\\"foo_sort\\\")\",\n \"def idx_createFieldIndex(plominoIndex, fieldname, fieldtype='TEXT', **args):\\n \\n if not hasattr(plominoIndex, 'createFieldIndex'):\\n plominoIndex = plominoIndex.getParentDatabase().getIndex()\\n \\n if not fieldname in plominoIndex.indexes():\\n plominoIndex.createFieldIndex(fieldname, fieldtype, **args)\",\n \"def ensure_index(self, schema, language, mapping, return_index='external'):\\n assert schema and language and mapping\\n assert len(language) == 2\\n assert return_index == 'external' or return_index == 'internal'\\n\\n external = self.get_external_index_name(schema, language, mapping.name)\\n internal = self.get_internal_index_name(\\n schema, language, mapping.name, mapping.version)\\n\\n return_value = return_index == 'external' and external or internal\\n\\n if internal in self.created_indices:\\n return return_value\\n\\n if self.es_client.indices.exists(index=internal):\\n self.created_indices.add(internal)\\n return return_value\\n\\n # create the index\\n self.es_client.indices.create(\\n index=internal,\\n mappings={\\n 'properties': mapping.for_language(language)\\n },\\n settings={\\n 'analysis': ANALYSIS_CONFIG,\\n 'index': {\\n 'number_of_shards': 1,\\n 'number_of_replicas': 0,\\n 'refresh_interval': '5s'\\n }\\n }\\n )\\n\\n # point the alias to the new index\\n self.es_client.indices.put_alias(name=external, index=internal)\\n\\n # cache the result\\n self.created_indices.add(internal)\\n\\n return return_value\",\n \"def create_index(self):\\r\\n\\r\\n #### Begin functionality here\\r\\n\\r\\n return()\",\n \"def create_index(self, table_name, index, timeout):\\n _abstract()\",\n \"def create_index(self, table_name, index, timeout):\\n _abstract()\",\n \"def createIndex(schemaName, tableName, index):\\n return \\\"\\\"\\\"CREATE {unique} INDEX {name} ON \\\\\\\"{schema_name}\\\\\\\".\\\\\\\"{tableName}\\\\\\\" ({fields})\\\"\\\"\\\".format(\\n unique = \\\"UNIQUE\\\" if index.uniqueness else \\\"\\\",\\n schema_name = schemaName,\\n name = \\\"\\\\\\\"\\\" + tableName + \\\"__\\\" + index.name + \\\"\\\\\\\"\\\" if index.name is not None else \\\"\\\",\\n tableName = tableName,\\n fields = \\\", \\\".join(map(lambda index: '\\\"' + str(index.name) + '\\\"', index.fields))\\n )\",\n \"def test_string_list_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_string_list_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_list_index, \\\"foobar\\\")\\n\\t)\",\n \"def test_create_bad_sample_names(self):\\n # set a horrible list of sample names\\n self.metadata.index = ['o()xxxx[{::::::::>', 'sample.1', 'sample.3']\\n with self.assertRaises(QiitaDBColumnError):\\n SampleTemplate.create(self.metadata, self.new_study)\",\n \"def create_index(self):\\n self.send_robust(self.es_index, data=self.es_meta)\\n self.set_index_normal_settings()\",\n \"def test_create_index(self, collection):\\n collection.create_index(\\\"hello\\\")\\n assert collection._indexes == {\\\"_id_\\\": ((\\\"_id\\\",), {(1,)})}\\n\\n collection.create_index(\\\"hello\\\", unique=True)\\n assert collection._indexes == {\\n \\\"_id_\\\": ((\\\"_id\\\",), {(1,)}),\\n \\\"hello_1\\\": ((\\\"hello\\\",), {(\\\"there\\\",)}),\\n }\",\n \"def create_schema():\\n schema = Schema(idx=ID(stored=True),\\n data=STORED,\\n body=TEXT(analyzer=StemmingAnalyzer()),\\n )\\n print(\\\"schema creation successful\\\")\\n return schema\",\n \"def schema_index(schema, version):\\n if schema.abstract:\\n raise InvalidData(\\\"Cannot index abstract schema: %s\\\" % schema)\\n name = \\\"entity-%s\\\" % schema.name.lower()\\n return index_name(name, version=version)\",\n \"def test_create_index_swift(self):\\n\\n indexfile = tempfile.mktemp()\\n self.addCleanup(os.unlink, indexfile)\\n\\n index = TroveSwiftIndexBuilder(\\\"short.dat\\\", out=indexfile)\\n\\n # read the index file that was created\\n with open(indexfile, 'r+b') as fd:\\n indextext = fd.read()\\n indexlines = indextext.split('\\\\n')\\n\\n # 11 lines includes on blank line at the end\\n self.assertEquals(11, len(indexlines))\\n del indexlines[10]\\n\\n # check the first character of each line\\n docs = [line[0] for line in indexlines]\\n self.assertEquals(['1', '2', '3', '4', '5', '6', '7', '8', '9', '1'], docs)\\n\\n # check some lines from the index\\n ref = \\\"1, 0, 31, short.dat\\\"\\n self.assertEqual(ref, indexlines[0])\\n ref = \\\"10, 279, 32, short.dat\\\"\\n self.assertEqual(ref, indexlines[9])\",\n \"def test_create_index(self):\\n\\n class Comment(Document):\\n message = StringField()\\n meta = {\\\"allow_inheritance\\\": True}\\n\\n Comment.create_index(\\\"message\\\")\\n\\n info = Comment.objects._collection.index_information()\\n info = [\\n (value[\\\"key\\\"], value.get(\\\"unique\\\", False), value.get(\\\"sparse\\\", False))\\n for key, value in info.items()\\n ]\\n assert ([(\\\"_cls\\\", 1), (\\\"message\\\", 1)], False, False) in info\",\n \"def create_new_index(self, dict_pg_info):\\n # ! Setting if fun can use default setting\\n ruler = Rules()\\n str_conn = ruler.pg_info_rules(dict_pg_info)\\n conn = psycopg2.connect(str_conn)\\n\\n with conn:\\n with conn.cursor() as cur:\\n str_create_table = \\\"CREATE TABLE \\\" + dict_pg_info['table'] + \\\" (path varchar PRIMARY KEY);\\\"\\n # ! Check if table already exit\\n cur.execute(str_create_table)\\n cur.close()\\n\\n conn.close()\",\n \"def create_indices():\\n destroy_indices()\\n\\n ActionDocument._index.create(ignore=[400, 404])\\n ClassificationDocument._index.create(ignore=[400, 404])\\n FunctionDocument._index.create(ignore=[400, 404])\\n PhaseDocument._index.create(ignore=[400, 404])\\n RecordDocument._index.create(ignore=[400, 404])\\n\\n yield\\n\\n destroy_indices()\",\n \"def test_get_algorithm_NotFound(self):\\n searched_id = 'xyz1'\\n right_list = []\\n create_test_algorithm_list(right_list, 101)\\n documents = []\\n create_test_documents_list(right_list, documents, 101)\\n index = search.Index(name=search_algorithm._INDEX_STRING)\\n index.put(documents)\\n # end of preparing data\\n result = index.get_range(ids_only=True)\\n self.assertLess(0, len(result.results), msg='The database is empty')\\n algorithm = search_algorithm.get_algorithm(index, searched_id)\\n self.assertEqual(1, algorithm, msg='Nonexistent Algorithm was found in 101 algorithms database')\",\n \"def test_search_table_fail_if_no_page_index(self) -> None:\\n with local_app.test_client() as test:\\n response = test.get('/api/search/v0/table', query_string=dict(query='test'))\\n self.assertEqual(response.status_code, HTTPStatus.INTERNAL_SERVER_ERROR)\",\n \"def validate(self):\\n\\n if self.indexer_table is None:\\n raise Exception('Indexed words table is not set')\",\n \"def test_text(self):\\n conn = self.database.connection()\\n cursor = conn.cursor()\\n dialect = self.database.dialect()\\n dbapi = self.database.dbapi()\\n query = dialect.translate('DROP TABLE test_text')\\n try:\\n cursor.execute(query)\\n except dbapi.Error:\\n conn.rollback()\\n query = dialect.translate('CREATE TABLE test_text ' \\\\\\n '( value TEXT NOT NULL )')\\n cursor.execute(query)\\n data = []\\n query = 'INSERT INTO test_text VALUES (%s)'\\n for i in range(10):\\n item = random_string(100000)\\n data.append(item)\\n cursor.execute(query, (item,))\\n query = 'SELECT * FROM test_text'\\n cursor.execute(query)\\n result = cursor.fetchall()\\n for row in result:\\n item = row[0]\\n assert isinstance(item, unicode)\\n assert item in data\\n data.remove(item)\\n query = dialect.translate('DELETE FROM test_text')\\n cursor.execute(query)\\n query = dialect.translate('DROP TABLE test_text')\\n cursor.execute(query)\\n conn.commit()\",\n \"def test_creation(self):\\n config = config_factory()\\n db = config.arango_db\\n\\n # Create analyzer\\n analyzer = ArangoAnalyzer(\\\"analyzer_sample\\\")\\n analyzer.set_stopwords(\\n language=\\\"english\\\",\\n custom_stopwords=[\\\"stop\\\", \\\"word\\\"],\\n include_default=False,\\n )\\n analyzer.type = ArangoAnalyzer._TYPE_TEXT\\n\\n analyzer.create(db)\\n assert analyzer.__dict__ == {\\n \\\"name\\\": \\\"analyzer_sample\\\",\\n \\\"type\\\": \\\"text\\\",\\n \\\"features\\\": [\\\"frequency\\\", \\\"norm\\\", \\\"position\\\"],\\n \\\"locale\\\": \\\"en\\\",\\n \\\"case\\\": \\\"lower\\\",\\n \\\"stopwords\\\": [\\\"stop\\\", \\\"word\\\"],\\n \\\"accent\\\": False,\\n \\\"stemming\\\": True,\\n \\\"edge_ngram\\\": None,\\n \\\"delimiter\\\": \\\",\\\",\\n \\\"min\\\": 2,\\n \\\"max\\\": 5,\\n \\\"preserve_original\\\": False,\\n \\\"start_marker\\\": \\\"\\\",\\n \\\"end_marker\\\": \\\"\\\",\\n \\\"stem_type\\\": \\\"binary\\\",\\n }\\n assert isinstance(db.analyzer(\\\"analyzer_sample\\\"), dict)\",\n \"def __init__(self, index_name, doc_type='string'):\\n super(ESMigration, self).__init__()\\n self.index_name = index_name\\n self.doc_type = doc_type\",\n \"def create_index(self):\\n if self.index_exists():\\n logger.info('Index {} already exists'.format(self.index_name))\\n logger.info('Deleting existing index')\\n self.indices_client.delete(index=self.index_name)\\n self.create_index_if_not_exist()\",\n \"def get_index_text(self, crate, module, impl, name):\\n raise NotImplementedError\",\n \"def __call__(self, index, checkFirst=False):\\n schema_name = index.schema().dbname()\\n index_name = index.dbname()\\n cmd = 'CREATE' if not index.testFlag(index.Flags.Unique) else 'CREATE UNIQUE'\\n\\n cols = ['`{0}`'.format(col.field()) if col.testFlag(col.Flags.CaseSensitive)\\n else '`{0}` COLLATE NOCASE'.format(col.field())\\n for col in index.columns()]\\n\\n cmd = '{0} INDEX `{1}` ON `{2}` ({3})'.format(cmd, index_name, schema_name, ', '.join(cols))\\n\\n if checkFirst:\\n cmd = \\\"\\\"\\\"\\\\\\n DO $$\\n BEGIN\\n IF NOT EXISTS (\\n SELECT 1\\n FROM pg_indexes\\n WHERE schemaname = 'public'\\n AND indexname = '{0}'\\n ) THEN {1};\\n END IF;\\n END$$;\\n \\\"\\\"\\\".format(index_name, cmd)\\n else:\\n cmd += ';'\\n\\n return cmd, {}\",\n \"def build_index():\\n pass\",\n \"def test_search_table_fail_if_no_query(self) -> None:\\n with local_app.test_client() as test:\\n response = test.get('/api/search/v0/table', query_string=dict(page_index='0'))\\n self.assertEqual(response.status_code, HTTPStatus.INTERNAL_SERVER_ERROR)\",\n \"def create_index(es_object, index_name):\\n created = False\\n \\\"\\\"\\\" index settings \\\"\\\"\\\"\\n settings = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 0\\n },\\n \\\"mappings\\\": {\\n \\\"physicians\\\": {\\n \\\"dynamic\\\": \\\"strict\\\",\\n \\\"properties\\\": {\\n \\\"overview\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"full_name\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"years_of_practice\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"language\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"office_location\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"hospital_affiliation\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"specialties\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"education_and_medical_training\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n \\\"certification_and_licensure\\\": {\\n \\\"type\\\": \\\"text\\\"\\n },\\n }\\n }\\n }\\n }\\n\\n try:\\n if not es_object.indices.exists(index_name):\\n # Ignore 400 means to ignore \\\"Index Already Exist\\\" error.\\n es_object.indices.create(index=index_name, ignore=400, body=settings)\\n print('Created Index')\\n created = True\\n except Exception as ex:\\n print(str(ex))\\n finally:\\n return created\",\n \"def _create_indices(cls):\\r\\n from thunderdome.connection import _hosts, _index_all_fields, create_key_index\\r\\n \\r\\n if not _hosts: return\\r\\n for column in cls._columns.values():\\r\\n if column.index or _index_all_fields:\\r\\n create_key_index(column.db_field_name)\",\n \"def __init__(self, create_index=True, online=True):\\n self.online = online\\n index_exists = self.index_exists()\\n if create_index and not index_exists:\\n self.create_index()\",\n \"def test_local_index_no_range_key(engine):\\n with pytest.raises(ValueError):\\n class Model(engine.model):\\n id = Column(UUID, hash_key=True)\\n another = Column(UUID)\\n by_another = LocalSecondaryIndex(range_key=\\\"another\\\")\",\n \"def test_word_not_found_in_file(self):\\n\\n # create indexer object\\n indexer = indexing_module.IndexModule()\\n\\n # index the location (storage/data/test/word_not_found)\\n indexer.index(_path_prefix + 'word_not_found')\\n\\n # search for few words and check that the result is empty\\n result = indexer.search(\\\"hello\\\")\\n print(result)\\n self.assertTrue(result == {})\\n\\n result = indexer.search(\\\"world\\\")\\n self.assertTrue(result == {})\",\n \"def create_index(self):\\n\\n indice = client.IndicesClient(self.es)\\n\\n if not indice.exists(self.es_main_index):\\n indice.create(\\n index=self.es_main_index\\n )\\n\\n return True\",\n \"def get_sql_create_index(tablename, schema, if_not_exists=False):\\n if not schema.get(\\\"columns\\\"):\\n raise ValueError(\\\"no columns defined\\\")\\n if len(schema[\\\"columns\\\"]) != len(set(schema[\\\"columns\\\"])):\\n raise ValueError(\\\"same column given more than once\\\")\\n sql = [\\\"CREATE\\\"]\\n if schema.get(\\\"unique\\\"):\\n sql.append(\\\"UNIQUE\\\")\\n sql.append(\\\"INDEX\\\")\\n if if_not_exists:\\n sql.append(\\\"IF NOT EXISTS\\\")\\n sql.append('\\\"%s\\\" ON \\\"%s\\\"' % (schema[\\\"name\\\"], tablename))\\n sql.append(\\\"(%s)\\\" % \\\",\\\".join([f'\\\"{c}\\\"' for c in schema[\\\"columns\\\"]]))\\n return \\\" \\\".join(sql)\",\n \"def checkCatalogIndexes(self, trans):\\n for lang in trans.keys():\\n index_id = 'objecttrans_%s' % lang\\n if not index_id in self.indexes():\\n self.__createObjecttransIndex(index_id)\\n index_id = 'objectname_%s' % lang\\n if not index_id in self.indexes():\\n self.__createObjectnameIndex(index_id)\",\n \"def setUp(self):\\n body = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 0\\n }\\n }\\n self.assertTrue(self.es.create_index('contacts_esclient_test', body))\\n self.assertFalse(self.es.create_index('contacts_esclient_test', body))\\n\\n self.assertTrue(self.es.create_index('contacts_esclient_test2', body))\\n self.assertFalse(self.es.create_index('contacts_esclient_test2', body))\\n\\n\\n \\\"\\\"\\\" Index some test data \\\"\\\"\\\"\\n data = {\\\"name\\\": \\\"Joe Tester\\\",\\\"age\\\": 21, \\\"sex\\\": \\\"male\\\"}\\n self.assertTrue(self.es.index(\\\"contacts_esclient_test\\\", \\\"person\\\", body=data,\\n docid=1))\\n data = {\\\"name\\\": \\\"Joe Schmoe\\\",\\\"age\\\": 17, \\\"sex\\\": \\\"male\\\"}\\n self.assertTrue(self.es.index(\\\"contacts_esclient_test\\\", \\\"person\\\", body=data,\\n docid=2))\\n\\n self.assertTrue(self.es.refresh('contacts_esclient_test'))\",\n \"def create(\\n self,\\n index: IO,\\n request_options: Optional[_models.RequestOptions] = None,\\n *,\\n content_type: str = \\\"application/json\\\",\\n **kwargs: Any\\n ) -> _models.SearchIndex:\",\n \"def test_get_algorithm_Empty(self):\\n searched_id = 'xyz1'\\n index = search.Index(name=search_algorithm._INDEX_STRING)\\n result = index.get_range(ids_only=True)\\n self.assertEqual(0, len(result.results), msg='The database is not empty')\\n algorithm = search_algorithm.get_algorithm(index, searched_id)\\n self.assertEqual(1, algorithm, msg='Non existent Algorithm was found in empty database')\",\n \"def get_index_text(self, name):\\n raise NotImplementedError('must be implemented in subclasses')\",\n \"def test_stochatreat_input_idx_col_str(correct_params):\\n idx_col_not_str = 0\\n with pytest.raises(TypeError):\\n stochatreat(\\n data=correct_params[\\\"data\\\"],\\n block_cols=[\\\"block\\\"],\\n treats=correct_params[\\\"treat\\\"],\\n idx_col=idx_col_not_str,\\n probs=correct_params[\\\"probs\\\"],\\n )\",\n \"def create_index(cls, engine):\\n\\n reg_imei = db.Index('reg_imei_index', cls.imei, postgresql_concurrently=True)\\n reg_imei.create(bind=engine)\\n\\n reg_normalized_imei = db.Index('reg_normalized_imei_index', cls.normalized_imei, postgresql_concurrently=True)\\n reg_normalized_imei.create(bind=engine)\",\n \"def create_index(self, db_name):\\n\\t\\tindex_func_path = self._get_index_func_filepath(db_name)\\n\\t\\t\\n\\t\\tif os.path.isfile(index_func_path):\\n\\t\\t\\t# create index request payload from predefined file\\t\\n\\t\\t\\twith open(index_func_path, 'r') as content_file:\\n\\t\\t\\t\\tpayload = content_file.read()\\n\\t\\t\\n\\t\\t\\tprint (\\\"Create index using function in: {}\\\".format(index_func_path))\\n\\t\\t\\turl = \\\"https://{}/{}/_design/view\\\".format(\\n\\t\\t\\t\\tself.cloudanthost, db_name)\\n\\t\\t\\tresponse = self.r.put(url, data=payload)\\n\\t\\t\\tassert response.status_code == 201\",\n \"def __init__(self):\\n super().__init__()\\n self.index_dir = self.base_dir + \\\"user/\\\"\\n self.index_schema = self.__get_index_schema()\\n if not os.path.exists(self.index_dir):\\n os.makedirs(self.index_dir)\\n self.indexer = index.create_in(self.index_dir, self.index_schema) # creates the index\\n else:\\n self.indexer = index.open_dir(self.index_dir) # opens the index if it already exists\",\n \"def create_index(self, *columns):\\n # Calling super() with older convention to support Python 2.7 & 2.6.\\n super(SqliteSource, self).create_index(*columns)\",\n \"def create_index(self, *columns):\\n # Calling super() with older convention to support Python 2.7 & 2.6.\\n super(SqliteSource, self).create_index(*columns)\",\n \"def _apply_index_op(db, op):\\n if 'createIndexes' not in op['o']:\\n return\\n o = op['o']\\n coll_name = o['createIndexes']\\n key = list(o['key'].items())\\n name = o['name']\\n return db[coll_name].create_index(key, name=name)\",\n \"def test_export_index(self):\",\n \"def test_raise_exception_bad_indices(self):\\n print(\\\"Testing exception is raised if indices are bad\\\")\\n\\n with self.assertRaises(Exception) as no_index:\\n get_region_data(self.wmo_boxes, self.float_name, self.config,\\n [], self.pres)\\n\\n self.assertTrue('NO DATA FOUND' in str(no_index.exception))\\n\\n with self.assertRaises(Exception) as big_index:\\n get_region_data(self.wmo_boxes, self.float_name, self.config,\\n [99999999999999999], self.pres)\\n\\n self.assertTrue('NO DATA FOUND' in str(big_index.exception))\",\n \"def validate_index(self):\\n schema_path = pkg_resources.resource_filename(\\n \\\"FLIR.conservator\\\", \\\"index_schema.json\\\"\\n )\\n with open(schema_path) as o:\\n schema = json.load(o)\\n\\n try:\\n with open(self.index_path) as index:\\n index_data = json.load(index)\\n jsonschema.validate(index_data, schema)\\n return True\\n except jsonschema.exceptions.ValidationError as e:\\n logger.error(e.message)\\n logger.debug(e)\\n return False\",\n \"def define_index_field(DomainName=None, IndexField=None):\\n pass\",\n \"def __init__(self):\\n super().__init__()\\n self.index_dir = self.base_dir + \\\"ticket/\\\"\\n self.index_schema = self.__get_index_schema()\\n if not os.path.exists(self.index_dir):\\n os.makedirs(self.index_dir)\\n self.indexer = index.create_in(self.index_dir, self.index_schema)\\n else:\\n self.indexer = index.open_dir(self.index_dir)\",\n \"def ensure_index(cls, *a, **ka):\\n cls._dbag.ensure_index(*a, **ka)\",\n \"def init_index(self):\\n raise NotImplementedError\",\n \"def test_should_return_correct_gremlin_for_key_index_creation(self):\\r\\n expected = 'g.createKeyIndex(\\\"email\\\", Vertex.class)'\\r\\n key_index = self.spec_parser.parse_key_index(self.key_index_spec)\\r\\n assert key_index.gremlin == expected\",\n \"def ensure_indexes(self):\\n self.db_connection.ensure_indexes()\",\n \"def test_AlgorithmsIdHandler_GET_NotFound(self):\\n searchedId='xyz1'\\n right_list = []\\n create_test_algorithm_list(right_list, 101)\\n documents = []\\n create_test_documents_list(right_list, documents, 101)\\n index = search.Index(name=search_algorithm._INDEX_STRING)\\n index.put(documents)\\n # end of preparing data\\n result = index.get_range(ids_only=True)\\n self.assertLess(0, len(result.results), msg='The database is empty')\\n response = self.testapp.get('/algorithms/' + searchedId, expect_errors=True)\\n self.assertEqual(404, response.status_int, msg='Non existent Algorithm was found in empty database')\\n self.assertEqual('application/json', response.content_type)\\n self.assertIn('Algorithm Not Found', response.normal_body.decode(encoding='UTF-8'))\",\n \"def test_missing_foreign_key_indices(db_session):\\n\\n from sqlalchemy_utils.functions import non_indexed_foreign_keys\\n from kotti import metadata\\n\\n assert non_indexed_foreign_keys(metadata) == {}\",\n \"def create_schema(self, schema: str):\\n return\",\n \"def test_geo_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_geo_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_geo_index, (0.0, 0.0))\\n\\t)\",\n \"def create_index(es_object, index_name):\\n created = False\\n # index settings\\n # the reason why we need mappings is avoid corrupting your data\\n settings = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 0\\n },\\n \\\"mappings\\\": {\\n # custom type called foods\\n \\\"foods\\\": {\\n \\\"properties\\\": {\\n # Specify that the food_name field contains text values.\\n \\\"food_name\\\": {\\n \\\"type\\\": \\\"text\\\",\\n \\\"fields\\\": {\\n \\\"raw\\\":{ \\n \\\"type\\\": \\\"keyword\\\"\\n } # The food_name.raw field can be used for sorting and aggregations\\n }\\n },\\n # Specify that the categories field contains text values.\\n \\\"categories\\\": {\\n \\\"type\\\": \\\"text\\\",\\n \\\"fields\\\": {\\n \\\"raw\\\":{ \\n \\\"type\\\": \\\"keyword\\\"\\n } # The categories.raw field can be used for sorting and aggregations\\n }\\n },\\n # Specify that the calories field contains integer values.\\n \\\"calories\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"protein\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"carbs\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"fat\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n }\\n \\n },\\n }\\n }\\n }\\n try:\\n if not es_object.indices.exists(index_name):\\n # Ignore 400 means to ignore \\\"Index Already Exist\\\" error.\\n es_object.indices.create(index=index_name, ignore=400, body=settings)\\n print('Created Index')\\n created = True\\n except Exception as ex:\\n print(str(ex))\\n finally:\\n return created\",\n \"def test_create_engine_no_adapters():\\n engine = create_engine(\\\"shillelagh://\\\")\\n\\n with pytest.raises(ProgrammingError) as excinfo:\\n Table(\\\"dummy://\\\", MetaData(bind=engine), autoload=True)\\n assert str(excinfo.value) == \\\"Unsupported table: dummy://\\\"\",\n \"def test_index(self):\",\n \"def test_secondary_index(self):\\n session = self.prepare()\\n\\n session.execute(\\\"CREATE TABLE t (id int PRIMARY KEY, v int, v2 text, v3 decimal)\\\")\\n session.execute((\\\"CREATE MATERIALIZED VIEW t_by_v AS SELECT * FROM t \\\"\\n \\\"WHERE v IS NOT NULL AND id IS NOT NULL PRIMARY KEY (v, id)\\\"))\\n assert_invalid(session, \\\"CREATE INDEX ON t_by_v (v2)\\\",\\n \\\"Secondary indexes are not supported on materialized views\\\")\",\n \"def test_integer_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_integer_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_simple_index, 12345)\\n\\t)\",\n \"def search_engine_index(request):\\n return {'NO_INDEX': settings.NO_INDEX}\",\n \"def test_table_name(self):\\n with self.assertRaises(IncompetentQiitaDeveloperError):\\n MetadataTemplate._table_name(self.study)\",\n \"def test_recreate_index_that_exists(self):\\n indices = self.elasticsearch_cls().indices\\n indices.exists.return_value = True\\n\\n index_name = 'abcd'\\n self.client._recreate_index(index_name)\\n indices.delete.assert_called_once_with(index_name)\\n indices.create.assert_called_once_with(index_name)\",\n \"def init_index(clear=False):\\n return _run_indexer_func(\\\"init_index\\\", clear)\",\n \"def ensure_indexes(self, cls):\\n return self.impl.ensure_indexes(cls)\",\n \"def __create_new_index(self, index, index_id, begin_timestamp):\\n index_name = index + \\\"_\\\" + str(index_id)\\n target_config = index + \\\".target_config\\\"\\n pipeline_id = index + \\\".pipeline\\\"\\n mapping = {\\n \\\"user_id\\\": {\\\"type\\\": \\\"long\\\"},\\n \\\"client_id\\\": {\\\"type\\\": \\\"long\\\"},\\n \\\"partner_id\\\": {\\\"type\\\": \\\"long\\\"},\\n \\\"module\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"page\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"uri\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"app_type\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"created_at\\\": {\\\"type\\\": \\\"date\\\"},\\n \\\"request_time\\\": {\\\"type\\\": \\\"date\\\"},\\n \\\"duration\\\": {\\\"type\\\": \\\"long\\\"},\\n }\\n body = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 1,\\n },\\n \\\"mappings\\\": {\\n \\\"docs\\\": {\\n \\\"properties\\\": mapping\\n }\\n },\\n }\\n self.client.indices.create(index=index_name, body=body)\\n self.client.index(\\n index=target_config,\\n doc_type=\\\"config\\\",\\n id=index_id,\\n body={\\\"index_id\\\": index_id, \\\"begin_timestamp\\\": begin_timestamp},\\n )\\n\\n return index_id\",\n \"def test_string_map_key_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_string_map_key_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_map_key_index, \\\"foobar\\\")\\n\\t)\",\n \"def test_existent_term_search(self):\\n results = self.searcher.search(\\\"coach\\\")\\n expected_results = 3\\n\\n self.assertEqual(results[0].indexable.docid, expected_results)\",\n \"def test_index_over_non_existing_field(self, collection):\\n collection.create_index(\\n [(\\\"hello\\\", Database.DESCENDING), (\\\"idontexist\\\", Database.ASCENDING)],\\n unique=True,\\n )\\n\\n collection.insert_many([{\\\"hello\\\": \\\"here\\\"}, {\\\"hello\\\": 2}])\\n assert collection._indexes == {\\n \\\"_id_\\\": ((\\\"_id\\\",), {(1,), (2,), (3,)}),\\n \\\"hello_1_idontexist_1\\\": (\\n (\\\"hello\\\", \\\"idontexist\\\"),\\n {(\\\"there\\\", None), (\\\"here\\\", None), (2, None)},\\n ),\\n }\\n assert collection.find({}, selection={\\\"hello\\\": 1, \\\"idontexist\\\": 1}) == [\\n {\\\"_id\\\": 1, \\\"hello\\\": \\\"there\\\", \\\"idontexist\\\": None},\\n {\\\"_id\\\": 2, \\\"hello\\\": \\\"here\\\", \\\"idontexist\\\": None},\\n {\\\"_id\\\": 3, \\\"hello\\\": 2, \\\"idontexist\\\": None},\\n ]\",\n \"def _schema_type(self) -> Optional[type]:\\n return IndexSchema\",\n \"def create(excludeDecls=False):\\r\\n return Index(conf.lib.clang_createIndex(excludeDecls, 0))\",\n \"def create_index(self):\\r\\n\\r\\n #### Begin functionality here\\r\\n if self.session is not None:\\r\\n self.disconnect()\\r\\n self.create_database()\\r\\n return(True)\",\n \"def test_corpus_is_not_present():\\n with pytest.raises(ValueError, match=\\\".*not contain a text corpus.*\\\"):\\n TextCleaner().transform(X_bin)\",\n \"def _SetupIndexes(self, _open=open):\\n pass\",\n \"def ParseIndex(self, text):\\n taxonStart = 0\\n taxonStop = len(self.alignment) - 1\\n columnStart = 0\\n columnStop = self.alignment.get_alignment_length() - 1\\n if (',' not in text):\\n self.AlertMessage('Invalid index format. (taxa or columns missing)', 'high')\\n return (-1,-1,-1,-1)\\n else:\\n text = text.strip()\\n indices = text.split(',')\\n if (len(indices) > 2):\\n self.AlertMessage('Invalid index format. (too many fields)', 'high')\\n return (-1,-1,-1,-1)\\n else:\\n if (':' in indices[0]): #there is a range specified in the taxon index\\n taxonIndices = indices[0].split(':')\\n if (taxonIndices[0]): #a start taxon is specified\\n try:\\n taxonStart = int(taxonIndices[0].strip())\\n except:\\n self.AlertMessage('Invalid index format. (taxon start index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n if (taxonIndices[1]): #a stop taxon is specified\\n try:\\n taxonStop = int(taxonIndices[1].strip())\\n except:\\n self.AlertMessage('Invalid index format. (taxon stop index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n elif (indices[0]): #a single taxon is specified\\n try:\\n taxonStart = int(indices[0].strip())\\n taxonStop = int(indices[0].strip())\\n except:\\n self.AlertMessage('Invalid index format. (taxon start or stop index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n if (':' in indices[1]): #there is a range specified in the taxon index\\n columnIndices = indices[1].split(':')\\n if (columnIndices[0]): #a start taxon is specified\\n try:\\n columnStart = int(columnIndices[0].strip())\\n except:\\n self.AlertMessage('Invalid index format. (column start index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n if (columnIndices[1]): #a stop taxon is specified\\n try:\\n columnStop = int(columnIndices[1].strip())\\n except:\\n self.AlertMessage('Invalid index format. (column stop index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n elif (indices[1]): #a single taxon is specified\\n try:\\n columnStart = int(indices[1].strip())\\n columnStop = int(indices[1].strip())\\n except:\\n self.AlertMessage('Invalid index format. (column start or stop index not an integer)', 'high')\\n return (-1, -1, -1, -1)\\n if ((0 <= taxonStart <= taxonStop) & (0 <= columnStart <= columnStop)):\\n return (taxonStart, taxonStop, columnStart, columnStop)\\n else:\\n self.AlertMessage('Invalid index range. (start > stop or index < 0)', 'high')\\n return (-1,-1,-1,-1)\",\n \"def save(self, *args, **kwargs):\\n # create the index string\\n message = str()\\n data = kwargs.get(\\\"data\\\")\\n ts = kwargs.get(\\\"ts\\\")\\n # prepare the doc for indexing\\n doc = dict()\\n doc['_type'] = kwargs.get(\\\"type\\\")\\n doc['info'] = data\\n doc['@timestamp'] = datetime.datetime(*ts[:6])\\n try:\\n exists = self.es.indices.exists(kwargs.get(\\\"index\\\"))\\n if not exists:\\n map_st, map_msg = self.build_mapping(**kwargs)\\n if not map_st:\\n return map_st, map_msg\\n res = self.es.index(\\n index=kwargs.get(\\\"index\\\"),\\n doc_type=doc['_type'],\\n body=doc, # message\\n timestamp=datetime.datetime.utcnow(), # set to current time\\n consistency='one', # do not wait for quorum / all shards\\n replication='async', # async\\n ttl=ELASTICSEARCH_TTL) # as defined in settings\\n return True, res\\n except TransportError, e:\\n # fail silently - just log and die ...\\n message = 'Error in indexing, host: {}, unable to index'.format(\\n ELASTICSEARCH_CONN)\\n if ADD_LOG_FAILURES:\\n LOGGER.exception(e)\\n return False, message\",\n \"def test_get_indices_null_or_empty_search_list(self):\\r\\n search_list = None\\r\\n self.assertRaises(ValueError, _get_indices, search_list, 'item')\\r\\n\\r\\n search_list = []\\r\\n self.assertEqual(_get_indices(search_list, 'item'), [])\\r\\n\\r\\n search_list = ''\\r\\n self.assertEqual(_get_indices(search_list, 'item'), [])\",\n \"def test_negative_index(self):\\n # TODO: change Exception\\n with self.assertRaises(Exception):\\n self.test_table.change_header(Path=-1)\",\n \"def test_read_index(self):\\n\\n indexfile = tempfile.mktemp()\\n self.addCleanup(os.unlink, indexfile)\\n\\n TroveIndexBuilder(\\\"test/short.dat\\\", out=indexfile)\\n\\n index = TroveIndex()\\n index.reload(indexfile)\\n\\n docs = sorted([doc for doc in index.documents])\\n self.assertEquals(10, len(docs))\\n\\n self.assertEquals([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], docs)\\n\\n doc = index.get_document(1)\\n ref = {u\\\"id\\\":\\\"1\\\",u\\\"titleName\\\":u\\\"Hello\\\"}\\n self.assertNotEquals(None, doc, \\\"Document not found for id 1\\\")\\n self.assertDictEqual(ref, doc)\\n\\n doc = index.get_document(10)\\n ref = {\\\"id\\\":\\\"10\\\",\\\"titleName\\\":\\\"Hello\\\"}\\n self.assertNotEquals(None, doc)\\n self.assertDictEqual(ref, doc)\",\n \"def init(self):\\n self._es.create_index_template(\\n name=DATASETS_INDEX_NAME,\\n template=DATASETS_INDEX_TEMPLATE,\\n force_recreate=True,\\n )\\n self._es.create_index(DATASETS_INDEX_NAME)\",\n \"def create_index(self, *columns):\\n self._assert_columns_exist(columns)\\n\\n # Build index name.\\n whitelist = lambda col: ''.join(x for x in col if x.isalnum())\\n idx_name = '_'.join(whitelist(col) for col in columns)\\n idx_name = 'idx_{0}_{1}'.format(self._table, idx_name)\\n\\n # Build column names.\\n col_names = [self._normalize_column(x) for x in columns]\\n col_names = ', '.join(col_names)\\n\\n # Prepare statement.\\n statement = 'CREATE INDEX IF NOT EXISTS {0} ON {1} ({2})'\\n statement = statement.format(idx_name, self._table, col_names)\\n\\n # Create index.\\n cursor = self._connection.cursor()\\n cursor.execute('PRAGMA synchronous=OFF')\\n cursor.execute(statement)\",\n \"def set_index(self, idx, rel, attrs):\\n\\n query = 'CREATE INDEX {} ON {} ({})'.format(idx, rel, ','.join(attrs))\\n\\n with self.tpch_cxn.cursor() as curs:\\n try:\\n curs.execute(query)\\n except pg.ProgrammingError as e:\\n print(e)\",\n \"def init_index(self, index_name):\\n return Index(self, index_name)\",\n \"def index_writer_init(idx_dir=\\\"Wiki_index\\\"):\\n try:\\n assert type(idx_dir) is str\\n except AssertionError:\\n raise TypeError\\n\\n try:\\n assert idx_dir != \\\"\\\"\\n except AssertionError:\\n raise ValueError\\n\\n # Creazione dello schema dei documenti da indicizzare\\n schema: Schema = Schema(title=TEXT(stored=True),\\n identifier=ID(stored=True, unique=True),\\n content=TEXT(stored=True, analyzer=StemmingAnalyzer()))\\n\\n # Verifica dell'esistenza della cartella dell'indice\\n if not path.exists(idx_dir):\\n # In caso la cartella non esista viene creata\\n mkdir(idx_dir)\\n\\n # Creazione dell'indice all'interno della cartella designata\\n index = create_in(idx_dir, schema)\\n\\n # La funzione restituisce un oggetto in grado di inserire (scrivere) documenti all'interno dell'indice\\n return index.writer()\",\n \"def create_index(self, *columns):\\n self._assert_columns_exist(columns)\\n\\n # Build index name.\\n whitelist = lambda col: ''.join(x for x in col if x.isalnum())\\n idx_name = '_'.join(whitelist(col) for col in columns)\\n idx_name = 'idx_{0}_{1}'.format(self._table, idx_name)\\n\\n # Build column names.\\n col_names = [self._normalize_column(x) for x in columns]\\n col_names = ', '.join(col_names)\\n\\n # Prepare statement.\\n statement = 'CREATE INDEX IF NOT EXISTS {0} ON {1} ({2})'\\n statement = statement.format(idx_name, self._table, col_names)\\n\\n # Create index.\\n cursor = self._connection.cursor()\\n cursor.execute(statement)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.85630506","0.6538482","0.6379478","0.62192476","0.6107749","0.60360074","0.6031513","0.59643614","0.5895175","0.5890334","0.58874357","0.57891434","0.56733567","0.5590555","0.55498284","0.55498284","0.55243","0.5514081","0.5476263","0.5456776","0.5440133","0.54239285","0.54204226","0.5398977","0.5362558","0.5357492","0.53446156","0.53399","0.53188425","0.5316743","0.5306752","0.53003365","0.52994305","0.52805585","0.5258978","0.5248693","0.5240499","0.52372605","0.52321255","0.52278715","0.522474","0.5224181","0.5219932","0.5210591","0.51919544","0.5191377","0.5189229","0.51803416","0.51796687","0.5174335","0.5170567","0.51703274","0.5166434","0.5165798","0.5154533","0.5154533","0.5132926","0.5130445","0.5129523","0.5116652","0.51141596","0.51094675","0.5104974","0.51027673","0.51016885","0.51010877","0.5099939","0.5089497","0.50865096","0.5078001","0.50744313","0.50635475","0.5045448","0.504529","0.50217324","0.50110435","0.5009058","0.50002354","0.49942875","0.4992305","0.4991577","0.4991238","0.49875164","0.49856395","0.4985328","0.4983066","0.49722615","0.49640176","0.4944367","0.49276116","0.4919689","0.4916198","0.4914819","0.49138898","0.49079907","0.49034205","0.49030703","0.4900367","0.4898219","0.489338"],"string":"[\n \"0.85630506\",\n \"0.6538482\",\n \"0.6379478\",\n \"0.62192476\",\n \"0.6107749\",\n \"0.60360074\",\n \"0.6031513\",\n \"0.59643614\",\n \"0.5895175\",\n \"0.5890334\",\n \"0.58874357\",\n \"0.57891434\",\n \"0.56733567\",\n \"0.5590555\",\n \"0.55498284\",\n \"0.55498284\",\n \"0.55243\",\n \"0.5514081\",\n \"0.5476263\",\n \"0.5456776\",\n \"0.5440133\",\n \"0.54239285\",\n \"0.54204226\",\n \"0.5398977\",\n \"0.5362558\",\n \"0.5357492\",\n \"0.53446156\",\n \"0.53399\",\n \"0.53188425\",\n \"0.5316743\",\n \"0.5306752\",\n \"0.53003365\",\n \"0.52994305\",\n \"0.52805585\",\n \"0.5258978\",\n \"0.5248693\",\n \"0.5240499\",\n \"0.52372605\",\n \"0.52321255\",\n \"0.52278715\",\n \"0.522474\",\n \"0.5224181\",\n \"0.5219932\",\n \"0.5210591\",\n \"0.51919544\",\n \"0.5191377\",\n \"0.5189229\",\n \"0.51803416\",\n \"0.51796687\",\n \"0.5174335\",\n \"0.5170567\",\n \"0.51703274\",\n \"0.5166434\",\n \"0.5165798\",\n \"0.5154533\",\n \"0.5154533\",\n \"0.5132926\",\n \"0.5130445\",\n \"0.5129523\",\n \"0.5116652\",\n \"0.51141596\",\n \"0.51094675\",\n \"0.5104974\",\n \"0.51027673\",\n \"0.51016885\",\n \"0.51010877\",\n \"0.5099939\",\n \"0.5089497\",\n \"0.50865096\",\n \"0.5078001\",\n \"0.50744313\",\n \"0.50635475\",\n \"0.5045448\",\n \"0.504529\",\n \"0.50217324\",\n \"0.50110435\",\n \"0.5009058\",\n \"0.50002354\",\n \"0.49942875\",\n \"0.4992305\",\n \"0.4991577\",\n \"0.4991238\",\n \"0.49875164\",\n \"0.49856395\",\n \"0.4985328\",\n \"0.4983066\",\n \"0.49722615\",\n \"0.49640176\",\n \"0.4944367\",\n \"0.49276116\",\n \"0.4919689\",\n \"0.4916198\",\n \"0.4914819\",\n \"0.49138898\",\n \"0.49079907\",\n \"0.49034205\",\n \"0.49030703\",\n \"0.4900367\",\n \"0.4898219\",\n \"0.489338\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.86921626"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74580,"cells":{"query":{"kind":"string","value":"Tests the __str__ method."},"document":{"kind":"string","value":"def test_str(self):\n name = str(self.engine)\n self.assertTrue(name.startswith('test_database_'))\n self.assertTrue(name.endswith('.test_docs'))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_str_method(self):\n b1 = BaseModel()\n b1printed = b1.__str__()\n self.assertEqual(b1printed,\n \"[BaseModel] ({}) {}\".format(b1.id, b1.__dict__))","def test_str(self):\n self.assertEqual(str(self.bs), str(self.wbs))\n self.assertEqual(str(self.be), str(self.be))\n # str(us) fails in Python 2\n self.assertEqual(str, type(str(self.wus)))\n # str(ue) fails in Python 2\n self.assertEqual(str, type(str(self.wue)))","def test_str(self):\n user = User()\n string = \"[User] ({}) {}\".format(user.id, user.__dict__)\n self.assertEqual(user.__str__(), string)","def test___str__(self):\n self.assertEqual(\n str(self.mineral),\n 'mineralxy')","def test_strmethod(self):\n b1 = BaseModel()\n self.assertEqual(type(str(b1)), str)","def test_str_method(self):\n s1 = Square(4, 6, 2, 12)\n self.assertEqual(s1.__str__(), '[Square] (12) 6/2 - 4')","def test_str(self):\n self.assertEqual(str(self.bioe), str(self.wbioe))\n self.assertEqual(str(self.uioe), str(self.wuioe))\n self.assertEqual(str(self.bose), str(self.wbose))\n self.assertEqual(str(self.uose), str(self.wuose))","def test_str(self):\n self.assertEqual(str(self.john), \"J. Doe\")\n self.assertEqual(str(self.solar), \"N. Graule\")","def test_str_magic_method(self):\n correct_output = \"[User] ({}) {}\".format(\n self.user_1.id, self.user_1.__dict__)\n\n self.assertEqual(correct_output, self.user_1.__str__())","def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)","def test_str(self):\n self.assertEqual(str(self.subject), \"Test Subject\")","def test_str(self):\n s1 = Square(4, 2, 3, 47)\n self.assertEqual(str(s1), \"[Square] (47) 2/3 - 4\")","def __str__(self):\r\n return Assert(self.obj.__str__())","def test__str__(self):\n\n self.assertEqual(\n self.employee.__str__(),\n (\n self.user.get_username() +\n \" - \" +\n self.employee.get_departament_display()\n )\n )","def test_str(self):\n account = AccountFactory(username=\"bill\")\n self.assertEqual(account.__str__(), \"bill\")","def test_str():\n c = Circle(4) \n assert c.__str__() == 'Circle with radius: 4.000000'","def test_str(self):\n self.assertEqual(str(self.content), \"Test Content\")","def test_str_(self):\n str(self.standardcode)\n repr(self.standardcode)","def simple_str(self):\n pass","def test_str(self):\n m = Machine.objects.get(id=1)\n self.assertEqual(str(m), m.visible_name)","def test_str_method(self):\n _name = 'test-name'\n el = MarkerId(_name)\n self.assertEqual(el.__str__(), _name)","def test_string_representation(self):\n entry = Meal(name=\"Foo Meal\")\n self.assertEqual(str(entry), 'Foo Meal')","def __str__(self) -> str:","def __str__(self) -> str:","def __str__(self) -> str:","def __str__(self) -> str:","def test_str(self):\n tour = G(Tour, display_name='test1')\n self.assertEqual('test1', str(tour))","def test_str():\n # Test for string special method with scalar Rnode objects\n x = Rnode(1.0)\n try:\n assert str(x) == 'Reverse-mode Rnode Object ( Values: 1.0 )'\n except AssertionError as e:\n print(e)\n raise AssertionError","def __str__(self):\n return self.string","def __str__(self):\n raise NotImplementedError(\"__str__ not implemented for \"+str(type(self)))","def test_str():\n post = models.Post(title=\"Test Post\")\n\n assert str(post) == post.title","def __str__(self): # reliably restored by inspect\n pass","def test_string_representation(self):\n customer = Customer(first_name=\"Mike\", last_name=\"Kim\")\n self.assertEqual(\n str(customer), \"{} {}\".format(customer.first_name, customer.last_name)\n )","def test_str(self):\n reviewer = Reviewer.objects.get(pk=1)\n self.assertEqual(str(reviewer), 'adam')","def test__str__method(self):\n Rectangle.reset_objects()\n s1 = Square(5)\n self.assertEqual(str(s1), \"[Square] (1) 0/0 - 5\")\n s2 = Square(2, 2)\n self.assertEqual(str(s2), \"[Square] (2) 2/0 - 2\")\n s3 = Square(3, 1, 3)\n self.assertEqual(str(s3), \"[Square] (3) 1/3 - 3\")","def test_str(self):\n reviewer = Company.objects.get(pk=1)\n self.assertEqual(str(reviewer), 'Company 1')","def test_str(self):\r\n statobj = inputtypes.Status('test')\r\n self.assertEqual(str(statobj), 'test')\r\n self.assertEqual(unicode(statobj), u'test')","def test_str(self):\n character = self.character\n\n self.assertEqual(str(character), self.character_raw['name'])","def test_str(self):\n test_title = Post(title='My Latest Blog Post')\n self.assertEqual(str(test_title), 'My Latest Blog Post')","def test_str(self):\n faction = self.faction\n\n self.assertEqual(str(faction), self.faction_raw['name'])","def __str__(self) :\n raise NotImplementedError","def test_string_representation(self) -> None:\n item = Item(text=\"some text\")\n self.assertEqual(str(item), \"some text\")","def test_str(self):\n star = Star.objects.create(name=\"R LEO\")\n observation = Observation(star=star, jd=2456567.2550, magnitude=8.5)\n expected = \"%s %s %s\" % (\n observation.star,\n observation.jd,\n observation.magnitude,\n )\n self.assertEqual(str(observation), expected)","def test_str(self):\n step = G(Step, display_name='test1')\n self.assertEqual('test1', str(step))","def test_str(self):\n user = User()\n user_details = {\"student_id\": user.id, \"first_name\": \"Joe\"}\n student = Student(**user_details)\n string = \"[Student] ({}) {}\".format(student.id, student.to_dict())\n self.assertEqual(string, str(student))","def test_str(self):\n reviewer = Review.objects.get(pk=1)\n self.assertEqual(str(reviewer), 'adam - Company 1')","def test_str(self):\n company = CompanyFactory()\n self.assertEqual(str(company),\n f\"Company {company.name} in {company.city}\")","def __str__(self):\n # print(self.get_string())\n return self.get_string()","def __str__(self):\n # print(self.get_string())\n return self.get_string()","def test_magicstring_method(self):\n\n base_class = BaseModel()\n rep = \"[{}] ({}) <{}>\".format(\n self.__class__.__name__, self.id, self.__dict__)\n\n self.assertEqual(str(base_class), rep)","def test_basemodel_str(self):\n B = BaseModel()\n s = \"[BaseModel] ({}) {}\".format(B.id, B.__dict__)\n self.assertEqual(s, str(B))","def test_basestr(self):\n date1 = datetime.today()\n test1 = BaseModel()\n test1.id = 712456769374\n test1.created_at = date1\n test1.updated_at = date1\n testerstr = test1.__str__()\n self.assertIn(\"[BaseModel] (712456769374)\", testerstr)\n self.assertIn(\"'id': 712456769374\", testerstr)\n self.assertIn(\"'created_at': \" + repr(date1), testerstr)\n self.assertIn(\"'updated_at': \" + repr(date1), testerstr)","def __str__(self) -> str:\n pass","def test_str(self):\n dummy = DummyCryptographicObject()\n str(dummy)","def __str__(self):\n raise RuntimeError(\"Needs to be implemented in base class\")","def test_str(self, r, rep):\n assert str(r) == rep","def test_str(self):\n log = self.log\n\n self.assertEqual(str(log), self.log_raw['name'])","def testCardStr(self):\n cardObj = Card('A','d')\n self.assertEquals('Ad',str(cardObj))","def stringable(self):\n return True","def __str__(self):\n return self._str","def __str__(self):\n return self.get_string()","def test_follow_str(self):\n follow = self.follow\n string = follow.__str__()\n expected_string = f\"{self.user} following {self.user_2}\"\n self.assertEqual(string, expected_string)","def __str__(self) -> str:\n raise NotImplementedError","def test_str(self):\n location = self.location\n\n self.assertEqual(str(location), self.location_raw['name'])","def __str__(self):\n raise NotImplementedError()","def __str__(self):\n raise NotImplementedError(\"Should be implemented by subclass\")","def test_ship_str(self):\n s1 = Ship.objects.get(imo_number=9632179)\n self.assertEqual(str(s1), 'Mathilde Maersk (9632179)')","def test_string_representation(self):\n school1 = School.objects.get(name=\"Test School 1\")\n self.assertEqual(str(school1), \"Test School 1\")","def __str__(self):\n pass;","def __str__(self):\n raise NotImplementedError(\"Implemented in a subclass\")","def __str__(self):\n return self.get_str()","def test_str(self):\n item = self.item\n\n self.assertEqual(str(item), self.item_raw['name'])","def __str__(self):","def __str__(self):","def __str__(self):","def __str__(self):","def __str__(self):","def __str__(self):","def test_str(self):\r\n self.assertEqual(str(self.black), 'black:#000000')\r\n self.assertEqual(str(self.red), 'red:#ff0000')\r\n self.assertEqual(str(self.pink), 'pink:#640000')","def test_str_method(self):\n r = Review()\n try:\n stdout, sys.stdout = sys.stdout, StringIO()\n print(r)\n output = sys.stdout.getvalue().strip()\n expect = '[{}] ({}) {}'.format(type(r).__name__, r.id, r.__dict__)\n assert output == expect\n finally:\n sys.stdout = stdout","def __str__(self):\n pass","def __str__(self):\n pass","def __str__(self):\n pass","def __str__(self):\n pass","def __str__(self):\n pass","def __str__(self):\n pass","def __str__(self):\n pass","def __str__(self):\n pass","def __str__(self):\n pass","def __str__(self):\n pass","def __str__(self):\n pass","def test_str_representation(self):\n self.assertEqual(str(self.comment),\n f'{self.comment.comment_description[:20]}...')","def __str__(self) -> str:\r\n return self.process(self.string)","def testStringRepresentationOnInstantiation(self):\r\n self.assertEqual(str(self.tv), 'table')","def __str__(self):\r\n return self.__call__()","def test_str(self, string, application):\n assert string == str(application)","def __str__(self):\n return str(self.__s)","def test_str(self):\n \n # Create a Resource object\n book = Book(\"Penguin Group\", \"New York\", \"fiction\", 1, \"White Noise\", \n Name(\"Don\", \"\", \"DeLillo\"), \n \"Delillo's White Noise follows narrator Jack \"\\\n \"Gladney, a professor at a small Liberal Arts \"\\\n \"college and describes an academic year. Jack \"\\\n \"teaches at a school called the \"\\\n \"College-on-the-Hill, where he serves as the \"\\\n \"department chair of Hitler studies. He lives in \"\\\n \"Blacksmith, a quiet college town, with his wife, \"\\\n \"Babette, and four of their children from earlier \"\\\n \"marriages: Heinrich, Steffie, Denise, and \"\\\n \"Wilder. Throughout the novel, various \"\\\n \"half-siblings and ex-spouses drift in and out \"\\\n \"of the family’s home.\",\n \"sci-fi\", \"English\", 1985, \"US\", 326, \"book\",\n [\"culture\", \"survival\", \"life\", \"society\"])\n \n # Assert expected result of the str function\n self.assertEqual(str(book), (\"ID: 1 \\nTitle: White Noise \"\\\n \"\\nCreator: Don DeLillo \\nSummary: Delillo's White Noise follows \"\\\n \"narrator Jack Gladney, a professor at a \\nsmall Liberal Arts \"\\\n \"college and describes an academic year. Jack teaches \\nat ... \"\\\n \"\\nGenre: sci-fi \\nLanguage: English \\nYear: 1985 \"\\\n \"\\nCountry: US \\nLength: 326p \\nType: book \"\\\n \"\\nKeywords: culture, life, society, survival\\nPublisher: \"\\\n \"Penguin Group \\nCity: New York \\nCategory: fiction\"))","def test_string_representation(self):\n test_school_program = SchoolProgram.objects.get(id=self.school_program.id)\n self.assertEqual(str(test_school_program), \"Test School 1 - MS Computer Science\")","def test_str(self):\n obj = objects.OpaqueObject(self.bytes_a, enums.OpaqueDataType.NONE)\n expected = str(binascii.hexlify(self.bytes_a))\n observed = str(obj)\n self.assertEqual(expected, observed)","def test_car_model_str_representation(car_model):\n assert str(car_model) == \"Golf\""],"string":"[\n \"def test_str_method(self):\\n b1 = BaseModel()\\n b1printed = b1.__str__()\\n self.assertEqual(b1printed,\\n \\\"[BaseModel] ({}) {}\\\".format(b1.id, b1.__dict__))\",\n \"def test_str(self):\\n self.assertEqual(str(self.bs), str(self.wbs))\\n self.assertEqual(str(self.be), str(self.be))\\n # str(us) fails in Python 2\\n self.assertEqual(str, type(str(self.wus)))\\n # str(ue) fails in Python 2\\n self.assertEqual(str, type(str(self.wue)))\",\n \"def test_str(self):\\n user = User()\\n string = \\\"[User] ({}) {}\\\".format(user.id, user.__dict__)\\n self.assertEqual(user.__str__(), string)\",\n \"def test___str__(self):\\n self.assertEqual(\\n str(self.mineral),\\n 'mineralxy')\",\n \"def test_strmethod(self):\\n b1 = BaseModel()\\n self.assertEqual(type(str(b1)), str)\",\n \"def test_str_method(self):\\n s1 = Square(4, 6, 2, 12)\\n self.assertEqual(s1.__str__(), '[Square] (12) 6/2 - 4')\",\n \"def test_str(self):\\n self.assertEqual(str(self.bioe), str(self.wbioe))\\n self.assertEqual(str(self.uioe), str(self.wuioe))\\n self.assertEqual(str(self.bose), str(self.wbose))\\n self.assertEqual(str(self.uose), str(self.wuose))\",\n \"def test_str(self):\\n self.assertEqual(str(self.john), \\\"J. Doe\\\")\\n self.assertEqual(str(self.solar), \\\"N. Graule\\\")\",\n \"def test_str_magic_method(self):\\n correct_output = \\\"[User] ({}) {}\\\".format(\\n self.user_1.id, self.user_1.__dict__)\\n\\n self.assertEqual(correct_output, self.user_1.__str__())\",\n \"def test_is_str_rep_string(self):\\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)\",\n \"def test_str(self):\\n self.assertEqual(str(self.subject), \\\"Test Subject\\\")\",\n \"def test_str(self):\\n s1 = Square(4, 2, 3, 47)\\n self.assertEqual(str(s1), \\\"[Square] (47) 2/3 - 4\\\")\",\n \"def __str__(self):\\r\\n return Assert(self.obj.__str__())\",\n \"def test__str__(self):\\n\\n self.assertEqual(\\n self.employee.__str__(),\\n (\\n self.user.get_username() +\\n \\\" - \\\" +\\n self.employee.get_departament_display()\\n )\\n )\",\n \"def test_str(self):\\n account = AccountFactory(username=\\\"bill\\\")\\n self.assertEqual(account.__str__(), \\\"bill\\\")\",\n \"def test_str():\\n c = Circle(4) \\n assert c.__str__() == 'Circle with radius: 4.000000'\",\n \"def test_str(self):\\n self.assertEqual(str(self.content), \\\"Test Content\\\")\",\n \"def test_str_(self):\\n str(self.standardcode)\\n repr(self.standardcode)\",\n \"def simple_str(self):\\n pass\",\n \"def test_str(self):\\n m = Machine.objects.get(id=1)\\n self.assertEqual(str(m), m.visible_name)\",\n \"def test_str_method(self):\\n _name = 'test-name'\\n el = MarkerId(_name)\\n self.assertEqual(el.__str__(), _name)\",\n \"def test_string_representation(self):\\n entry = Meal(name=\\\"Foo Meal\\\")\\n self.assertEqual(str(entry), 'Foo Meal')\",\n \"def __str__(self) -> str:\",\n \"def __str__(self) -> str:\",\n \"def __str__(self) -> str:\",\n \"def __str__(self) -> str:\",\n \"def test_str(self):\\n tour = G(Tour, display_name='test1')\\n self.assertEqual('test1', str(tour))\",\n \"def test_str():\\n # Test for string special method with scalar Rnode objects\\n x = Rnode(1.0)\\n try:\\n assert str(x) == 'Reverse-mode Rnode Object ( Values: 1.0 )'\\n except AssertionError as e:\\n print(e)\\n raise AssertionError\",\n \"def __str__(self):\\n return self.string\",\n \"def __str__(self):\\n raise NotImplementedError(\\\"__str__ not implemented for \\\"+str(type(self)))\",\n \"def test_str():\\n post = models.Post(title=\\\"Test Post\\\")\\n\\n assert str(post) == post.title\",\n \"def __str__(self): # reliably restored by inspect\\n pass\",\n \"def test_string_representation(self):\\n customer = Customer(first_name=\\\"Mike\\\", last_name=\\\"Kim\\\")\\n self.assertEqual(\\n str(customer), \\\"{} {}\\\".format(customer.first_name, customer.last_name)\\n )\",\n \"def test_str(self):\\n reviewer = Reviewer.objects.get(pk=1)\\n self.assertEqual(str(reviewer), 'adam')\",\n \"def test__str__method(self):\\n Rectangle.reset_objects()\\n s1 = Square(5)\\n self.assertEqual(str(s1), \\\"[Square] (1) 0/0 - 5\\\")\\n s2 = Square(2, 2)\\n self.assertEqual(str(s2), \\\"[Square] (2) 2/0 - 2\\\")\\n s3 = Square(3, 1, 3)\\n self.assertEqual(str(s3), \\\"[Square] (3) 1/3 - 3\\\")\",\n \"def test_str(self):\\n reviewer = Company.objects.get(pk=1)\\n self.assertEqual(str(reviewer), 'Company 1')\",\n \"def test_str(self):\\r\\n statobj = inputtypes.Status('test')\\r\\n self.assertEqual(str(statobj), 'test')\\r\\n self.assertEqual(unicode(statobj), u'test')\",\n \"def test_str(self):\\n character = self.character\\n\\n self.assertEqual(str(character), self.character_raw['name'])\",\n \"def test_str(self):\\n test_title = Post(title='My Latest Blog Post')\\n self.assertEqual(str(test_title), 'My Latest Blog Post')\",\n \"def test_str(self):\\n faction = self.faction\\n\\n self.assertEqual(str(faction), self.faction_raw['name'])\",\n \"def __str__(self) :\\n raise NotImplementedError\",\n \"def test_string_representation(self) -> None:\\n item = Item(text=\\\"some text\\\")\\n self.assertEqual(str(item), \\\"some text\\\")\",\n \"def test_str(self):\\n star = Star.objects.create(name=\\\"R LEO\\\")\\n observation = Observation(star=star, jd=2456567.2550, magnitude=8.5)\\n expected = \\\"%s %s %s\\\" % (\\n observation.star,\\n observation.jd,\\n observation.magnitude,\\n )\\n self.assertEqual(str(observation), expected)\",\n \"def test_str(self):\\n step = G(Step, display_name='test1')\\n self.assertEqual('test1', str(step))\",\n \"def test_str(self):\\n user = User()\\n user_details = {\\\"student_id\\\": user.id, \\\"first_name\\\": \\\"Joe\\\"}\\n student = Student(**user_details)\\n string = \\\"[Student] ({}) {}\\\".format(student.id, student.to_dict())\\n self.assertEqual(string, str(student))\",\n \"def test_str(self):\\n reviewer = Review.objects.get(pk=1)\\n self.assertEqual(str(reviewer), 'adam - Company 1')\",\n \"def test_str(self):\\n company = CompanyFactory()\\n self.assertEqual(str(company),\\n f\\\"Company {company.name} in {company.city}\\\")\",\n \"def __str__(self):\\n # print(self.get_string())\\n return self.get_string()\",\n \"def __str__(self):\\n # print(self.get_string())\\n return self.get_string()\",\n \"def test_magicstring_method(self):\\n\\n base_class = BaseModel()\\n rep = \\\"[{}] ({}) <{}>\\\".format(\\n self.__class__.__name__, self.id, self.__dict__)\\n\\n self.assertEqual(str(base_class), rep)\",\n \"def test_basemodel_str(self):\\n B = BaseModel()\\n s = \\\"[BaseModel] ({}) {}\\\".format(B.id, B.__dict__)\\n self.assertEqual(s, str(B))\",\n \"def test_basestr(self):\\n date1 = datetime.today()\\n test1 = BaseModel()\\n test1.id = 712456769374\\n test1.created_at = date1\\n test1.updated_at = date1\\n testerstr = test1.__str__()\\n self.assertIn(\\\"[BaseModel] (712456769374)\\\", testerstr)\\n self.assertIn(\\\"'id': 712456769374\\\", testerstr)\\n self.assertIn(\\\"'created_at': \\\" + repr(date1), testerstr)\\n self.assertIn(\\\"'updated_at': \\\" + repr(date1), testerstr)\",\n \"def __str__(self) -> str:\\n pass\",\n \"def test_str(self):\\n dummy = DummyCryptographicObject()\\n str(dummy)\",\n \"def __str__(self):\\n raise RuntimeError(\\\"Needs to be implemented in base class\\\")\",\n \"def test_str(self, r, rep):\\n assert str(r) == rep\",\n \"def test_str(self):\\n log = self.log\\n\\n self.assertEqual(str(log), self.log_raw['name'])\",\n \"def testCardStr(self):\\n cardObj = Card('A','d')\\n self.assertEquals('Ad',str(cardObj))\",\n \"def stringable(self):\\n return True\",\n \"def __str__(self):\\n return self._str\",\n \"def __str__(self):\\n return self.get_string()\",\n \"def test_follow_str(self):\\n follow = self.follow\\n string = follow.__str__()\\n expected_string = f\\\"{self.user} following {self.user_2}\\\"\\n self.assertEqual(string, expected_string)\",\n \"def __str__(self) -> str:\\n raise NotImplementedError\",\n \"def test_str(self):\\n location = self.location\\n\\n self.assertEqual(str(location), self.location_raw['name'])\",\n \"def __str__(self):\\n raise NotImplementedError()\",\n \"def __str__(self):\\n raise NotImplementedError(\\\"Should be implemented by subclass\\\")\",\n \"def test_ship_str(self):\\n s1 = Ship.objects.get(imo_number=9632179)\\n self.assertEqual(str(s1), 'Mathilde Maersk (9632179)')\",\n \"def test_string_representation(self):\\n school1 = School.objects.get(name=\\\"Test School 1\\\")\\n self.assertEqual(str(school1), \\\"Test School 1\\\")\",\n \"def __str__(self):\\n pass;\",\n \"def __str__(self):\\n raise NotImplementedError(\\\"Implemented in a subclass\\\")\",\n \"def __str__(self):\\n return self.get_str()\",\n \"def test_str(self):\\n item = self.item\\n\\n self.assertEqual(str(item), self.item_raw['name'])\",\n \"def __str__(self):\",\n \"def __str__(self):\",\n \"def __str__(self):\",\n \"def __str__(self):\",\n \"def __str__(self):\",\n \"def __str__(self):\",\n \"def test_str(self):\\r\\n self.assertEqual(str(self.black), 'black:#000000')\\r\\n self.assertEqual(str(self.red), 'red:#ff0000')\\r\\n self.assertEqual(str(self.pink), 'pink:#640000')\",\n \"def test_str_method(self):\\n r = Review()\\n try:\\n stdout, sys.stdout = sys.stdout, StringIO()\\n print(r)\\n output = sys.stdout.getvalue().strip()\\n expect = '[{}] ({}) {}'.format(type(r).__name__, r.id, r.__dict__)\\n assert output == expect\\n finally:\\n sys.stdout = stdout\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def __str__(self):\\n pass\",\n \"def test_str_representation(self):\\n self.assertEqual(str(self.comment),\\n f'{self.comment.comment_description[:20]}...')\",\n \"def __str__(self) -> str:\\r\\n return self.process(self.string)\",\n \"def testStringRepresentationOnInstantiation(self):\\r\\n self.assertEqual(str(self.tv), 'table')\",\n \"def __str__(self):\\r\\n return self.__call__()\",\n \"def test_str(self, string, application):\\n assert string == str(application)\",\n \"def __str__(self):\\n return str(self.__s)\",\n \"def test_str(self):\\n \\n # Create a Resource object\\n book = Book(\\\"Penguin Group\\\", \\\"New York\\\", \\\"fiction\\\", 1, \\\"White Noise\\\", \\n Name(\\\"Don\\\", \\\"\\\", \\\"DeLillo\\\"), \\n \\\"Delillo's White Noise follows narrator Jack \\\"\\\\\\n \\\"Gladney, a professor at a small Liberal Arts \\\"\\\\\\n \\\"college and describes an academic year. Jack \\\"\\\\\\n \\\"teaches at a school called the \\\"\\\\\\n \\\"College-on-the-Hill, where he serves as the \\\"\\\\\\n \\\"department chair of Hitler studies. He lives in \\\"\\\\\\n \\\"Blacksmith, a quiet college town, with his wife, \\\"\\\\\\n \\\"Babette, and four of their children from earlier \\\"\\\\\\n \\\"marriages: Heinrich, Steffie, Denise, and \\\"\\\\\\n \\\"Wilder. Throughout the novel, various \\\"\\\\\\n \\\"half-siblings and ex-spouses drift in and out \\\"\\\\\\n \\\"of the family’s home.\\\",\\n \\\"sci-fi\\\", \\\"English\\\", 1985, \\\"US\\\", 326, \\\"book\\\",\\n [\\\"culture\\\", \\\"survival\\\", \\\"life\\\", \\\"society\\\"])\\n \\n # Assert expected result of the str function\\n self.assertEqual(str(book), (\\\"ID: 1 \\\\nTitle: White Noise \\\"\\\\\\n \\\"\\\\nCreator: Don DeLillo \\\\nSummary: Delillo's White Noise follows \\\"\\\\\\n \\\"narrator Jack Gladney, a professor at a \\\\nsmall Liberal Arts \\\"\\\\\\n \\\"college and describes an academic year. Jack teaches \\\\nat ... \\\"\\\\\\n \\\"\\\\nGenre: sci-fi \\\\nLanguage: English \\\\nYear: 1985 \\\"\\\\\\n \\\"\\\\nCountry: US \\\\nLength: 326p \\\\nType: book \\\"\\\\\\n \\\"\\\\nKeywords: culture, life, society, survival\\\\nPublisher: \\\"\\\\\\n \\\"Penguin Group \\\\nCity: New York \\\\nCategory: fiction\\\"))\",\n \"def test_string_representation(self):\\n test_school_program = SchoolProgram.objects.get(id=self.school_program.id)\\n self.assertEqual(str(test_school_program), \\\"Test School 1 - MS Computer Science\\\")\",\n \"def test_str(self):\\n obj = objects.OpaqueObject(self.bytes_a, enums.OpaqueDataType.NONE)\\n expected = str(binascii.hexlify(self.bytes_a))\\n observed = str(obj)\\n self.assertEqual(expected, observed)\",\n \"def test_car_model_str_representation(car_model):\\n assert str(car_model) == \\\"Golf\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82420087","0.8141123","0.81234753","0.8109608","0.8077134","0.80220187","0.800164","0.7937999","0.7932961","0.7748346","0.77479196","0.77436966","0.77424705","0.77423435","0.7715638","0.7699125","0.76134163","0.75954235","0.7555366","0.75448424","0.7537086","0.7515191","0.7496381","0.7496381","0.7496381","0.7496381","0.74950105","0.7478341","0.74644244","0.7459018","0.7453437","0.7448232","0.7442385","0.7418837","0.7416698","0.7404875","0.7402296","0.7402215","0.7372689","0.735695","0.73465365","0.73406965","0.73377836","0.73351246","0.73348147","0.7323561","0.7319247","0.73137146","0.73137146","0.73020613","0.73015547","0.72984505","0.7289193","0.7285224","0.7277373","0.7273871","0.72628486","0.7252736","0.7234044","0.72124","0.71873826","0.7177581","0.71767485","0.71747446","0.7172177","0.71712166","0.7158179","0.71542495","0.71476924","0.71417636","0.71351725","0.7125832","0.71207976","0.71207976","0.71207976","0.71207976","0.71207976","0.71207976","0.7117108","0.7105146","0.70791084","0.70791084","0.70791084","0.70791084","0.70791084","0.70791084","0.70791084","0.70791084","0.70791084","0.70791084","0.70791084","0.7068233","0.7061538","0.7032144","0.70179135","0.7016941","0.7016372","0.7015023","0.70090085","0.7006484","0.6991313"],"string":"[\n \"0.82420087\",\n \"0.8141123\",\n \"0.81234753\",\n \"0.8109608\",\n \"0.8077134\",\n \"0.80220187\",\n \"0.800164\",\n \"0.7937999\",\n \"0.7932961\",\n \"0.7748346\",\n \"0.77479196\",\n \"0.77436966\",\n \"0.77424705\",\n \"0.77423435\",\n \"0.7715638\",\n \"0.7699125\",\n \"0.76134163\",\n \"0.75954235\",\n \"0.7555366\",\n \"0.75448424\",\n \"0.7537086\",\n \"0.7515191\",\n \"0.7496381\",\n \"0.7496381\",\n \"0.7496381\",\n \"0.7496381\",\n \"0.74950105\",\n \"0.7478341\",\n \"0.74644244\",\n \"0.7459018\",\n \"0.7453437\",\n \"0.7448232\",\n \"0.7442385\",\n \"0.7418837\",\n \"0.7416698\",\n \"0.7404875\",\n \"0.7402296\",\n \"0.7402215\",\n \"0.7372689\",\n \"0.735695\",\n \"0.73465365\",\n \"0.73406965\",\n \"0.73377836\",\n \"0.73351246\",\n \"0.73348147\",\n \"0.7323561\",\n \"0.7319247\",\n \"0.73137146\",\n \"0.73137146\",\n \"0.73020613\",\n \"0.73015547\",\n \"0.72984505\",\n \"0.7289193\",\n \"0.7285224\",\n \"0.7277373\",\n \"0.7273871\",\n \"0.72628486\",\n \"0.7252736\",\n \"0.7234044\",\n \"0.72124\",\n \"0.71873826\",\n \"0.7177581\",\n \"0.71767485\",\n \"0.71747446\",\n \"0.7172177\",\n \"0.71712166\",\n \"0.7158179\",\n \"0.71542495\",\n \"0.71476924\",\n \"0.71417636\",\n \"0.71351725\",\n \"0.7125832\",\n \"0.71207976\",\n \"0.71207976\",\n \"0.71207976\",\n \"0.71207976\",\n \"0.71207976\",\n \"0.71207976\",\n \"0.7117108\",\n \"0.7105146\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.70791084\",\n \"0.7068233\",\n \"0.7061538\",\n \"0.7032144\",\n \"0.70179135\",\n \"0.7016941\",\n \"0.7016372\",\n \"0.7015023\",\n \"0.70090085\",\n \"0.7006484\",\n \"0.6991313\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":74581,"cells":{"query":{"kind":"string","value":"Sets up an ElasticSearch index and inserts the test_docs provided by FilterTestCaseMixin."},"document":{"kind":"string","value":"def setUp(self):\n super(MongoDbFilterTestCase, self).setUp()\n\n for doc in self.test_docs:\n self.engine.insert(doc)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setUp(self):\n body = {\n \"settings\": {\n \"number_of_shards\": 1,\n \"number_of_replicas\": 0\n }\n }\n self.assertTrue(self.es.create_index('contacts_esclient_test', body))\n self.assertFalse(self.es.create_index('contacts_esclient_test', body))\n\n self.assertTrue(self.es.create_index('contacts_esclient_test2', body))\n self.assertFalse(self.es.create_index('contacts_esclient_test2', body))\n\n\n \"\"\" Index some test data \"\"\"\n data = {\"name\": \"Joe Tester\",\"age\": 21, \"sex\": \"male\"}\n self.assertTrue(self.es.index(\"contacts_esclient_test\", \"person\", body=data,\n docid=1))\n data = {\"name\": \"Joe Schmoe\",\"age\": 17, \"sex\": \"male\"}\n self.assertTrue(self.es.index(\"contacts_esclient_test\", \"person\", body=data,\n docid=2))\n\n self.assertTrue(self.es.refresh('contacts_esclient_test'))","def setup(self):\n collection = self._get_collection()\n\n indices = copy(self.params[\"indices\"])\n\n if \"when\" not in indices:\n indices[\"when\"] = {}\n\n for index in indices:\n self.log(DEBUG, \"Ensuring we have index for {}\".format(index))\n\n options = indices[index]\n collection.create_index(index, *options)\n self.log(DEBUG, \"Done.\")","def init(self):\n self._es.create_index_template(\n name=DATASETS_INDEX_NAME,\n template=DATASETS_INDEX_TEMPLATE,\n force_recreate=True,\n )\n self._es.create_index(DATASETS_INDEX_NAME)","def set_up(self):\n # This test suite IGNORES datastore inconsistency because it is only\n # interested in what content eventually gets into the search index.\n #\n # Consequently, this suite should only contain tests where this\n # assumption is both valid and required. Any other test should use the\n # default PopulatedTest Case, which assumes maximum inconsistency.\n self.consistency_probability = 1\n super(ConsistentSearchTest, self).set_up()\n\n # We'll want ready access to a Cron instance and the content search\n # index.\n self.cron = Cron(self.admin_api)\n indexer = Indexer.get_or_insert('the-indexer')\n indexer.delete_all_content() # fresh start, no pre-populated stuff\n self.search_index = indexer.get_index()","def setUp(self):\n self.es = Elasticsearch(hosts=[LOCAL_ELASTICSEARCH])\n try:\n self.es.indices.delete(index='packages')\n except NotFoundError:\n pass\n self.runner = CliRunner()\n self.dbfilename = 'test.db'\n config._set_connection_string('sqlite:///test.db')","def test_init(self):\n es = elasticsearch.ElasticSearch(server='8.8.8.8',\n user='alice',\n password='iLoveDogs',\n doc_type='someLogCategory')\n\n self.assertTrue(isinstance(es, elasticsearch.ElasticSearch))","def create_indices():\n destroy_indices()\n\n ActionDocument._index.create(ignore=[400, 404])\n ClassificationDocument._index.create(ignore=[400, 404])\n FunctionDocument._index.create(ignore=[400, 404])\n PhaseDocument._index.create(ignore=[400, 404])\n RecordDocument._index.create(ignore=[400, 404])\n\n yield\n\n destroy_indices()","def build(self):\n\t\tself.documents = self.get_items_to_index()\n\t\tself.build_index()","def setUp(self):\n self.patcher = patch('esis.es.Elasticsearch')\n self.elasticsearch_cls = self.patcher.start()\n self.client = Client(host='localhost', port=9200)","def store_index(self, index, doc_type, source_list, init_id):\n\n bulk_actions = []\n doc_id = init_id\n\n for source in source_list:\n data_body = ElasticSearchUtility.__index_data_body(index, doc_type, doc_id, source[\"_source\"])\n bulk_actions.append(data_body)\n doc_id += 1\n\n print 'inserting - ', len(bulk_actions)\n helpers.bulk(self.es, bulk_actions)","def prepare_environment():\n elastic_search = Elasticsearch('{}:{}'.format(\n _CONFIG.elastic.elastic_hostname,\n _CONFIG.elastic.elastic_port))\n try:\n if not elastic_search.indices.exists(_CONFIG.elastic.elastic_index):\n elastic_search.indices.create(\n index=_CONFIG.elastic.elastic_index,\n body=_CONFIG.elastic.metadata_index_setup)\n except ConnectionError:\n sys.exit(\"Can't start because of no connection to ElasticSearch.\")","def test_index(self):","def create_indices(self) -> None:\n self.client.indices.create(\n index=\"business\",\n body=BUSINESS_MAPPINGS\n )\n self.client.indices.create(\n index=\"review\",\n body=REVIEW_MAPPINGS\n )\n self.client.indices.create(\n index=\"tip\",\n body=TIP_MAPPINGS\n )","def create_index(self):\n self.send_robust(self.es_index, data=self.es_meta)\n self.set_index_normal_settings()","def test_creating_index_type(self):","def create_index():","def setUp(self):\n self.twitter = Twitter(CUR_DIR + \"/test_crossfit.tweets\", CUR_DIR + \"/test_stop_words.txt\")\n self.twitter.load_tweets_and_build_index()\n\n self.searcher = Searcher(self.twitter.tweets, self.twitter.stop_words)","def test_export_index(self):","def test_add_index_settings(self):\n response = self.post('base:add_index', self.form_data(), **self.slugs())\n assert 'base/project.html' in response.template_name\n\n response = self.get('base:index_settings', **self.index_slugs())\n assert 'base/index_update_form.html' in response.template_name","def test_queryset_index(self):\n car = Car.objects.all()\n car.index_to_appsearch()\n # Note that the app search chunk size is set to 5 in `tests.settings`\n # Therefore you should see 5 calls to cover 22 documents\n self.assertEqual(self.client_index.call_count, 5)","def setUp(self):\r\n super(TestCourseIndex, self).setUp()\r\n # had a problem where index showed course but has_access failed to retrieve it for non-staff\r\n self.odd_course = CourseFactory.create(\r\n org='test.org_1-2',\r\n number='test-2.3_course',\r\n display_name='dotted.course.name-2',\r\n )","def create_index(self):\r\n\r\n #### Begin functionality here\r\n\r\n return()","def es():\n # pylint: disable=invalid-name\n\n client = Elasticsearch(ES_TEST_HOSTS)\n client.indices.create(index=ES_TEST_INDEX)\n yield client\n client.indices.delete(index=ES_TEST_INDEX)","def test_index_containers(self):\n pass","def build_index():\n pass","def test_elasticsearch(self):\n\n full_log_path = get_full_test_logs_path(cname=self)\n log.info(f\"Logs file path name is : {full_log_path}\")\n log.info(\"The ElasticSearch deployment test started.\")\n if self.es.get_health():\n log.info(\"The Status of the elasticsearch is OK\")\n else:\n log.warning(\"The Status of the elasticsearch is Not OK\")\n log.info(\"Waiting another 30 sec.\")\n time.sleep(30)\n if self.es.get_health():\n log.info(\"The Status of the elasticsearch is OK\")\n else:\n log.error(\"The Status of the elasticsearch is Not OK ! Exiting.\")\n\n if self.es.get_health():\n log.info(\"\\nThe Elastic-Search server information :\\n\")\n log.info(f\"The Elasticsearch IP is {self.es.get_ip()}\")\n log.info(f\"The Elasticsearch port is {self.es.get_port()}\")\n log.info(f\"The Password to connect is {self.es.get_password()}\")\n\n else:\n assert False, \"The Elasticsearch module is not ready !\"\n\n log.info(f\"Test UUDI is : {self.smallfile_run(self.es)}\")\n\n assert self.es.dumping_all_data(full_log_path), \"Can not Retrieve the test data\"\n\n assert run_command(\n f\"ls {full_log_path}/FullResults.tgz\"\n ), \"Results file did not retrieve from pod\"\n\n try:\n main_es = Elasticsearch(\n [\n {\n \"host\": defaults.ELASTICSEARCH_DEV_IP,\n \"port\": defaults.ELASTICSEARCE_PORT,\n \"url\": f\"http://{defaults.ELASTICSEARCH_DEV_IP}:{defaults.ELASTICSEARCE_PORT}\",\n }\n ]\n )\n except esexp.ConnectionError:\n log.warning(\"Cannot connect to ES server in the LocalServer\")\n main_es = None\n assert elasticsearch_load(\n main_es, full_log_path\n ), \"Can not load data into Main ES server\"","def _SetupIndexes(self, _open=open):\n pass","def setup(self):\n self.create_songs_index()\n self.create_fingerprints_index()\n self.delete_unfingerprinted()","def init_index(clear=False):\n return _run_indexer_func(\"init_index\", clear)","def __init__(self):\n super().__init__()\n self.index_dir = self.base_dir + \"ticket/\"\n self.index_schema = self.__get_index_schema()\n if not os.path.exists(self.index_dir):\n os.makedirs(self.index_dir)\n self.indexer = index.create_in(self.index_dir, self.index_schema)\n else:\n self.indexer = index.open_dir(self.index_dir)","def build_index(self):\n self.rebuild_index()","def create(\n self,\n index: IO,\n request_options: Optional[_models.RequestOptions] = None,\n *,\n content_type: str = \"application/json\",\n **kwargs: Any\n ) -> _models.SearchIndex:","def __init__(self):\n super().__init__()\n self.index_dir = self.base_dir + \"user/\"\n self.index_schema = self.__get_index_schema()\n if not os.path.exists(self.index_dir):\n os.makedirs(self.index_dir)\n self.indexer = index.create_in(self.index_dir, self.index_schema) # creates the index\n else:\n self.indexer = index.open_dir(self.index_dir) # opens the index if it already exists","def create_index():\n es = Elasticsearch()\n es.indices.delete(index='drugaid-index')\n with open('../data_crawling/demo_drug_data-fixed.json') as f:\n drug_data = json.load(f)\n\n for drug in drug_data:\n es.index(index='drugaid-index', doc_type='drug', body=drug)","def init_index(self):\n raise NotImplementedError","def setup_mapping():\n # Mapping describes how elasticsearch handles a document during indexing.\n # Most fields are detected and mapped automatically.\n m = {\n # Turn off analysis on name so we can sort by it.\n 'name': {'index': 'not_analyzed', 'type': 'string'},\n }\n es = elasticutils.get_es()\n try:\n es.create_index_if_missing(settings.ES_INDEX)\n es.put_mapping(Addon._meta.app_label, {'properties': m},\n settings.ES_INDEX)\n except pyes.ElasticSearchException:\n pass","def setUp(self):\n super(IndexViewsTestCase, self).setUp()\n params = POST1_PARAMS.copy()\n params['blog'] = self.test_blog\n params['author'] = self.test_user1.author\n self.add_post(params)","def create(\n self,\n index: _models.SearchIndex,\n request_options: Optional[_models.RequestOptions] = None,\n *,\n content_type: str = \"application/json\",\n **kwargs: Any\n ) -> _models.SearchIndex:","def __init__(\n self,\n host: str = \"localhost\",\n port: int = 9200,\n username: str = \"\",\n password: str = \"\",\n index: str = \"document\",\n search_fields: Union[str, list] = \"text\",\n text_field: str = \"text\",\n name_field: str = \"name\",\n external_source_id_field: str = \"external_source_id\",\n embedding_field: Optional[str] = None,\n embedding_dim: Optional[int] = None,\n custom_mapping: Optional[dict] = None,\n excluded_meta_data: Optional[list] = None,\n faq_question_field: Optional[str] = None,\n scheme: str = \"http\",\n ca_certs: bool = False,\n verify_certs: bool = True,\n create_index: bool = True\n ):\n self.client = Elasticsearch(hosts=[{\"host\": host, \"port\": port}], http_auth=(username, password),\n scheme=scheme, ca_certs=ca_certs, verify_certs=verify_certs)\n\n # if no custom_mapping is supplied, use the default mapping\n if not custom_mapping:\n custom_mapping = {\n \"mappings\": {\n \"properties\": {\n name_field: {\"type\": \"text\"},\n text_field: {\"type\": \"text\"},\n external_source_id_field: {\"type\": \"text\"},\n }\n }\n }\n if embedding_field:\n custom_mapping[\"mappings\"][\"properties\"][embedding_field] = {\"type\": \"dense_vector\",\n \"dims\": embedding_dim}\n # create an index if not exists\n if create_index:\n self.client.indices.create(index=index, ignore=400, body=custom_mapping)\n self.index = index\n\n # configure mappings to ES fields that will be used for querying / displaying results\n if type(search_fields) == str:\n search_fields = [search_fields]\n\n #TODO we should implement a more flexible interal mapping here that simplifies the usage of additional,\n # custom fields (e.g. meta data you want to return)\n self.search_fields = search_fields\n self.text_field = text_field\n self.name_field = name_field\n self.external_source_id_field = external_source_id_field\n self.embedding_field = embedding_field\n self.excluded_meta_data = excluded_meta_data\n self.faq_question_field = faq_question_field","def setUp(self):\n self.docCounter = 0","def _initIndexes(self):\n class Record:\n \"\"\" a moron simple object for carrying the 'extra'-payload to index\n constructors\n \"\"\"\n def __init__(self, **kw):\n self.__dict__.update(kw)\n\n addIndex = self.addIndex\n addColumn = self.addColumn\n\n # Content indexes\n self._catalog.indexes.clear()\n for (index_name, index_type, extra) in self.enumerateIndexes():\n if extra is None:\n addIndex( index_name, index_type)\n else:\n if isinstance(extra, StringTypes):\n p = Record(indexed_attrs=extra)\n elif isinstance(extra, DictType):\n p = Record(**extra)\n else:\n p = Record()\n addIndex( index_name, index_type, extra=p )\n\n # Cached metadata\n self._catalog.names = ()\n self._catalog.schema.clear()\n for column_name in self.enumerateColumns():\n addColumn( column_name )","def bulk_push_to_elastic(elastic_search_url, index, docs):\n CREATE_TEMPLATE = {\"create\": {\"_index\": index, \"_type\": \"_doc\", \"_id\": \"\"}}\n\n bulk_request_body = \"\"\n for doc in docs:\n CREATE_TEMPLATE[\"create\"][\"_id\"] = doc[HASH_ID]\n bulk_request_body += json.dumps(CREATE_TEMPLATE) + NEW_LINE\n bulk_request_body += json.dumps(doc) + NEW_LINE\n\n # Request\n headers = {\"content-type\": \"application/x-ndjson\"}\n\n url = elastic_search_url + \"/\" + \"_bulk\"\n\n response = requests.post(url, data=bulk_request_body, headers=headers)\n return response","def setup_index_shards_per_node(cluster: str, index: str, number: int):\n\n elastic = sreElastic(host=cluster)\n elastic.set_number_shards_per_node(index=index, number=number)","def setup_search(self):\n if self.testing:\n return MockExternalSearchIndex()\n else:\n search = ExternalSearchIndex(self._db)\n if not search:\n self.log.warning(\"No external search server configured.\")\n return None\n return search","def es(request):\n from django.conf import settings\n from search.index import get_elasticsearch\n from search.search import get_indices_alias\n\n # Clear lru_cache of Elasticsearch indices.\n get_indices_alias.cache_clear()\n\n # Test setup\n xdist_suffix = getattr(\n request.config, 'slaveinput', {}\n ).get('slaveid', 'es_test_index')\n es_prefix = f'{settings.ES_INDEX_PREFIX}{xdist_suffix}'\n setattr(settings, 'ES_INDEX_PREFIX', es_prefix)\n\n # Actual test\n yield\n\n # Test teardown\n get_elasticsearch().indices.delete(index=f'{es_prefix}*')","def test_add_documents_empty(empty_index):\n index = empty_index()\n with pytest.raises(Exception) as e_info:\n index.add_documents(\"\")\n assert e_info.value.code == \"missing_payload\"\n assert e_info.value.type == \"invalid_request\"","def indexDocsToES(indexName):\n docs = pd.DataFrame()\n try:\n docs = pd.read_csv(\"documents/full_news_documents.csv\")\n except FileNotFoundError:\n print(\"File not found\")\n\n use_these_keys = ['id', 'category', 'headline', 'authors', 'link', 'short_description', 'date', 'body']\n helpers.bulk(es_client, doc_generator(docs, indexName, use_these_keys))","def test_get_documents_populated(index_with_documents):\n response = index_with_documents().get_documents()\n assert isinstance(response.results, list)\n assert len(response.results) == 20","def test_search(self):\n self.elasticsearch_cls().search.return_value = {\n 'hits': {\n 'total': 10,\n 'hits': [1, 2, 3, 4],\n },\n '_scroll_id': 'abcd'\n }\n self.elasticsearch_cls().scroll.side_effect = [\n {'hits': {'hits': [5, 6, 7]}},\n {'hits': {'hits': [8, 9, 10]}},\n {'hits': {'hits': []}},\n ]\n\n query = 'this is the query'\n hits = list(self.client.search(query))\n\n self.assertListEqual(\n hits,\n [[1, 2, 3, 4], [5, 6, 7], [8, 9, 10]])","def main():\n try:\n res = requests.get('http://localhost:9200')\n pprint(json.loads(res.content.decode('utf-8')))\n except requests.exceptions.ConnectionError:\n print(\"ERROR: ELASTICSEARCH Server is not running!\")\n exit(-1)\n\n # scrapeAndSaveNewsArticles()\n # generateNewsDocsCSV() # may need to be modified based on how scrapeAndSave function file output\n if not es_client.indices.exists(index='huffpost_news_index'):\n print(\"PLEASE WAIT... LOADING DOCUMENTS INTO INVERTED INDEX\")\n indexDocsToES('huffpost_news_index')","def setUp(self):\n self.builder = SphinxBuilder()\n\n # set up a place for a fake sphinx project\n self.twistedRootDir = FilePath(self.mktemp())\n self.sphinxDir = self.twistedRootDir.child(\"docs\")\n self.sphinxDir.makedirs()\n self.sourceDir = self.sphinxDir","def setUpTestData(cls) -> None:\n\n # Define base url\n cls.url = BASE_URL + '/'\n\n # Make 9 \"normal\" authors.\n cls.authors: typing.List[Author] = [\n create_author() for _ in range(9)\n ]\n\n # Make 1 superuser author.\n cls.super_author: Author = create_author(True)\n\n # Serialize data once so that it's not called in ever test\n cls.serialized_data = AuthorListSerializer(Author.objects.all(), many=True).data","def setUp(self):\r\n\r\n User.query.delete()\r\n Article.query.delete()\r\n db.session.commit()\r\n\r\n u = User(\r\n username=\"testuser\",\r\n password=\"HASHED_PASSWORD\",\r\n location=\"US-VA\"\r\n )\r\n\r\n db.session.add(u)\r\n db.session.commit()\r\n\r\n a = Article(\r\n path='testPath',\r\n url='https://wwww.testurl.com',\r\n location=\"US\",\r\n title=\"testTitle\",\r\n excerpt=\"testExcerpt\",\r\n image=\"https://www.testurl.com/testimg.jpg\",\r\n source=\"testSource\",\r\n published_date=\"2020-04-25 05:06:00\",\r\n saved_by=u.id\r\n )\r\n\r\n db.session.add(a)\r\n db.session.commit()\r\n\r\n self.client = app.test_client()\r\n app.config['TESTING'] = True\r\n app.config['WTF_CSRF_ENABLED'] = False","def test_index_stats(self):\n #Create Index\n self.run_multi_operations(buckets = self.buckets,\n query_definitions = self.query_definitions,\n create_index = True, drop_index = False)\n #Check Index Stats\n self.sleep(30)\n index_map = self.get_index_stats()\n self.log.info(index_map)\n for query_definition in self.query_definitions:\n index_name = query_definition.index_name\n for bucket in self.buckets:\n bucket_name = bucket.name\n check_keys = ['items_count', 'total_scan_duration', 'num_docs_queued',\n 'num_requests', 'num_rows_returned', 'num_docs_queued',\n 'num_docs_pending','delete_bytes' ]\n map = self._create_stats_map(items_count=2016)\n self._verify_index_stats(index_map, index_name, bucket_name, map, check_keys)","def build_index(self):\n self.create_index()\n logger.debug(f\"Building index with {self.n_trees} trees.\")\n\n for i in range(len(self.corpus_embeddings)):\n self.index.add_item(i, self.corpus_embeddings[i])\n self.index.build(self.n_trees)","def _build_search(self, index, **kwargs):\n startdate = kwargs.get('startdate', None)\n if startdate:\n timefield = kwargs.get('timefield')\n enddate = kwargs.get('enddate', 'now')\n filters = kwargs.get('filters', None)\n exclude = kwargs.get('exclude', None)\n ranges = kwargs.get('ranges', None)\n fields_to_include = kwargs.get('field_to_include', None)\n wildcards = kwargs.get('wildcard', None)\n start_from = kwargs.get('from_', None)\n size = kwargs.get('size', None)\n sort_ = kwargs.get('sort', None)\n\n search = Search(using=self.es, index=index, doc_type=self.doc_type)\\\n .params(request_timeout=2000)\n\n if startdate:\n if startdate != enddate:\n timeRange = {timefield: {'gte': startdate, 'lt': enddate}}\n else:\n timeRange = {timefield: {'gte': startdate, 'lte': enddate}}\n search = search.filter('range', **timeRange)\n if filters:\n for key, val in filters.items():\n search = search.filter('terms' if isinstance(val, list) else 'term', **{key: val})\n if exclude:\n for ex in exclude.keys():\n search = search.exclude('terms', **{ex: exclude[ex]})\n if ranges:\n # ranges are expected in format:\n # [{field:{'gte':value, 'lte':value}}, {field: {'gte': value}}, {field: {'lte': value}}]\n for range_filter in ranges:\n search = search.filter('range', **range_filter)\n if fields_to_include:\n for field in fields_to_include.keys():\n search = search.source(**{field: fields_to_include[field]})\n if wildcards:\n for wild in wildcards:\n search = search.filter('wildcard', **{wild: wildcards[wild]})\n if start_from:\n search = search.extra(**{\"from_\": start_from})\n if size:\n search = search.extra(**{\"size\": size})\n if sort_:\n search = search.sort(*sort_)\n\n self._logger.info(json.dumps(search.to_dict()))\n\n return search","def test_track_index(self, collection):\n collection.create_index(\"hello\", unique=True)\n collection.insert_many([{\"hello\": \"here\"}, {\"hello\": 2}])\n assert collection._indexes == {\n \"_id_\": ((\"_id\",), {(1,), (2,), (3,)}),\n \"hello_1\": ((\"hello\",), {(\"there\",), (\"here\",), (2,)}),\n }","def set_in_index(document, index, type):\n response = None\n\n #Try 3 times to store the document in ES, each time picking a random ES node address in case of failure\n for retries in range(3):\n try:\n log('ES Set Request :: ' + json.dumps(document) + ' : ' + index + ':' + type)\n response = es.index(index=index, doc_type=type, id=document['id'], body=document)\n log(\"ES Set Response :: \" + json.dumps(response))\n except ImproperlyConfigured:\n log(\"ES ImproperlyConfigured!\" + traceback.format_exc())\n continue\n except ElasticsearchException:\n log(\"ES ElasticsearchException!\" + traceback.format_exc())\n continue\n except TransportError:\n log(\"ES TransportError!\" + traceback.format_exc())\n continue\n except NotFoundError:\n log(\"ES NotFoundError!\" + traceback.format_exc())\n continue\n except ConflictError:\n log(\"ES ConflictError!\" + traceback.format_exc())\n continue\n except RequestError:\n log(\"ES RequestError!\" + traceback.format_exc())\n continue\n except SerializationError:\n log(\"ES SerializationError!\" + traceback.format_exc())\n continue\n except ConnectionError:\n log(\"ES ConnectionError!\" + traceback.format_exc())\n continue\n except Exception:\n log(\"ES Exception!\" + traceback.format_exc())\n continue\n finally:\n log(\"Total number of ES write attempts: \" + str(retries + 1))\n #Exit for loop if ES transaction is successful otherwise pick another node and continue retrying\n break\n\n if response is None or response == '':\n return 'false'\n else:\n return 'true'","def test_create_index_fail_test(tcex: TcEx, monkeypatch: MonkeyPatch):\n\n # monkeypatch method\n def mp_post(*args, **kwargs): # pylint: disable=unused-argument\n return MockPost({}, ok=False)\n\n monkeypatch.setattr(tcex.session.tc, 'post', mp_post)\n\n # create index\n key = str(uuid.uuid4())\n try:\n tcex.api.tc.v2.datastore('local', key)\n assert False, 'Failed to catch error on ok=False'\n except RuntimeError:\n assert True","def test_search(self):\n from rubber import resource\n requestmock = RequestMock()\n resource.requests = requestmock\n\n q = {'query': {'term': {'user': 'kimchy'}}}\n self.Article.elasticsearch.search(q, toto='titi')\n\n self.assertEquals(1, len(requestmock.stack))\n self.assertEquals('http://example.com:9200/tests/article/_search', requestmock.stack[0]['url'])\n self.assertEquals('GET', requestmock.stack[0]['method'])\n self.assertEquals('titi', requestmock.stack[0]['kwargs']['toto'])\n from rubber.instanceutils import data_to_json\n self.assertEquals(data_to_json(q), requestmock.stack[0]['kwargs']['data'])\n\n self.Article.elasticsearch.mapping.put({'some': 'mapping'}, toto='titi')\n\n self.assertEquals(2, len(requestmock.stack))\n self.assertEquals('http://example.com:9200/tests/article/_mapping', requestmock.stack[1]['url'])\n self.assertEquals('PUT', requestmock.stack[1]['method'])\n self.assertEquals('titi', requestmock.stack[1]['kwargs']['toto'])","def test_index(self):\n es = elasticsearch.ElasticSearch(server='8.8.8.8',\n user='alice',\n password='iLoveDogs',\n doc_type='someLogCategory')\n\n index = es.index\n expected = time.strftime('logs-%Y.%m.%d')\n\n self.assertEqual(index, expected)","def setUp(self):\n db.drop_all() # clean up the last tests\n db.create_all() # make our sqlalchemy tables\n self.app = app.test_client()\n initialize_logging(logging.CRITICAL)","def build_index():\n print \"building index..\"\n\n index_dir = INDEX_DIR_CODE\n if TEST_COLLECTION:\n index_dir = INDEX_DIR_TEST\n CR_DOCS_DB.drop()\n CR_DOCS_DB.ensure_index(\"code_id\", unique=True)\n if os.path.exists(index_dir):\n shutil.rmtree(index_dir)\n os.mkdir(index_dir)\n schema = get_schema()\n storage = FileStorage(index_dir)\n ix = storage.create_index(schema)\n w = ix.writer()\n print \"finding posts..\"\n posts_with_code = POSTS_DB.find({\"answers.Body\": {\"$regex\": \"/.*<code>.*/\"}}, timeout=False)\n print \"adding files..\"\n q = add_from_file(w) if TEST_COLLECTION else 0\n for i, question in enumerate(posts_with_code):\n if TEST_COLLECTION:\n q += add_one_code(w, question, q)\n if q > 999:\n break\n else:\n q += add_doc(w, question)\n if i % 1000 == 0 and not i == 0:\n print \"commit number:\", str(i/1000), \"with\", q, \"codes\"\n w.commit()\n w = ix.writer()\n\n w.commit()\n posts_with_code.close()\n print \"the index was built!\"\n return ix","def _ensure_es_index(self, index):\n if not self.elasticsearch.indices.exists(index):\n try:\n self.elasticsearch.indices.create(index=index)\n except TransportError as error_msg:\n self.logger.error(str(error_msg.error))\n return False\n self.logger.info('Created Index: %s', index)\n\n return True","def _set_es_workers(self, **kwargs):\n def make_es_worker(search_conn, es_index, es_doc_type, class_name):\n \"\"\"\n Returns a new es_worker instance\n\n args:\n -----\n search_conn: the connection to elasticsearch\n es_index: the name of the elasticsearch index\n es_doc_type: the name of the elasticsearch doctype\n class_name: name of the rdf class that is being indexed\n \"\"\"\n new_esbase = copy.copy(search_conn)\n new_esbase.es_index = es_index\n new_esbase.doc_type = es_doc_type\n log.info(\"Indexing '%s' into ES index '%s' doctype '%s'\",\n class_name.pyuri,\n es_index,\n es_doc_type)\n return new_esbase\n\n def additional_indexers(rdf_class):\n \"\"\"\n returns additional classes to index based off of the es definitions\n \"\"\"\n rtn_list = rdf_class.es_indexers()\n rtn_list.remove(rdf_class)\n return rtn_list\n\n\n self.es_worker = make_es_worker(self.search_conn,\n self.es_index,\n self.es_doc_type,\n self.rdf_class.__name__)\n if not kwargs.get(\"idx_only_base\"):\n self.other_indexers = {item.__name__: make_es_worker(\n self.search_conn,\n item.es_defs.get('kds_esIndex')[0],\n item.es_defs.get('kds_esDocType')[0],\n item.__name__)\n for item in additional_indexers(self.rdf_class)}\n else:\n self.other_indexers = {}","def indexCreated(self, *args, **kwargs): # real signature unknown\n pass","def build_index(self):\n\t\tix = self.create_index()\n\t\twriter = AsyncWriter(ix)\n\n\t\tfor i, document in enumerate(self.documents):\n\t\t\tif document:\n\t\t\t\twriter.add_document(**document)\n\t\t\tupdate_progress_bar(\"Building Index\", i, len(self.documents))\n\n\t\twriter.commit(optimize=True)","def es_connection_setUp(es_server):\n datalab_logger_connections.info(\"ES : setUp connection\")\n return Elasticsearch(es_server, timeout=30, ignore=[400, 404], maxsize=100)","def __init__(self, index, document_id, client=None):\n self.index = index\n self.document_id = document_id\n self.client = client or current_search_client","def __init__(self, index_name, doc_type='string'):\n super(ESMigration, self).__init__()\n self.index_name = index_name\n self.doc_type = doc_type","def create_indexes(self) -> None:\n self.collection.create_index(\"traceId\")\n self.collection.create_index(\"process.serviceName\")","def setUp(self):\n\n #Get Flask test client\n self.client = app.test_client\n #Show errors from Flask than happen\n app.config['TESTING'] = True\n #Connect to test database\n connect_to_db(app, \"postgresql:///testdb\")\n #Create tables and add sample data to them\n db.create_all()\n example_data()","def indexCreationStarted(self, *args, **kwargs): # real signature unknown\n pass","def create_index(self):\n\n indice = client.IndicesClient(self.es)\n\n if not indice.exists(self.es_main_index):\n indice.create(\n index=self.es_main_index\n )\n\n return True","def handle(self, *args, **options):\n self.create_indices()\n self.bulk()","def test_recreate_index_that_exists(self):\n indices = self.elasticsearch_cls().indices\n indices.exists.return_value = True\n\n index_name = 'abcd'\n self.client._recreate_index(index_name)\n indices.delete.assert_called_once_with(index_name)\n indices.create.assert_called_once_with(index_name)","def insert_index(self):\n pass","def create_index(es_object, index_name):\n created = False\n # index settings\n # the reason why we need mappings is avoid corrupting your data\n settings = {\n \"settings\": {\n \"number_of_shards\": 1,\n \"number_of_replicas\": 0\n },\n \"mappings\": {\n # custom type called foods\n \"foods\": {\n \"properties\": {\n # Specify that the food_name field contains text values.\n \"food_name\": {\n \"type\": \"text\",\n \"fields\": {\n \"raw\":{ \n \"type\": \"keyword\"\n } # The food_name.raw field can be used for sorting and aggregations\n }\n },\n # Specify that the categories field contains text values.\n \"categories\": {\n \"type\": \"text\",\n \"fields\": {\n \"raw\":{ \n \"type\": \"keyword\"\n } # The categories.raw field can be used for sorting and aggregations\n }\n },\n # Specify that the calories field contains integer values.\n \"calories\": {\n \"type\": \"integer\"\n },\n \"protein\": {\n \"type\": \"integer\"\n },\n \"carbs\": {\n \"type\": \"integer\"\n },\n \"fat\": {\n \"type\": \"integer\"\n }\n \n },\n }\n }\n }\n try:\n if not es_object.indices.exists(index_name):\n # Ignore 400 means to ignore \"Index Already Exist\" error.\n es_object.indices.create(index=index_name, ignore=400, body=settings)\n print('Created Index')\n created = True\n except Exception as ex:\n print(str(ex))\n finally:\n return created","def __init__(self, conn_str=ELASTICSEARCH_CONN):\n self.es = Elasticsearch(conn_str)","def _es_push_indexes(self, content):\n for c in self.es_clients:\n c.create_index(content)","def setUp(self):\n\n # Get the Flask test client\n self.client = app.test_client()\n app.config['TESTING'] = True\n\n # Connect to test database\n connect_to_db(app, \"postgresql:///testdb\")\n\n # Create tables and add sample data\n db.create_all()\n example_data()","def __init__(self, create_index=True, online=True):\n self.online = online\n index_exists = self.index_exists()\n if create_index and not index_exists:\n self.create_index()","def test_post_foods_search(self):\n pass","def index_all(self, index_name):\n oks = 0\n notoks = 0\n for ok, item in streaming_bulk(\n self.es_client,\n self._iter_documents(index_name)\n ):\n if ok:\n oks += 1\n else:\n notoks += 1\n logging.info(\n \"Import results: %d ok, %d not ok\",\n oks,\n notoks\n )","def _create_indexes(self):\r\n # WARNING: The collection will be locked during the index\r\n # creation. If the collection has a large number of\r\n # documents in it, the operation can take a long time.\r\n\r\n # TODO: The creation of indexes can be moved to a Django\r\n # management command or equivalent. There is also an option to\r\n # run the indexing on the background, without locking.\r\n self.collection.ensure_index([('time', pymongo.DESCENDING)])\r\n self.collection.ensure_index('event_type')","def test_create_index_is_called(self):\r\n assert len(self.index_calls) == 0\r\n\r\n connection._index_all_fields = False\r\n \r\n class TestIndexCreationCallTestVertex(Vertex):\r\n col1 = properties.Text(index=True)\r\n col2 = properties.Text(index=True, db_field='____column')\r\n col3 = properties.Text(db_field='____column3')\r\n\r\n assert len(self.index_calls) == 2\r\n assert 'vid' not in self.index_calls\r\n assert 'col1' in self.index_calls\r\n assert '____column' in self.index_calls\r\n assert '____column3' not in self.index_calls\r\n\r\n connection._index_all_fields = True\r\n self.index_calls = []\r\n\r\n class TestIndexCreationCallTestVertex2(Vertex):\r\n col1 = properties.Text()\r\n col2 = properties.Text(db_field='____column')\r\n\r\n assert len(self.index_calls) == 3\r\n assert 'vid' in self.index_calls\r\n assert 'col1' in self.index_calls\r\n assert '____column' in self.index_calls","def indexable_objects_iter(docs, es_index, es_doctype):\n\n for doc in docs:\n insert_doc = {\n '_index': es_index,\n '_type': es_doctype,\n '_id': \"%s-%s\" % (doc['state'], doc['id']),\n '_source': doc\n }\n yield insert_doc","def setUp(self):\r\n self.output_dir = '/tmp/'\r\n\r\n otu_table_vals = array([[0, 0], [1, 5]])\r\n\r\n self.otu_table = table_factory(otu_table_vals,\r\n ['Sample1', 'Sample2'],\r\n ['OTU1', 'OTU2'],\r\n [None, None],\r\n [{\"taxonomy\": [\"Bacteria\"]},\r\n {\"taxonomy\": [\"Archaea\"]}])\r\n\r\n filt_otu_table_vals = array([[1, 5]])\r\n\r\n self.filt_otu_table = table_factory(filt_otu_table_vals,\r\n ['Sample1', 'Sample2'],\r\n ['OTU2'],\r\n [None, None],\r\n [{\"taxonomy\": [\"Archaea\"]}])\r\n\r\n self.num_otu_hits = 5\r\n self._folders_to_cleanup = []","def __init__(self, index, data_type):\n self._datastore = ElasticsearchDataStore(\n host=current_app.config['ELASTIC_HOST'],\n port=current_app.config['ELASTIC_PORT'])\n self._config = SimilarityScorerConfig(index, data_type)","def setUpClass(cls):\n\n cls.client = TestClient(fastapi_app.app)\n log.info('Completed initialization for FastAPI based REST API tests')","def _init_es(self):\n es = Elasticsearch([{'host': elastic_conf['host'], 'port': elastic_conf['port']}])\n print('Connected to Elastic Search:', es.ping())\n return es","def test_documents_for(self):\n # Test the default ES version\n self._test_documents_for(_documents_for)\n\n # Test the DB version\n self._test_documents_for(_db_documents_for)","def setUp(self):\n db.drop_all() # clean up the last tests\n db.create_all() # create new tables\n self.app = app.test_client()","def initialize_indexer_backend(self):\n pass","def test_create_index(self, collection):\n collection.create_index(\"hello\")\n assert collection._indexes == {\"_id_\": ((\"_id\",), {(1,)})}\n\n collection.create_index(\"hello\", unique=True)\n assert collection._indexes == {\n \"_id_\": ((\"_id\",), {(1,)}),\n \"hello_1\": ((\"hello\",), {(\"there\",)}),\n }","def setUp(self):\n db.drop_all() # clean up the last tests\n db.create_all() # create new tables\n self.app = app.test_client()","def test_load_index(self):\n\n c = Client()\n response = c.get('/taric_books/')\n\n self.assertEqual(response.status_code, 200)","def test_index_table(self, mapping_cls, bulk_mock):\n indices = self.elasticsearch_cls().indices\n mapping = mapping_cls()\n rows = [\n {'id': 1, 'number': '123456789'},\n {'id': 2, 'number': '234567890'},\n {'id': 3, 'number': '345678901'},\n ]\n bulk_mock.return_value = (len(rows), [])\n\n db_path = 'some path'\n table_name = 'calls'\n table_reader = Mock()\n table_reader.rows.return_value = rows\n table_reader.database.db_filename = db_path\n table_reader.table.name = table_name\n documents_indexed = self.client._index_table(table_reader)\n indices.put_mapping.assert_called_once_with(\n index=self.client.INDEX_NAME,\n doc_type=hashlib.md5(\n '{}:{}'.format(db_path, table_name)).hexdigest(),\n body=mapping.mapping)\n self.assertEqual(documents_indexed, len(rows))","def __create_new_index(self, index, index_id, begin_timestamp):\n index_name = index + \"_\" + str(index_id)\n target_config = index + \".target_config\"\n pipeline_id = index + \".pipeline\"\n mapping = {\n \"user_id\": {\"type\": \"long\"},\n \"client_id\": {\"type\": \"long\"},\n \"partner_id\": {\"type\": \"long\"},\n \"module\": {\"type\": \"keyword\"},\n \"page\": {\"type\": \"keyword\"},\n \"uri\": {\"type\": \"keyword\"},\n \"app_type\": {\"type\": \"keyword\"},\n \"created_at\": {\"type\": \"date\"},\n \"request_time\": {\"type\": \"date\"},\n \"duration\": {\"type\": \"long\"},\n }\n body = {\n \"settings\": {\n \"number_of_shards\": 1,\n \"number_of_replicas\": 1,\n },\n \"mappings\": {\n \"docs\": {\n \"properties\": mapping\n }\n },\n }\n self.client.indices.create(index=index_name, body=body)\n self.client.index(\n index=target_config,\n doc_type=\"config\",\n id=index_id,\n body={\"index_id\": index_id, \"begin_timestamp\": begin_timestamp},\n )\n\n return index_id","def test_model_object_index(self):\n car = Car.objects.first()\n car.index_to_appsearch()\n self.assertEqual(self.client_index.call_count, 1)"],"string":"[\n \"def setUp(self):\\n body = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 0\\n }\\n }\\n self.assertTrue(self.es.create_index('contacts_esclient_test', body))\\n self.assertFalse(self.es.create_index('contacts_esclient_test', body))\\n\\n self.assertTrue(self.es.create_index('contacts_esclient_test2', body))\\n self.assertFalse(self.es.create_index('contacts_esclient_test2', body))\\n\\n\\n \\\"\\\"\\\" Index some test data \\\"\\\"\\\"\\n data = {\\\"name\\\": \\\"Joe Tester\\\",\\\"age\\\": 21, \\\"sex\\\": \\\"male\\\"}\\n self.assertTrue(self.es.index(\\\"contacts_esclient_test\\\", \\\"person\\\", body=data,\\n docid=1))\\n data = {\\\"name\\\": \\\"Joe Schmoe\\\",\\\"age\\\": 17, \\\"sex\\\": \\\"male\\\"}\\n self.assertTrue(self.es.index(\\\"contacts_esclient_test\\\", \\\"person\\\", body=data,\\n docid=2))\\n\\n self.assertTrue(self.es.refresh('contacts_esclient_test'))\",\n \"def setup(self):\\n collection = self._get_collection()\\n\\n indices = copy(self.params[\\\"indices\\\"])\\n\\n if \\\"when\\\" not in indices:\\n indices[\\\"when\\\"] = {}\\n\\n for index in indices:\\n self.log(DEBUG, \\\"Ensuring we have index for {}\\\".format(index))\\n\\n options = indices[index]\\n collection.create_index(index, *options)\\n self.log(DEBUG, \\\"Done.\\\")\",\n \"def init(self):\\n self._es.create_index_template(\\n name=DATASETS_INDEX_NAME,\\n template=DATASETS_INDEX_TEMPLATE,\\n force_recreate=True,\\n )\\n self._es.create_index(DATASETS_INDEX_NAME)\",\n \"def set_up(self):\\n # This test suite IGNORES datastore inconsistency because it is only\\n # interested in what content eventually gets into the search index.\\n #\\n # Consequently, this suite should only contain tests where this\\n # assumption is both valid and required. Any other test should use the\\n # default PopulatedTest Case, which assumes maximum inconsistency.\\n self.consistency_probability = 1\\n super(ConsistentSearchTest, self).set_up()\\n\\n # We'll want ready access to a Cron instance and the content search\\n # index.\\n self.cron = Cron(self.admin_api)\\n indexer = Indexer.get_or_insert('the-indexer')\\n indexer.delete_all_content() # fresh start, no pre-populated stuff\\n self.search_index = indexer.get_index()\",\n \"def setUp(self):\\n self.es = Elasticsearch(hosts=[LOCAL_ELASTICSEARCH])\\n try:\\n self.es.indices.delete(index='packages')\\n except NotFoundError:\\n pass\\n self.runner = CliRunner()\\n self.dbfilename = 'test.db'\\n config._set_connection_string('sqlite:///test.db')\",\n \"def test_init(self):\\n es = elasticsearch.ElasticSearch(server='8.8.8.8',\\n user='alice',\\n password='iLoveDogs',\\n doc_type='someLogCategory')\\n\\n self.assertTrue(isinstance(es, elasticsearch.ElasticSearch))\",\n \"def create_indices():\\n destroy_indices()\\n\\n ActionDocument._index.create(ignore=[400, 404])\\n ClassificationDocument._index.create(ignore=[400, 404])\\n FunctionDocument._index.create(ignore=[400, 404])\\n PhaseDocument._index.create(ignore=[400, 404])\\n RecordDocument._index.create(ignore=[400, 404])\\n\\n yield\\n\\n destroy_indices()\",\n \"def build(self):\\n\\t\\tself.documents = self.get_items_to_index()\\n\\t\\tself.build_index()\",\n \"def setUp(self):\\n self.patcher = patch('esis.es.Elasticsearch')\\n self.elasticsearch_cls = self.patcher.start()\\n self.client = Client(host='localhost', port=9200)\",\n \"def store_index(self, index, doc_type, source_list, init_id):\\n\\n bulk_actions = []\\n doc_id = init_id\\n\\n for source in source_list:\\n data_body = ElasticSearchUtility.__index_data_body(index, doc_type, doc_id, source[\\\"_source\\\"])\\n bulk_actions.append(data_body)\\n doc_id += 1\\n\\n print 'inserting - ', len(bulk_actions)\\n helpers.bulk(self.es, bulk_actions)\",\n \"def prepare_environment():\\n elastic_search = Elasticsearch('{}:{}'.format(\\n _CONFIG.elastic.elastic_hostname,\\n _CONFIG.elastic.elastic_port))\\n try:\\n if not elastic_search.indices.exists(_CONFIG.elastic.elastic_index):\\n elastic_search.indices.create(\\n index=_CONFIG.elastic.elastic_index,\\n body=_CONFIG.elastic.metadata_index_setup)\\n except ConnectionError:\\n sys.exit(\\\"Can't start because of no connection to ElasticSearch.\\\")\",\n \"def test_index(self):\",\n \"def create_indices(self) -> None:\\n self.client.indices.create(\\n index=\\\"business\\\",\\n body=BUSINESS_MAPPINGS\\n )\\n self.client.indices.create(\\n index=\\\"review\\\",\\n body=REVIEW_MAPPINGS\\n )\\n self.client.indices.create(\\n index=\\\"tip\\\",\\n body=TIP_MAPPINGS\\n )\",\n \"def create_index(self):\\n self.send_robust(self.es_index, data=self.es_meta)\\n self.set_index_normal_settings()\",\n \"def test_creating_index_type(self):\",\n \"def create_index():\",\n \"def setUp(self):\\n self.twitter = Twitter(CUR_DIR + \\\"/test_crossfit.tweets\\\", CUR_DIR + \\\"/test_stop_words.txt\\\")\\n self.twitter.load_tweets_and_build_index()\\n\\n self.searcher = Searcher(self.twitter.tweets, self.twitter.stop_words)\",\n \"def test_export_index(self):\",\n \"def test_add_index_settings(self):\\n response = self.post('base:add_index', self.form_data(), **self.slugs())\\n assert 'base/project.html' in response.template_name\\n\\n response = self.get('base:index_settings', **self.index_slugs())\\n assert 'base/index_update_form.html' in response.template_name\",\n \"def test_queryset_index(self):\\n car = Car.objects.all()\\n car.index_to_appsearch()\\n # Note that the app search chunk size is set to 5 in `tests.settings`\\n # Therefore you should see 5 calls to cover 22 documents\\n self.assertEqual(self.client_index.call_count, 5)\",\n \"def setUp(self):\\r\\n super(TestCourseIndex, self).setUp()\\r\\n # had a problem where index showed course but has_access failed to retrieve it for non-staff\\r\\n self.odd_course = CourseFactory.create(\\r\\n org='test.org_1-2',\\r\\n number='test-2.3_course',\\r\\n display_name='dotted.course.name-2',\\r\\n )\",\n \"def create_index(self):\\r\\n\\r\\n #### Begin functionality here\\r\\n\\r\\n return()\",\n \"def es():\\n # pylint: disable=invalid-name\\n\\n client = Elasticsearch(ES_TEST_HOSTS)\\n client.indices.create(index=ES_TEST_INDEX)\\n yield client\\n client.indices.delete(index=ES_TEST_INDEX)\",\n \"def test_index_containers(self):\\n pass\",\n \"def build_index():\\n pass\",\n \"def test_elasticsearch(self):\\n\\n full_log_path = get_full_test_logs_path(cname=self)\\n log.info(f\\\"Logs file path name is : {full_log_path}\\\")\\n log.info(\\\"The ElasticSearch deployment test started.\\\")\\n if self.es.get_health():\\n log.info(\\\"The Status of the elasticsearch is OK\\\")\\n else:\\n log.warning(\\\"The Status of the elasticsearch is Not OK\\\")\\n log.info(\\\"Waiting another 30 sec.\\\")\\n time.sleep(30)\\n if self.es.get_health():\\n log.info(\\\"The Status of the elasticsearch is OK\\\")\\n else:\\n log.error(\\\"The Status of the elasticsearch is Not OK ! Exiting.\\\")\\n\\n if self.es.get_health():\\n log.info(\\\"\\\\nThe Elastic-Search server information :\\\\n\\\")\\n log.info(f\\\"The Elasticsearch IP is {self.es.get_ip()}\\\")\\n log.info(f\\\"The Elasticsearch port is {self.es.get_port()}\\\")\\n log.info(f\\\"The Password to connect is {self.es.get_password()}\\\")\\n\\n else:\\n assert False, \\\"The Elasticsearch module is not ready !\\\"\\n\\n log.info(f\\\"Test UUDI is : {self.smallfile_run(self.es)}\\\")\\n\\n assert self.es.dumping_all_data(full_log_path), \\\"Can not Retrieve the test data\\\"\\n\\n assert run_command(\\n f\\\"ls {full_log_path}/FullResults.tgz\\\"\\n ), \\\"Results file did not retrieve from pod\\\"\\n\\n try:\\n main_es = Elasticsearch(\\n [\\n {\\n \\\"host\\\": defaults.ELASTICSEARCH_DEV_IP,\\n \\\"port\\\": defaults.ELASTICSEARCE_PORT,\\n \\\"url\\\": f\\\"http://{defaults.ELASTICSEARCH_DEV_IP}:{defaults.ELASTICSEARCE_PORT}\\\",\\n }\\n ]\\n )\\n except esexp.ConnectionError:\\n log.warning(\\\"Cannot connect to ES server in the LocalServer\\\")\\n main_es = None\\n assert elasticsearch_load(\\n main_es, full_log_path\\n ), \\\"Can not load data into Main ES server\\\"\",\n \"def _SetupIndexes(self, _open=open):\\n pass\",\n \"def setup(self):\\n self.create_songs_index()\\n self.create_fingerprints_index()\\n self.delete_unfingerprinted()\",\n \"def init_index(clear=False):\\n return _run_indexer_func(\\\"init_index\\\", clear)\",\n \"def __init__(self):\\n super().__init__()\\n self.index_dir = self.base_dir + \\\"ticket/\\\"\\n self.index_schema = self.__get_index_schema()\\n if not os.path.exists(self.index_dir):\\n os.makedirs(self.index_dir)\\n self.indexer = index.create_in(self.index_dir, self.index_schema)\\n else:\\n self.indexer = index.open_dir(self.index_dir)\",\n \"def build_index(self):\\n self.rebuild_index()\",\n \"def create(\\n self,\\n index: IO,\\n request_options: Optional[_models.RequestOptions] = None,\\n *,\\n content_type: str = \\\"application/json\\\",\\n **kwargs: Any\\n ) -> _models.SearchIndex:\",\n \"def __init__(self):\\n super().__init__()\\n self.index_dir = self.base_dir + \\\"user/\\\"\\n self.index_schema = self.__get_index_schema()\\n if not os.path.exists(self.index_dir):\\n os.makedirs(self.index_dir)\\n self.indexer = index.create_in(self.index_dir, self.index_schema) # creates the index\\n else:\\n self.indexer = index.open_dir(self.index_dir) # opens the index if it already exists\",\n \"def create_index():\\n es = Elasticsearch()\\n es.indices.delete(index='drugaid-index')\\n with open('../data_crawling/demo_drug_data-fixed.json') as f:\\n drug_data = json.load(f)\\n\\n for drug in drug_data:\\n es.index(index='drugaid-index', doc_type='drug', body=drug)\",\n \"def init_index(self):\\n raise NotImplementedError\",\n \"def setup_mapping():\\n # Mapping describes how elasticsearch handles a document during indexing.\\n # Most fields are detected and mapped automatically.\\n m = {\\n # Turn off analysis on name so we can sort by it.\\n 'name': {'index': 'not_analyzed', 'type': 'string'},\\n }\\n es = elasticutils.get_es()\\n try:\\n es.create_index_if_missing(settings.ES_INDEX)\\n es.put_mapping(Addon._meta.app_label, {'properties': m},\\n settings.ES_INDEX)\\n except pyes.ElasticSearchException:\\n pass\",\n \"def setUp(self):\\n super(IndexViewsTestCase, self).setUp()\\n params = POST1_PARAMS.copy()\\n params['blog'] = self.test_blog\\n params['author'] = self.test_user1.author\\n self.add_post(params)\",\n \"def create(\\n self,\\n index: _models.SearchIndex,\\n request_options: Optional[_models.RequestOptions] = None,\\n *,\\n content_type: str = \\\"application/json\\\",\\n **kwargs: Any\\n ) -> _models.SearchIndex:\",\n \"def __init__(\\n self,\\n host: str = \\\"localhost\\\",\\n port: int = 9200,\\n username: str = \\\"\\\",\\n password: str = \\\"\\\",\\n index: str = \\\"document\\\",\\n search_fields: Union[str, list] = \\\"text\\\",\\n text_field: str = \\\"text\\\",\\n name_field: str = \\\"name\\\",\\n external_source_id_field: str = \\\"external_source_id\\\",\\n embedding_field: Optional[str] = None,\\n embedding_dim: Optional[int] = None,\\n custom_mapping: Optional[dict] = None,\\n excluded_meta_data: Optional[list] = None,\\n faq_question_field: Optional[str] = None,\\n scheme: str = \\\"http\\\",\\n ca_certs: bool = False,\\n verify_certs: bool = True,\\n create_index: bool = True\\n ):\\n self.client = Elasticsearch(hosts=[{\\\"host\\\": host, \\\"port\\\": port}], http_auth=(username, password),\\n scheme=scheme, ca_certs=ca_certs, verify_certs=verify_certs)\\n\\n # if no custom_mapping is supplied, use the default mapping\\n if not custom_mapping:\\n custom_mapping = {\\n \\\"mappings\\\": {\\n \\\"properties\\\": {\\n name_field: {\\\"type\\\": \\\"text\\\"},\\n text_field: {\\\"type\\\": \\\"text\\\"},\\n external_source_id_field: {\\\"type\\\": \\\"text\\\"},\\n }\\n }\\n }\\n if embedding_field:\\n custom_mapping[\\\"mappings\\\"][\\\"properties\\\"][embedding_field] = {\\\"type\\\": \\\"dense_vector\\\",\\n \\\"dims\\\": embedding_dim}\\n # create an index if not exists\\n if create_index:\\n self.client.indices.create(index=index, ignore=400, body=custom_mapping)\\n self.index = index\\n\\n # configure mappings to ES fields that will be used for querying / displaying results\\n if type(search_fields) == str:\\n search_fields = [search_fields]\\n\\n #TODO we should implement a more flexible interal mapping here that simplifies the usage of additional,\\n # custom fields (e.g. meta data you want to return)\\n self.search_fields = search_fields\\n self.text_field = text_field\\n self.name_field = name_field\\n self.external_source_id_field = external_source_id_field\\n self.embedding_field = embedding_field\\n self.excluded_meta_data = excluded_meta_data\\n self.faq_question_field = faq_question_field\",\n \"def setUp(self):\\n self.docCounter = 0\",\n \"def _initIndexes(self):\\n class Record:\\n \\\"\\\"\\\" a moron simple object for carrying the 'extra'-payload to index\\n constructors\\n \\\"\\\"\\\"\\n def __init__(self, **kw):\\n self.__dict__.update(kw)\\n\\n addIndex = self.addIndex\\n addColumn = self.addColumn\\n\\n # Content indexes\\n self._catalog.indexes.clear()\\n for (index_name, index_type, extra) in self.enumerateIndexes():\\n if extra is None:\\n addIndex( index_name, index_type)\\n else:\\n if isinstance(extra, StringTypes):\\n p = Record(indexed_attrs=extra)\\n elif isinstance(extra, DictType):\\n p = Record(**extra)\\n else:\\n p = Record()\\n addIndex( index_name, index_type, extra=p )\\n\\n # Cached metadata\\n self._catalog.names = ()\\n self._catalog.schema.clear()\\n for column_name in self.enumerateColumns():\\n addColumn( column_name )\",\n \"def bulk_push_to_elastic(elastic_search_url, index, docs):\\n CREATE_TEMPLATE = {\\\"create\\\": {\\\"_index\\\": index, \\\"_type\\\": \\\"_doc\\\", \\\"_id\\\": \\\"\\\"}}\\n\\n bulk_request_body = \\\"\\\"\\n for doc in docs:\\n CREATE_TEMPLATE[\\\"create\\\"][\\\"_id\\\"] = doc[HASH_ID]\\n bulk_request_body += json.dumps(CREATE_TEMPLATE) + NEW_LINE\\n bulk_request_body += json.dumps(doc) + NEW_LINE\\n\\n # Request\\n headers = {\\\"content-type\\\": \\\"application/x-ndjson\\\"}\\n\\n url = elastic_search_url + \\\"/\\\" + \\\"_bulk\\\"\\n\\n response = requests.post(url, data=bulk_request_body, headers=headers)\\n return response\",\n \"def setup_index_shards_per_node(cluster: str, index: str, number: int):\\n\\n elastic = sreElastic(host=cluster)\\n elastic.set_number_shards_per_node(index=index, number=number)\",\n \"def setup_search(self):\\n if self.testing:\\n return MockExternalSearchIndex()\\n else:\\n search = ExternalSearchIndex(self._db)\\n if not search:\\n self.log.warning(\\\"No external search server configured.\\\")\\n return None\\n return search\",\n \"def es(request):\\n from django.conf import settings\\n from search.index import get_elasticsearch\\n from search.search import get_indices_alias\\n\\n # Clear lru_cache of Elasticsearch indices.\\n get_indices_alias.cache_clear()\\n\\n # Test setup\\n xdist_suffix = getattr(\\n request.config, 'slaveinput', {}\\n ).get('slaveid', 'es_test_index')\\n es_prefix = f'{settings.ES_INDEX_PREFIX}{xdist_suffix}'\\n setattr(settings, 'ES_INDEX_PREFIX', es_prefix)\\n\\n # Actual test\\n yield\\n\\n # Test teardown\\n get_elasticsearch().indices.delete(index=f'{es_prefix}*')\",\n \"def test_add_documents_empty(empty_index):\\n index = empty_index()\\n with pytest.raises(Exception) as e_info:\\n index.add_documents(\\\"\\\")\\n assert e_info.value.code == \\\"missing_payload\\\"\\n assert e_info.value.type == \\\"invalid_request\\\"\",\n \"def indexDocsToES(indexName):\\n docs = pd.DataFrame()\\n try:\\n docs = pd.read_csv(\\\"documents/full_news_documents.csv\\\")\\n except FileNotFoundError:\\n print(\\\"File not found\\\")\\n\\n use_these_keys = ['id', 'category', 'headline', 'authors', 'link', 'short_description', 'date', 'body']\\n helpers.bulk(es_client, doc_generator(docs, indexName, use_these_keys))\",\n \"def test_get_documents_populated(index_with_documents):\\n response = index_with_documents().get_documents()\\n assert isinstance(response.results, list)\\n assert len(response.results) == 20\",\n \"def test_search(self):\\n self.elasticsearch_cls().search.return_value = {\\n 'hits': {\\n 'total': 10,\\n 'hits': [1, 2, 3, 4],\\n },\\n '_scroll_id': 'abcd'\\n }\\n self.elasticsearch_cls().scroll.side_effect = [\\n {'hits': {'hits': [5, 6, 7]}},\\n {'hits': {'hits': [8, 9, 10]}},\\n {'hits': {'hits': []}},\\n ]\\n\\n query = 'this is the query'\\n hits = list(self.client.search(query))\\n\\n self.assertListEqual(\\n hits,\\n [[1, 2, 3, 4], [5, 6, 7], [8, 9, 10]])\",\n \"def main():\\n try:\\n res = requests.get('http://localhost:9200')\\n pprint(json.loads(res.content.decode('utf-8')))\\n except requests.exceptions.ConnectionError:\\n print(\\\"ERROR: ELASTICSEARCH Server is not running!\\\")\\n exit(-1)\\n\\n # scrapeAndSaveNewsArticles()\\n # generateNewsDocsCSV() # may need to be modified based on how scrapeAndSave function file output\\n if not es_client.indices.exists(index='huffpost_news_index'):\\n print(\\\"PLEASE WAIT... LOADING DOCUMENTS INTO INVERTED INDEX\\\")\\n indexDocsToES('huffpost_news_index')\",\n \"def setUp(self):\\n self.builder = SphinxBuilder()\\n\\n # set up a place for a fake sphinx project\\n self.twistedRootDir = FilePath(self.mktemp())\\n self.sphinxDir = self.twistedRootDir.child(\\\"docs\\\")\\n self.sphinxDir.makedirs()\\n self.sourceDir = self.sphinxDir\",\n \"def setUpTestData(cls) -> None:\\n\\n # Define base url\\n cls.url = BASE_URL + '/'\\n\\n # Make 9 \\\"normal\\\" authors.\\n cls.authors: typing.List[Author] = [\\n create_author() for _ in range(9)\\n ]\\n\\n # Make 1 superuser author.\\n cls.super_author: Author = create_author(True)\\n\\n # Serialize data once so that it's not called in ever test\\n cls.serialized_data = AuthorListSerializer(Author.objects.all(), many=True).data\",\n \"def setUp(self):\\r\\n\\r\\n User.query.delete()\\r\\n Article.query.delete()\\r\\n db.session.commit()\\r\\n\\r\\n u = User(\\r\\n username=\\\"testuser\\\",\\r\\n password=\\\"HASHED_PASSWORD\\\",\\r\\n location=\\\"US-VA\\\"\\r\\n )\\r\\n\\r\\n db.session.add(u)\\r\\n db.session.commit()\\r\\n\\r\\n a = Article(\\r\\n path='testPath',\\r\\n url='https://wwww.testurl.com',\\r\\n location=\\\"US\\\",\\r\\n title=\\\"testTitle\\\",\\r\\n excerpt=\\\"testExcerpt\\\",\\r\\n image=\\\"https://www.testurl.com/testimg.jpg\\\",\\r\\n source=\\\"testSource\\\",\\r\\n published_date=\\\"2020-04-25 05:06:00\\\",\\r\\n saved_by=u.id\\r\\n )\\r\\n\\r\\n db.session.add(a)\\r\\n db.session.commit()\\r\\n\\r\\n self.client = app.test_client()\\r\\n app.config['TESTING'] = True\\r\\n app.config['WTF_CSRF_ENABLED'] = False\",\n \"def test_index_stats(self):\\n #Create Index\\n self.run_multi_operations(buckets = self.buckets,\\n query_definitions = self.query_definitions,\\n create_index = True, drop_index = False)\\n #Check Index Stats\\n self.sleep(30)\\n index_map = self.get_index_stats()\\n self.log.info(index_map)\\n for query_definition in self.query_definitions:\\n index_name = query_definition.index_name\\n for bucket in self.buckets:\\n bucket_name = bucket.name\\n check_keys = ['items_count', 'total_scan_duration', 'num_docs_queued',\\n 'num_requests', 'num_rows_returned', 'num_docs_queued',\\n 'num_docs_pending','delete_bytes' ]\\n map = self._create_stats_map(items_count=2016)\\n self._verify_index_stats(index_map, index_name, bucket_name, map, check_keys)\",\n \"def build_index(self):\\n self.create_index()\\n logger.debug(f\\\"Building index with {self.n_trees} trees.\\\")\\n\\n for i in range(len(self.corpus_embeddings)):\\n self.index.add_item(i, self.corpus_embeddings[i])\\n self.index.build(self.n_trees)\",\n \"def _build_search(self, index, **kwargs):\\n startdate = kwargs.get('startdate', None)\\n if startdate:\\n timefield = kwargs.get('timefield')\\n enddate = kwargs.get('enddate', 'now')\\n filters = kwargs.get('filters', None)\\n exclude = kwargs.get('exclude', None)\\n ranges = kwargs.get('ranges', None)\\n fields_to_include = kwargs.get('field_to_include', None)\\n wildcards = kwargs.get('wildcard', None)\\n start_from = kwargs.get('from_', None)\\n size = kwargs.get('size', None)\\n sort_ = kwargs.get('sort', None)\\n\\n search = Search(using=self.es, index=index, doc_type=self.doc_type)\\\\\\n .params(request_timeout=2000)\\n\\n if startdate:\\n if startdate != enddate:\\n timeRange = {timefield: {'gte': startdate, 'lt': enddate}}\\n else:\\n timeRange = {timefield: {'gte': startdate, 'lte': enddate}}\\n search = search.filter('range', **timeRange)\\n if filters:\\n for key, val in filters.items():\\n search = search.filter('terms' if isinstance(val, list) else 'term', **{key: val})\\n if exclude:\\n for ex in exclude.keys():\\n search = search.exclude('terms', **{ex: exclude[ex]})\\n if ranges:\\n # ranges are expected in format:\\n # [{field:{'gte':value, 'lte':value}}, {field: {'gte': value}}, {field: {'lte': value}}]\\n for range_filter in ranges:\\n search = search.filter('range', **range_filter)\\n if fields_to_include:\\n for field in fields_to_include.keys():\\n search = search.source(**{field: fields_to_include[field]})\\n if wildcards:\\n for wild in wildcards:\\n search = search.filter('wildcard', **{wild: wildcards[wild]})\\n if start_from:\\n search = search.extra(**{\\\"from_\\\": start_from})\\n if size:\\n search = search.extra(**{\\\"size\\\": size})\\n if sort_:\\n search = search.sort(*sort_)\\n\\n self._logger.info(json.dumps(search.to_dict()))\\n\\n return search\",\n \"def test_track_index(self, collection):\\n collection.create_index(\\\"hello\\\", unique=True)\\n collection.insert_many([{\\\"hello\\\": \\\"here\\\"}, {\\\"hello\\\": 2}])\\n assert collection._indexes == {\\n \\\"_id_\\\": ((\\\"_id\\\",), {(1,), (2,), (3,)}),\\n \\\"hello_1\\\": ((\\\"hello\\\",), {(\\\"there\\\",), (\\\"here\\\",), (2,)}),\\n }\",\n \"def set_in_index(document, index, type):\\n response = None\\n\\n #Try 3 times to store the document in ES, each time picking a random ES node address in case of failure\\n for retries in range(3):\\n try:\\n log('ES Set Request :: ' + json.dumps(document) + ' : ' + index + ':' + type)\\n response = es.index(index=index, doc_type=type, id=document['id'], body=document)\\n log(\\\"ES Set Response :: \\\" + json.dumps(response))\\n except ImproperlyConfigured:\\n log(\\\"ES ImproperlyConfigured!\\\" + traceback.format_exc())\\n continue\\n except ElasticsearchException:\\n log(\\\"ES ElasticsearchException!\\\" + traceback.format_exc())\\n continue\\n except TransportError:\\n log(\\\"ES TransportError!\\\" + traceback.format_exc())\\n continue\\n except NotFoundError:\\n log(\\\"ES NotFoundError!\\\" + traceback.format_exc())\\n continue\\n except ConflictError:\\n log(\\\"ES ConflictError!\\\" + traceback.format_exc())\\n continue\\n except RequestError:\\n log(\\\"ES RequestError!\\\" + traceback.format_exc())\\n continue\\n except SerializationError:\\n log(\\\"ES SerializationError!\\\" + traceback.format_exc())\\n continue\\n except ConnectionError:\\n log(\\\"ES ConnectionError!\\\" + traceback.format_exc())\\n continue\\n except Exception:\\n log(\\\"ES Exception!\\\" + traceback.format_exc())\\n continue\\n finally:\\n log(\\\"Total number of ES write attempts: \\\" + str(retries + 1))\\n #Exit for loop if ES transaction is successful otherwise pick another node and continue retrying\\n break\\n\\n if response is None or response == '':\\n return 'false'\\n else:\\n return 'true'\",\n \"def test_create_index_fail_test(tcex: TcEx, monkeypatch: MonkeyPatch):\\n\\n # monkeypatch method\\n def mp_post(*args, **kwargs): # pylint: disable=unused-argument\\n return MockPost({}, ok=False)\\n\\n monkeypatch.setattr(tcex.session.tc, 'post', mp_post)\\n\\n # create index\\n key = str(uuid.uuid4())\\n try:\\n tcex.api.tc.v2.datastore('local', key)\\n assert False, 'Failed to catch error on ok=False'\\n except RuntimeError:\\n assert True\",\n \"def test_search(self):\\n from rubber import resource\\n requestmock = RequestMock()\\n resource.requests = requestmock\\n\\n q = {'query': {'term': {'user': 'kimchy'}}}\\n self.Article.elasticsearch.search(q, toto='titi')\\n\\n self.assertEquals(1, len(requestmock.stack))\\n self.assertEquals('http://example.com:9200/tests/article/_search', requestmock.stack[0]['url'])\\n self.assertEquals('GET', requestmock.stack[0]['method'])\\n self.assertEquals('titi', requestmock.stack[0]['kwargs']['toto'])\\n from rubber.instanceutils import data_to_json\\n self.assertEquals(data_to_json(q), requestmock.stack[0]['kwargs']['data'])\\n\\n self.Article.elasticsearch.mapping.put({'some': 'mapping'}, toto='titi')\\n\\n self.assertEquals(2, len(requestmock.stack))\\n self.assertEquals('http://example.com:9200/tests/article/_mapping', requestmock.stack[1]['url'])\\n self.assertEquals('PUT', requestmock.stack[1]['method'])\\n self.assertEquals('titi', requestmock.stack[1]['kwargs']['toto'])\",\n \"def test_index(self):\\n es = elasticsearch.ElasticSearch(server='8.8.8.8',\\n user='alice',\\n password='iLoveDogs',\\n doc_type='someLogCategory')\\n\\n index = es.index\\n expected = time.strftime('logs-%Y.%m.%d')\\n\\n self.assertEqual(index, expected)\",\n \"def setUp(self):\\n db.drop_all() # clean up the last tests\\n db.create_all() # make our sqlalchemy tables\\n self.app = app.test_client()\\n initialize_logging(logging.CRITICAL)\",\n \"def build_index():\\n print \\\"building index..\\\"\\n\\n index_dir = INDEX_DIR_CODE\\n if TEST_COLLECTION:\\n index_dir = INDEX_DIR_TEST\\n CR_DOCS_DB.drop()\\n CR_DOCS_DB.ensure_index(\\\"code_id\\\", unique=True)\\n if os.path.exists(index_dir):\\n shutil.rmtree(index_dir)\\n os.mkdir(index_dir)\\n schema = get_schema()\\n storage = FileStorage(index_dir)\\n ix = storage.create_index(schema)\\n w = ix.writer()\\n print \\\"finding posts..\\\"\\n posts_with_code = POSTS_DB.find({\\\"answers.Body\\\": {\\\"$regex\\\": \\\"/.*<code>.*/\\\"}}, timeout=False)\\n print \\\"adding files..\\\"\\n q = add_from_file(w) if TEST_COLLECTION else 0\\n for i, question in enumerate(posts_with_code):\\n if TEST_COLLECTION:\\n q += add_one_code(w, question, q)\\n if q > 999:\\n break\\n else:\\n q += add_doc(w, question)\\n if i % 1000 == 0 and not i == 0:\\n print \\\"commit number:\\\", str(i/1000), \\\"with\\\", q, \\\"codes\\\"\\n w.commit()\\n w = ix.writer()\\n\\n w.commit()\\n posts_with_code.close()\\n print \\\"the index was built!\\\"\\n return ix\",\n \"def _ensure_es_index(self, index):\\n if not self.elasticsearch.indices.exists(index):\\n try:\\n self.elasticsearch.indices.create(index=index)\\n except TransportError as error_msg:\\n self.logger.error(str(error_msg.error))\\n return False\\n self.logger.info('Created Index: %s', index)\\n\\n return True\",\n \"def _set_es_workers(self, **kwargs):\\n def make_es_worker(search_conn, es_index, es_doc_type, class_name):\\n \\\"\\\"\\\"\\n Returns a new es_worker instance\\n\\n args:\\n -----\\n search_conn: the connection to elasticsearch\\n es_index: the name of the elasticsearch index\\n es_doc_type: the name of the elasticsearch doctype\\n class_name: name of the rdf class that is being indexed\\n \\\"\\\"\\\"\\n new_esbase = copy.copy(search_conn)\\n new_esbase.es_index = es_index\\n new_esbase.doc_type = es_doc_type\\n log.info(\\\"Indexing '%s' into ES index '%s' doctype '%s'\\\",\\n class_name.pyuri,\\n es_index,\\n es_doc_type)\\n return new_esbase\\n\\n def additional_indexers(rdf_class):\\n \\\"\\\"\\\"\\n returns additional classes to index based off of the es definitions\\n \\\"\\\"\\\"\\n rtn_list = rdf_class.es_indexers()\\n rtn_list.remove(rdf_class)\\n return rtn_list\\n\\n\\n self.es_worker = make_es_worker(self.search_conn,\\n self.es_index,\\n self.es_doc_type,\\n self.rdf_class.__name__)\\n if not kwargs.get(\\\"idx_only_base\\\"):\\n self.other_indexers = {item.__name__: make_es_worker(\\n self.search_conn,\\n item.es_defs.get('kds_esIndex')[0],\\n item.es_defs.get('kds_esDocType')[0],\\n item.__name__)\\n for item in additional_indexers(self.rdf_class)}\\n else:\\n self.other_indexers = {}\",\n \"def indexCreated(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def build_index(self):\\n\\t\\tix = self.create_index()\\n\\t\\twriter = AsyncWriter(ix)\\n\\n\\t\\tfor i, document in enumerate(self.documents):\\n\\t\\t\\tif document:\\n\\t\\t\\t\\twriter.add_document(**document)\\n\\t\\t\\tupdate_progress_bar(\\\"Building Index\\\", i, len(self.documents))\\n\\n\\t\\twriter.commit(optimize=True)\",\n \"def es_connection_setUp(es_server):\\n datalab_logger_connections.info(\\\"ES : setUp connection\\\")\\n return Elasticsearch(es_server, timeout=30, ignore=[400, 404], maxsize=100)\",\n \"def __init__(self, index, document_id, client=None):\\n self.index = index\\n self.document_id = document_id\\n self.client = client or current_search_client\",\n \"def __init__(self, index_name, doc_type='string'):\\n super(ESMigration, self).__init__()\\n self.index_name = index_name\\n self.doc_type = doc_type\",\n \"def create_indexes(self) -> None:\\n self.collection.create_index(\\\"traceId\\\")\\n self.collection.create_index(\\\"process.serviceName\\\")\",\n \"def setUp(self):\\n\\n #Get Flask test client\\n self.client = app.test_client\\n #Show errors from Flask than happen\\n app.config['TESTING'] = True\\n #Connect to test database\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n #Create tables and add sample data to them\\n db.create_all()\\n example_data()\",\n \"def indexCreationStarted(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def create_index(self):\\n\\n indice = client.IndicesClient(self.es)\\n\\n if not indice.exists(self.es_main_index):\\n indice.create(\\n index=self.es_main_index\\n )\\n\\n return True\",\n \"def handle(self, *args, **options):\\n self.create_indices()\\n self.bulk()\",\n \"def test_recreate_index_that_exists(self):\\n indices = self.elasticsearch_cls().indices\\n indices.exists.return_value = True\\n\\n index_name = 'abcd'\\n self.client._recreate_index(index_name)\\n indices.delete.assert_called_once_with(index_name)\\n indices.create.assert_called_once_with(index_name)\",\n \"def insert_index(self):\\n pass\",\n \"def create_index(es_object, index_name):\\n created = False\\n # index settings\\n # the reason why we need mappings is avoid corrupting your data\\n settings = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 0\\n },\\n \\\"mappings\\\": {\\n # custom type called foods\\n \\\"foods\\\": {\\n \\\"properties\\\": {\\n # Specify that the food_name field contains text values.\\n \\\"food_name\\\": {\\n \\\"type\\\": \\\"text\\\",\\n \\\"fields\\\": {\\n \\\"raw\\\":{ \\n \\\"type\\\": \\\"keyword\\\"\\n } # The food_name.raw field can be used for sorting and aggregations\\n }\\n },\\n # Specify that the categories field contains text values.\\n \\\"categories\\\": {\\n \\\"type\\\": \\\"text\\\",\\n \\\"fields\\\": {\\n \\\"raw\\\":{ \\n \\\"type\\\": \\\"keyword\\\"\\n } # The categories.raw field can be used for sorting and aggregations\\n }\\n },\\n # Specify that the calories field contains integer values.\\n \\\"calories\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"protein\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"carbs\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n },\\n \\\"fat\\\": {\\n \\\"type\\\": \\\"integer\\\"\\n }\\n \\n },\\n }\\n }\\n }\\n try:\\n if not es_object.indices.exists(index_name):\\n # Ignore 400 means to ignore \\\"Index Already Exist\\\" error.\\n es_object.indices.create(index=index_name, ignore=400, body=settings)\\n print('Created Index')\\n created = True\\n except Exception as ex:\\n print(str(ex))\\n finally:\\n return created\",\n \"def __init__(self, conn_str=ELASTICSEARCH_CONN):\\n self.es = Elasticsearch(conn_str)\",\n \"def _es_push_indexes(self, content):\\n for c in self.es_clients:\\n c.create_index(content)\",\n \"def setUp(self):\\n\\n # Get the Flask test client\\n self.client = app.test_client()\\n app.config['TESTING'] = True\\n\\n # Connect to test database\\n connect_to_db(app, \\\"postgresql:///testdb\\\")\\n\\n # Create tables and add sample data\\n db.create_all()\\n example_data()\",\n \"def __init__(self, create_index=True, online=True):\\n self.online = online\\n index_exists = self.index_exists()\\n if create_index and not index_exists:\\n self.create_index()\",\n \"def test_post_foods_search(self):\\n pass\",\n \"def index_all(self, index_name):\\n oks = 0\\n notoks = 0\\n for ok, item in streaming_bulk(\\n self.es_client,\\n self._iter_documents(index_name)\\n ):\\n if ok:\\n oks += 1\\n else:\\n notoks += 1\\n logging.info(\\n \\\"Import results: %d ok, %d not ok\\\",\\n oks,\\n notoks\\n )\",\n \"def _create_indexes(self):\\r\\n # WARNING: The collection will be locked during the index\\r\\n # creation. If the collection has a large number of\\r\\n # documents in it, the operation can take a long time.\\r\\n\\r\\n # TODO: The creation of indexes can be moved to a Django\\r\\n # management command or equivalent. There is also an option to\\r\\n # run the indexing on the background, without locking.\\r\\n self.collection.ensure_index([('time', pymongo.DESCENDING)])\\r\\n self.collection.ensure_index('event_type')\",\n \"def test_create_index_is_called(self):\\r\\n assert len(self.index_calls) == 0\\r\\n\\r\\n connection._index_all_fields = False\\r\\n \\r\\n class TestIndexCreationCallTestVertex(Vertex):\\r\\n col1 = properties.Text(index=True)\\r\\n col2 = properties.Text(index=True, db_field='____column')\\r\\n col3 = properties.Text(db_field='____column3')\\r\\n\\r\\n assert len(self.index_calls) == 2\\r\\n assert 'vid' not in self.index_calls\\r\\n assert 'col1' in self.index_calls\\r\\n assert '____column' in self.index_calls\\r\\n assert '____column3' not in self.index_calls\\r\\n\\r\\n connection._index_all_fields = True\\r\\n self.index_calls = []\\r\\n\\r\\n class TestIndexCreationCallTestVertex2(Vertex):\\r\\n col1 = properties.Text()\\r\\n col2 = properties.Text(db_field='____column')\\r\\n\\r\\n assert len(self.index_calls) == 3\\r\\n assert 'vid' in self.index_calls\\r\\n assert 'col1' in self.index_calls\\r\\n assert '____column' in self.index_calls\",\n \"def indexable_objects_iter(docs, es_index, es_doctype):\\n\\n for doc in docs:\\n insert_doc = {\\n '_index': es_index,\\n '_type': es_doctype,\\n '_id': \\\"%s-%s\\\" % (doc['state'], doc['id']),\\n '_source': doc\\n }\\n yield insert_doc\",\n \"def setUp(self):\\r\\n self.output_dir = '/tmp/'\\r\\n\\r\\n otu_table_vals = array([[0, 0], [1, 5]])\\r\\n\\r\\n self.otu_table = table_factory(otu_table_vals,\\r\\n ['Sample1', 'Sample2'],\\r\\n ['OTU1', 'OTU2'],\\r\\n [None, None],\\r\\n [{\\\"taxonomy\\\": [\\\"Bacteria\\\"]},\\r\\n {\\\"taxonomy\\\": [\\\"Archaea\\\"]}])\\r\\n\\r\\n filt_otu_table_vals = array([[1, 5]])\\r\\n\\r\\n self.filt_otu_table = table_factory(filt_otu_table_vals,\\r\\n ['Sample1', 'Sample2'],\\r\\n ['OTU2'],\\r\\n [None, None],\\r\\n [{\\\"taxonomy\\\": [\\\"Archaea\\\"]}])\\r\\n\\r\\n self.num_otu_hits = 5\\r\\n self._folders_to_cleanup = []\",\n \"def __init__(self, index, data_type):\\n self._datastore = ElasticsearchDataStore(\\n host=current_app.config['ELASTIC_HOST'],\\n port=current_app.config['ELASTIC_PORT'])\\n self._config = SimilarityScorerConfig(index, data_type)\",\n \"def setUpClass(cls):\\n\\n cls.client = TestClient(fastapi_app.app)\\n log.info('Completed initialization for FastAPI based REST API tests')\",\n \"def _init_es(self):\\n es = Elasticsearch([{'host': elastic_conf['host'], 'port': elastic_conf['port']}])\\n print('Connected to Elastic Search:', es.ping())\\n return es\",\n \"def test_documents_for(self):\\n # Test the default ES version\\n self._test_documents_for(_documents_for)\\n\\n # Test the DB version\\n self._test_documents_for(_db_documents_for)\",\n \"def setUp(self):\\n db.drop_all() # clean up the last tests\\n db.create_all() # create new tables\\n self.app = app.test_client()\",\n \"def initialize_indexer_backend(self):\\n pass\",\n \"def test_create_index(self, collection):\\n collection.create_index(\\\"hello\\\")\\n assert collection._indexes == {\\\"_id_\\\": ((\\\"_id\\\",), {(1,)})}\\n\\n collection.create_index(\\\"hello\\\", unique=True)\\n assert collection._indexes == {\\n \\\"_id_\\\": ((\\\"_id\\\",), {(1,)}),\\n \\\"hello_1\\\": ((\\\"hello\\\",), {(\\\"there\\\",)}),\\n }\",\n \"def setUp(self):\\n db.drop_all() # clean up the last tests\\n db.create_all() # create new tables\\n self.app = app.test_client()\",\n \"def test_load_index(self):\\n\\n c = Client()\\n response = c.get('/taric_books/')\\n\\n self.assertEqual(response.status_code, 200)\",\n \"def test_index_table(self, mapping_cls, bulk_mock):\\n indices = self.elasticsearch_cls().indices\\n mapping = mapping_cls()\\n rows = [\\n {'id': 1, 'number': '123456789'},\\n {'id': 2, 'number': '234567890'},\\n {'id': 3, 'number': '345678901'},\\n ]\\n bulk_mock.return_value = (len(rows), [])\\n\\n db_path = 'some path'\\n table_name = 'calls'\\n table_reader = Mock()\\n table_reader.rows.return_value = rows\\n table_reader.database.db_filename = db_path\\n table_reader.table.name = table_name\\n documents_indexed = self.client._index_table(table_reader)\\n indices.put_mapping.assert_called_once_with(\\n index=self.client.INDEX_NAME,\\n doc_type=hashlib.md5(\\n '{}:{}'.format(db_path, table_name)).hexdigest(),\\n body=mapping.mapping)\\n self.assertEqual(documents_indexed, len(rows))\",\n \"def __create_new_index(self, index, index_id, begin_timestamp):\\n index_name = index + \\\"_\\\" + str(index_id)\\n target_config = index + \\\".target_config\\\"\\n pipeline_id = index + \\\".pipeline\\\"\\n mapping = {\\n \\\"user_id\\\": {\\\"type\\\": \\\"long\\\"},\\n \\\"client_id\\\": {\\\"type\\\": \\\"long\\\"},\\n \\\"partner_id\\\": {\\\"type\\\": \\\"long\\\"},\\n \\\"module\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"page\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"uri\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"app_type\\\": {\\\"type\\\": \\\"keyword\\\"},\\n \\\"created_at\\\": {\\\"type\\\": \\\"date\\\"},\\n \\\"request_time\\\": {\\\"type\\\": \\\"date\\\"},\\n \\\"duration\\\": {\\\"type\\\": \\\"long\\\"},\\n }\\n body = {\\n \\\"settings\\\": {\\n \\\"number_of_shards\\\": 1,\\n \\\"number_of_replicas\\\": 1,\\n },\\n \\\"mappings\\\": {\\n \\\"docs\\\": {\\n \\\"properties\\\": mapping\\n }\\n },\\n }\\n self.client.indices.create(index=index_name, body=body)\\n self.client.index(\\n index=target_config,\\n doc_type=\\\"config\\\",\\n id=index_id,\\n body={\\\"index_id\\\": index_id, \\\"begin_timestamp\\\": begin_timestamp},\\n )\\n\\n return index_id\",\n \"def test_model_object_index(self):\\n car = Car.objects.first()\\n car.index_to_appsearch()\\n self.assertEqual(self.client_index.call_count, 1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.770078","0.7126145","0.68322456","0.6620452","0.6540127","0.64694273","0.6463141","0.63948095","0.63571036","0.6345623","0.63434756","0.62436575","0.6241861","0.62167126","0.6173552","0.6168257","0.6160923","0.61488116","0.61431104","0.6114989","0.6112638","0.61078477","0.60961443","0.6088263","0.6085637","0.60685426","0.6068401","0.60621226","0.60492617","0.6024941","0.60124254","0.59818494","0.5977584","0.59753495","0.59590375","0.5958373","0.59516615","0.59236974","0.585799","0.5829391","0.58282787","0.5822192","0.5807408","0.57578254","0.5730082","0.57238036","0.57224524","0.5701289","0.5699173","0.5695353","0.56889325","0.56671274","0.56511706","0.56489253","0.56467503","0.56454456","0.56431466","0.56299376","0.56266356","0.56260526","0.5617657","0.56160593","0.5609596","0.56017745","0.560055","0.55877465","0.5587652","0.5572126","0.5569475","0.55667585","0.55656123","0.55645","0.5548391","0.5545548","0.5544492","0.5538783","0.5525807","0.5521719","0.55181235","0.5517911","0.5500316","0.54913867","0.5490912","0.54841614","0.5478225","0.54754627","0.54692525","0.5463227","0.546177","0.5458796","0.5453683","0.54510826","0.54499227","0.5449478","0.5449187","0.5446562","0.5445322","0.5441659","0.5441197","0.5434966"],"string":"[\n \"0.770078\",\n \"0.7126145\",\n \"0.68322456\",\n \"0.6620452\",\n \"0.6540127\",\n \"0.64694273\",\n \"0.6463141\",\n \"0.63948095\",\n \"0.63571036\",\n \"0.6345623\",\n \"0.63434756\",\n \"0.62436575\",\n \"0.6241861\",\n \"0.62167126\",\n \"0.6173552\",\n \"0.6168257\",\n \"0.6160923\",\n \"0.61488116\",\n \"0.61431104\",\n \"0.6114989\",\n \"0.6112638\",\n \"0.61078477\",\n \"0.60961443\",\n \"0.6088263\",\n \"0.6085637\",\n \"0.60685426\",\n \"0.6068401\",\n \"0.60621226\",\n \"0.60492617\",\n \"0.6024941\",\n \"0.60124254\",\n \"0.59818494\",\n \"0.5977584\",\n \"0.59753495\",\n \"0.59590375\",\n \"0.5958373\",\n \"0.59516615\",\n \"0.59236974\",\n \"0.585799\",\n \"0.5829391\",\n \"0.58282787\",\n \"0.5822192\",\n \"0.5807408\",\n \"0.57578254\",\n \"0.5730082\",\n \"0.57238036\",\n \"0.57224524\",\n \"0.5701289\",\n \"0.5699173\",\n \"0.5695353\",\n \"0.56889325\",\n \"0.56671274\",\n \"0.56511706\",\n \"0.56489253\",\n \"0.56467503\",\n \"0.56454456\",\n \"0.56431466\",\n \"0.56299376\",\n \"0.56266356\",\n \"0.56260526\",\n \"0.5617657\",\n \"0.56160593\",\n \"0.5609596\",\n \"0.56017745\",\n \"0.560055\",\n \"0.55877465\",\n \"0.5587652\",\n \"0.5572126\",\n \"0.5569475\",\n \"0.55667585\",\n \"0.55656123\",\n \"0.55645\",\n \"0.5548391\",\n \"0.5545548\",\n \"0.5544492\",\n \"0.5538783\",\n \"0.5525807\",\n \"0.5521719\",\n \"0.55181235\",\n \"0.5517911\",\n \"0.5500316\",\n \"0.54913867\",\n \"0.5490912\",\n \"0.54841614\",\n \"0.5478225\",\n \"0.54754627\",\n \"0.54692525\",\n \"0.5463227\",\n \"0.546177\",\n \"0.5458796\",\n \"0.5453683\",\n \"0.54510826\",\n \"0.54499227\",\n \"0.5449478\",\n \"0.5449187\",\n \"0.5446562\",\n \"0.5445322\",\n \"0.5441659\",\n \"0.5441197\",\n \"0.5434966\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6929581"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":74582,"cells":{"query":{"kind":"string","value":"Limit result list with \"MAX_RESULT\""},"document":{"kind":"string","value":"def limit_result(result_set):\n max_result = MAX_RESULT\n result = []\n if max_result > 0:\n result = result_set[:max_result]\n result.append(\"Total result: {}\".format(len(result_set)))\n return result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def set_result_limit(self, data):\n self.add_payload('resultLimit', data)\n self._result_limit = self._uni(data)","def limit(self, limit):\n\n # Return between 1 and 250 results, defaults to 10\n return max(1, min(250, int(limit) if limit else 10))","def max_results(self, max_results: float):\n\n self._max_results = max_results","def get_select_all_max_items(self):\n return 1500","def paging_results(self):\n\n return 30","def _determine_limit(self, limit):\n\n # Note: +1 is allowed here because it allows\n # the user to fetch one beyond to see if they\n # are at the end of the list\n if not limit:\n res = conf.api_configuration.max_returned_num + 1\n else:\n res = min(conf.api_configuration.max_returned_num + 1, limit)\n\n return res","def max_results(self) -> str:\n return pulumi.get(self, \"max_results\")","def test_search_result_limit(self):\n results = self.searcher.search(\"crossfit\", 1)\n expected_results = 6\n\n self.assertEqual(results[0].indexable.docid, expected_results)","def post_process_result(res, query, max_rank=None):\n # Sets the rank of result\n i = 0\n for r in res:\n r.rank = _lcs_diff_cent(query, r.address)\n if not max_rank is None and r.rank > max_rank:\n res.pop(i)\n else:\n i = i + 1\n\n # It sorts the result in ascending order of rank\n res.sort(lambda a,b: a.rank-b.rank)\n\n return res","def set_options(self, options_list):\n self._result_limit = options_list['result_limit'].get_value()","def limit(requestContext, seriesList, n):\n return seriesList[0:n]","def limit(self, max_num):\n return QuerySet(itertools.islice(self, max_num))","def _Get(self, count):\n if count > MAXIMUM_RESULTS:\n count = MAXIMUM_RESULTS\n entity_list = self._Next(count)\n while len(entity_list) < count and self.__more_results:\n next_results = self._Next(count - len(entity_list))\n if not next_results:\n break\n entity_list += next_results\n return entity_list;","def add_result(self, result):\n if len(self.results) == self.max_size:\n del self.results[0]\n self.results.append(result)","def number_results(self):\n pass","def test_get_filtered_list_limit(self):\n flexmock(errata).should_receive(\"Advisory\").and_return(None)\n\n response = flexmock(status_code=200)\n response.should_receive(\"json\").and_return(test_structures.example_erratum_filtered_list)\n\n flexmock(errata.requests).should_receive(\"get\").and_return(response)\n\n res = errata.get_filtered_list(limit=1)\n self.assertEqual(1, len(res))","def test_get_hyperflex_feature_limit_internal_list(self):\n pass","def make_more(self,result,con):","def test_get_hyperflex_feature_limit_external_list(self):\n pass","def apply_limit(tasks, limit):\n return tasks.limit(limit)","def calculate(self, limit):\r\n pass","def limit(self, limit):\n raise NotImplementedError(\"This should have been implemented.\")","async def test_txn_list_paginated_by_max_index(self):\n paging = Mocks.make_paging_response(0, 4)\n self.stream.preset_response(\n head_id='d',\n paging=paging,\n transactions=Mocks.make_txns('d', 'c', 'b'))\n\n response = await self.get_assert_200('/transactions?max=2&count=7')\n controls = Mocks.make_paging_controls(3, start_index=0)\n self.stream.assert_valid_request_sent(paging=controls)\n\n self.assert_has_valid_head(response, 'd')\n self.assert_has_valid_link(response, '/transactions?head=d&max=2&count=7')\n self.assert_has_valid_paging(response, paging,\n '/transactions?head=d&min=3&count=7')\n self.assert_has_valid_data_list(response, 3)\n self.assert_txns_well_formed(response['data'], 'd', 'c', 'b')","def test_list_zones_max_items_100(shared_zone_test_context):\n result = shared_zone_test_context.list_zones_client.list_zones(status=200)\n assert_that(result[\"maxItems\"], is_(100))","def test_query_train_jobs_with_exceeded_limit(self, client):\n params = dict(offset=0, limit=1000)\n url = get_url(BASE_URL, params)\n response = client.get(url)\n result = response.get_json()\n assert result.get('error_code') == '50540002'","def get_all_from_top_ten(title,users,max = 3):\n \"\"\" ten prolific users \"\"\"\n \"\"\" max : number of user with related followers \"\"\"\n getAllUsers(users,all_users,users_set,proceeded_users,max,user_cpt,title)\n for data in users_set:\n print(data.id)","def calculate(self, limit):\n pass","def truncate_ocr_sim_list(token, ocr_sims_list, limit=10):\n if len(ocr_sims_list) <= limit:\n return ocr_sims_list\n\n ocr_scores = set([sc for sim, sc in ocr_sims_list.items()])\n\n # Limit of 10 different scores allowed\n sorted_ocr_scores = sorted(ocr_scores, reverse=True)[:limit]\n ocr_list = []\n for score in sorted_ocr_scores:\n tmp_ocr_list = [ocr_sims for ocr_sims, ocr_score in ocr_sims_list.items() if ocr_score == score]\n\n if len(ocr_list) + len(tmp_ocr_list) > limit:\n list_len = limit - len(ocr_list)\n tmp_list = []\n\n while len(tmp_list) < list_len:\n tmp_list += select_lower_edit_distance(token, tmp_ocr_list)\n\n if len(ocr_list) + len(tmp_list) == limit: # Final list has exactly 10 elements\n ocr_list += tmp_list\n break\n else: # List has more than 10 arguments (need to chose only the n elements needed)\n alpha_tmp_list = []\n\n while len(alpha_tmp_list) != list_len:\n alpha_word = select_best_alphabetical_word(token, tmp_list)\n\n alpha_tmp_list.append(alpha_word)\n tmp_list = [tkn for tkn in tmp_list if tkn != alpha_word]\n\n ocr_list += alpha_tmp_list\n break\n elif len(ocr_list) + len(tmp_ocr_list) == limit:\n ocr_list += tmp_ocr_list\n break\n else: # len(ocr_list) + len(tmp_ocr_list) < limit\n ocr_list += tmp_ocr_list\n\n if len(ocr_list) != limit:\n raise IndexError(\"OCR list is still too big (\"+str(len(ocr_list))+\"/\"+str(limit)+\")\")\n\n return {tkn: ocr_sims_list[tkn] for tkn in ocr_list}","def get_results(job, limit):\n reader = results.ResultsReader(job.results(count=limit))\n return {\"results\": [row for row in reader]}","def max_results(self) -> float:\n return self._max_results","def _ip_list_pagination(ip_list, results_per_page):\n paged_response = [ip_list[i:i+results_per_page]\n for i in range(0, len(ip_list), results_per_page)]\n return paged_response","def __get_top(self, result, top=10):\n result = result.sort_values(by=\"bias_score\", ascending=False).drop_duplicates(subset='productid', keep=\"first\")\n print(result)\n result = result[:top].sort_values(by=\"final_score\", ascending=False).productid\n\n return list(result)","def limit(self, max_size):\n return self.__class__(itertools.islice(self, max_size))","def get_query_results(QueryExecutionId=None, NextToken=None, MaxResults=None):\n pass","def aggregated_results(self, limit=2000) -> List[dict]:\n stored_events = []\n for events in self._iter_events():\n stored_events.extend(events)\n if len(stored_events) >= limit:\n return stored_events[:limit]\n return stored_events","def limit(self, limit: int) -> 'Query':\n self.limit_index = max(1, limit)\n return self","def test_exceed_limit_request(self):\n actions.login(ADMIN_EMAIL)\n ids_list = list(range(SkillAggregateRestHandler.MAX_REQUEST_SIZE))\n get_url = '%s?%s' % (self.URL, urllib.urlencode({\n 'ids': ids_list}, True))\n\n response = transforms.loads(self.get(get_url).body)\n self.assertEqual(412, response['status'])","async def test_txn_list_paginated_by_max_id(self):\n paging = Mocks.make_paging_response(1, 4, previous_id='d', next_id='a')\n self.stream.preset_response(\n head_id='d',\n paging=paging,\n transactions=Mocks.make_txns('c', 'b'))\n\n response = await self.get_assert_200('/transactions?max=b&count=2')\n controls = Mocks.make_paging_controls(2, end_id='b')\n self.stream.assert_valid_request_sent(paging=controls)\n\n self.assert_has_valid_head(response, 'd')\n self.assert_has_valid_link(response, '/transactions?head=d&max=b&count=2')\n self.assert_has_valid_paging(response, paging,\n '/transactions?head=d&min=a&count=2',\n '/transactions?head=d&max=d&count=2')\n self.assert_has_valid_data_list(response, 2)\n self.assert_txns_well_formed(response['data'], 'c', 'b')","def limit(self, count):\n self._limit = count\n return self","def getBatchSize(self, context, obj):\n return 10","def test_listingCatalogEntriesWithLimit(self):\n expected_results = {\n '': 2,\n '?limit=1': 2,\n }\n\n for search, expected in expected_results.items():\n response = self.client.get(\n '/api/v1/catalogentry/{}'.format(search)\n )\n content = response.content.decode('utf8')\n json_data = json.loads(content)\n result = json_data['count']\n\n self.assertEqual(result, expected)","def test_get_remain_limit(self):\n finder = FinderInsidePro(self.test_key)\n limit = finder.get_remain_limit()\n assert isinstance(limit, int)\n assert limit > 0","def get_iterator_results(self, rest_response):\n full_response = list()\n full_response += rest_response['resultList']['result']\n\n if rest_response.get('count') and int(rest_response.get('count')) > 0:\n count = rest_response.get('count')\n max_page_size = rest_response.get('maxPageSize')\n if int(count) > int(max_page_size):\n total_iterations = int(math.ceil(count / float(max_page_size)))\n iterator_id = rest_response.get('id')\n # We skip to second page as we already have the first page in\n # the input param rest_response\n for x in range(1, total_iterations):\n start = x * max_page_size + 1\n end = (x + 1) * max_page_size\n if end > count:\n end = count\n full_response += self.get_iterator_page_list(iterator_id,\n start, end)\n return full_response","def limit(self, limit):\n self._evaluated = False\n self._limit = limit\n return self","def test_get_top_respects_limit(self):\r\n\r\n ranked_3_app = self.create_app_with_contributors(8, 0, name='three')\r\n ranked_2_app = self.create_app_with_contributors(9, 0, name='two')\r\n ranked_1_app = self.create_app_with_contributors(10, 0, name='one')\r\n ranked_4_app = self.create_app_with_contributors(7, 0, name='four')\r\n\r\n top_apps = cached_apps.get_top(n=2)\r\n\r\n assert len(top_apps) is 2, len(top_apps)","def get_max_item(self):\n return self._get_page('maxitem').json()","def test_result_query_over_10k(cbcsdk_mock):\n api = cbcsdk_mock.api\n cbcsdk_mock.mock_request(\"POST\",\n \"/livequery/v1/orgs/test/runs/run_id/results/_search\",\n GET_RUN_RESULTS_RESP_OVER_10k)\n result_query = api.select(Result).run_id(\"run_id\")\n\n list(result_query._perform_query())\n assert result_query._total_results == 10000","def test_get_result_top_files(self):\n pass","def maximumAbove(requestContext, seriesList, n):\n results = []\n for series in seriesList:\n if max(series) > n:\n results.append(series)\n return results","def resultDetectionQuantitationLimit(self) -> List[DetectionQuantitationLimit]:\r\n return self.__resultDetectionQuantitationLimit","def test_limit(self) -> None:\n\n channel = self.make_request(\n \"GET\",\n self.url + \"?limit=5\",\n access_token=self.admin_user_tok,\n )\n\n self.assertEqual(200, channel.code, msg=channel.json_body)\n self.assertEqual(channel.json_body[\"total\"], 20)\n self.assertEqual(len(channel.json_body[\"event_reports\"]), 5)\n self.assertEqual(channel.json_body[\"next_token\"], 5)\n self._check_fields(channel.json_body[\"event_reports\"])","def limit(self, row_count):\n self._limit = row_count\n return self","def test_result_size_too_large(fxc, endpoint, size=550000):\n fn_uuid = fxc.register_function(\n large_result_producer, endpoint, description=\"LargeResultProducer\"\n )\n task_id = fxc.run(\n size, # This is the current result size limit\n endpoint_id=endpoint,\n function_id=fn_uuid,\n )\n\n with pytest.raises(MaxResultSizeExceeded):\n wait_for_task(fxc, task_id, walltime=10)","def test_limit(self):\n\n mapper(Item, items)\n mapper(Order, orders, properties={\n 'items':relation(Item, secondary=order_items, lazy=False, order_by=items.c.id)\n })\n mapper(User, users, properties={\n 'addresses':relation(mapper(Address, addresses), lazy=False, order_by=addresses.c.id),\n 'orders':relation(Order, lazy=True)\n })\n\n sess = create_session()\n q = sess.query(User)\n\n if testing.against('mysql'):\n l = q.limit(2).all()\n assert fixtures.user_all_result[:2] == l\n else:\n l = q.limit(2).offset(1).order_by(User.id).all()\n print fixtures.user_all_result[1:3]\n print l\n assert fixtures.user_all_result[1:3] == l","def GetNextBatchOfResults(self) -> typing.List[Repository.Repository]:\n while True:\n try:\n logging.debug('Requesting page %d', self.next_page_num)\n page = list(self.query.get_page(self.next_page_num))\n logging.debug('Page %d contains %d results', self.next_page_num,\n len(page))\n self.next_page_num += 1\n return page\n except github.RateLimitExceededException:\n logging.debug('Pausing on GitHub rate limit')\n time.sleep(3)\n except github.GithubException:\n # One possible cause for this exception is when trying to request\n # a page beyond 1000 results, since GitHub only returns the first\n # 1000 results for a query.\n return []","def request_more_resources():\n logger.info(\"NEED MORE RESOURCES!!!!\")","def testRetrievingWithoutSpecifyingLimit(self):\n cached_items = cached_list_logic.getCachedItems('test_list', start=2)\n self.assertListEqual([self.item3, self.item4, self.item5], cached_items)","def test_allowed_result_sizes(fxc, endpoint, size):\n fn_uuid = fxc.register_function(\n large_result_producer, endpoint, description=\"LargeResultProducer\"\n )\n task_id = fxc.run(\n size, # This is the current result size limit\n endpoint_id=endpoint,\n function_id=fn_uuid,\n )\n\n x = wait_for_task(fxc, task_id, walltime=10)\n assert len(x) == size, \"Result size does not match excepted size\"","def test_list_flavors_limit_results(self):\n response = self.flavors_client.list_flavors(limit=1)\n flavors = response.entity\n self.assertEqual(1, len(flavors))","def test_collection_limit(testapp):\n obj1 = {\n 'title': \"Testing1\",\n 'description': \"This is testing object 1\",\n }\n obj2 = {\n 'title': \"Testing2\",\n 'description': \"This is testing object 2\",\n }\n obj3 = {\n 'title': \"Testing3\",\n 'description': \"This is testing object 3\",\n }\n testapp.post_json('/embedding-tests', obj1, status=201)\n testapp.post_json('/embedding-tests', obj2, status=201)\n testapp.post_json('/embedding-tests', obj3, status=201)\n res_all = testapp.get('/embedding-tests/?limit=all', status=200)\n res_2 = testapp.get('/embedding-tests/?limit=2', status=200)\n assert len(res_all.json['@graph']) == 3\n assert len(res_2.json['@graph']) == 2","def test_cell_list_limit_positive_num_success(self, mock_list):\n self.shell('cell-list -r 1 --limit 1')\n mock_list.assert_called_once_with(limit=1)","def option_max_scans_in_ui(self):\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/view/optionMaxScansInUI/')))","def extract_final_max_q(results):\n return results.loc[results.index[-1], 'max_q_values']","def limit(self, row_count, offset=0):\n self._limit = (row_count, offset)\n return self","def limit(self, limit):\n self._limit = limit","def get_last(self, count):","def maximumBelow(requestContext, seriesList, n):\n\n result = []\n for series in seriesList:\n if max(series) <= n:\n result.append(series)\n return result","def limit(self, limit):\n\n return self._get(\"limit\", limit, Table)","def get_limit(self):\n return self.limit","def nextNumberOfResults(self, N=10):\n self.start += self.N\n self.N = N","def calc(results: int, current: int, limit=100) -> List[Tuple[int, int, bool, bool, bool]]:\n\n # total number of pages, limit is number of result per page\n pages = results // limit\n if results % limit > 0:\n pages += 1\n\n result = []\n current_page = int(current)\n min_page = max(current_page - PAGING_LINKS + 1, 0)\n max_page = min(current_page + PAGING_LINKS, pages)\n\n for page in range(min_page, max_page):\n result.append((page,\n page * limit,\n # highlight current page marker\n True if page == current_page else False,\n # replace first page number with arrow\n True if min_page >= 1 else False,\n True if current_page >= pages - PAGING_LINKS else False))\n if len(result) == 1:\n return []\n return result","def limit(iterable, n):\n for count, element in enumerate(iterable):\n if count >= n: break\n else: yield element","def test_messenger_limit():\n all_messages_resp = requests.get(BASE_URL)\n all_messages = all_messages_resp.json()\n total_message_count = len(all_messages)\n message_limit = total_message_count // 2\n\n query_params = {\"limit\": message_limit}\n limit_resp = requests.get(BASE_URL, params=query_params)\n limited_messages = limit_resp.json()\n assert limit_resp.status_code == 200\n assert len(limited_messages) == message_limit","def test_limit(self):\n user_a = self.Person.objects.create(name=\"User A\", age=20)\n _ = self.Person.objects.create(name=\"User B\", age=30)\n\n # Test limit on a new queryset\n people = list(self.Person.objects.limit(1))\n assert len(people) == 1\n assert people[0] == user_a\n\n # Test limit on an existing queryset\n people = self.Person.objects\n assert len(people) == 2\n people2 = people.limit(1)\n assert len(people) == 2\n assert len(people2) == 1\n assert people2[0] == user_a\n\n # Test limit with 0 as parameter\n people = self.Person.objects.limit(0)\n assert people.count(with_limit_and_skip=True) == 2\n assert len(people) == 2\n\n # Test chaining of only after limit\n person = self.Person.objects().limit(1).only(\"name\").first()\n assert person == user_a\n assert person.name == \"User A\"\n assert person.age is None","def _limit(self, row):\n if 'counter' not in self._state:\n init_value = -1 if self._args.head else 0\n self._state['counter'] = init_value\n self._state['counter'] += 1\n if self._state['counter'] >= self._args.limit:\n self._leave = True\n return row","def test_returns_limit_projects(self):\n # Arrange\n # Create and arrange test projects\n self.arrange_projects()\n # Act\n response = self.client.get(\n f\"{self.url}?limit=1\", headers={\"Authorization\": self.user_session_token}\n )\n # Assert\n self.assertEqual(response.status_code, 200)\n self.assertEqual(len(response.json[\"results\"]), 1)","def output_results(self, model, limit):\n out = []\n for key in self.results[model].keys():\n qId = key\n count = 1\n sorted_docs = sorted(self.results[model][key].items(), key=operator.itemgetter(1), reverse=True)[:limit] \n for val in sorted_docs:\n dId = val[0]\n dScore = val[1]\n dRank = count\n out.append(str(qId) + \" \" + \"Q0\" + \" \" + str(dId) + \" \" + str(dRank) + \" \" + str(dScore) + \" \" + \"Exp\")\n count += 1\n return out","def select_recent_queries(max_rows=5):\n conn = sqlite3.connect(DB_PATH)\n cursor = conn.cursor()\n\n # Insert a row of data\n cursor.execute(\"\"\"SELECT search\n FROM query\n ORDER BY id DESC\n LIMIT \"\"\" + str(max_rows) + \";\")\n\n results = cursor.fetchall()\n\n # We can also close the connection if we are done with it.\n conn.close()\n\n return results","def limit(self, variants):\n return variants[:self.lim]","def test_last_page_is_exactly_page_size():\n # A page of 2 results, then a second with 1 result. The exception should\n # not be called.\n def pages(*args, **kwargs):\n yield json_to_resp([{'id': 1}, {'id': 2}])\n yield json_to_resp([{'id': 3}, {'id': 4}])\n\n # PW returns 200 OK and empty list, not 404, when past last page.\n yield json_to_resp([])\n\n cn = connect(email='foo', token='bar')\n cn.session.post = mock.Mock(side_effect=pages())\n rs = ResultSet(resource_cls=IdResource, page_size=2)\n assert {r.id for r in rs} == {1, 2, 3, 4}","def get_more_records(self):\n\n\t\treturn self.__more_records","def limit_size(self, catalog):\n if len(catalog)<=self.limit:\n return catalog\n mem = {}\n for instance in catalog:\n if (instance['vCpu'], math.ceil(instance['memory'])) not in mem:\n mem[(instance['vCpu'], math.ceil(instance['memory']))] = instance\n out = [val for val in mem.values()]\n if len(out)>self.limit:\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\n return out[:self.limit]\n return out","def testMax(self):\n top = 10\n table = self.auth.table(self.dataset, self.table, top=top)\n record_count = len(table)\n self.assertLessEqual(record_count, top)","def limit(self):\n return self._limit","def limit(self):\n return self._limit","def test_max_features_paged(\n self, mp_wfs, mp_get_schema, mp_remote_describefeaturetype,\n mp_wfs_max_features, mp_remote_wfs_paged_feature):\n s = BoringSearch()\n df = s.search(return_fields=['pkey_boring'], max_features=15)\n assert len(df) == 15","def where_above(lst, limit):\n return [x for x in lst if x > limit]","def test_exceeded_limit(self):\n msg=self.sample_data(\"error_exceeded_limit.xml\")\n error = ErrorParser().process_all(msg)\n assert isinstance(error, PatronLoanLimitReached)\n eq_(u'Patron cannot loan more than 12 documents', error.message)","def test_limit(db_session):\n query_params = {\"limit\": \"1\"}\n parser = ModelQueryParamParser(query_params)\n album_resource = AlbumResource(session=db_session)\n offset_limit_info = parser.parse_offset_limit(page_max_size=30)\n offset = offset_limit_info.offset\n limit = offset_limit_info.limit\n result = album_resource.get_collection(\n filters=parser.parse_filters(album_resource.model),\n sorts=parser.parse_sorts(),\n limit=limit,\n offset=offset\n )\n assert len(result) == 1","def getBatchSize(self, context, obj):\n return 100","def testQueryWithLimit(self):\n for i in range(11):\n row_name = \"aff4:/row:%02d\" % i\n data_store.DB.Set(row_name, \"metadata:5\", i, token=self.token)\n data_store.DB.Set(row_name, \"aff4:type\", \"test\", token=self.token)\n\n # Retrieve all subjects with prefix row1:\n rows = [row for row in data_store.DB.Query(\n [], data_store.DB.filter.HasPredicateFilter(\"metadata:5\"),\n subject_prefix=\"aff4:/row:\", limit=(2, 3), token=self.token)]\n\n self.assertEqual(len(rows), 3)\n self.assertEqual(rows[0][\"subject\"][0][0], \"aff4:/row:02\")\n self.assertEqual(rows[1][\"subject\"][0][0], \"aff4:/row:03\")\n self.assertEqual(rows[2][\"subject\"][0][0], \"aff4:/row:04\")","def limit(self, limit):\n self._limit = limit\n return self","def extract_q_values(results):\n return results['max_q_values'].tolist()","def queryWithResultSize(self, query_str, result_size):\n try:\n conn = MySQLdb.connect(**self.connect)\n cursor = conn.cursor()\n cursor.execute(query_str)\n result = cursor.fetchmany(result_size)\n conn.commit()\n conn.close()\n return result\n except Exception, e:\n logger.error(\"mysql util error:\" + str(e))","def get_top(n=10):\r\n sql = text('''SELECT \"user\".id, \"user\".name, \"user\".fullname, \"user\".email_addr,\r\n \"user\".created, \"user\".info, COUNT(task_run.id) AS task_runs FROM task_run, \"user\"\r\n WHERE \"user\".id=task_run.user_id GROUP BY \"user\".id\r\n ORDER BY task_runs DESC LIMIT :limit''')\r\n results = db.engine.execute(sql, limit=n)\r\n top_users = []\r\n for row in results:\r\n user = dict(id=row.id, name=row.name, fullname=row.fullname,\r\n email_addr=row.email_addr,\r\n created=row.created,\r\n task_runs=row.task_runs,\r\n info=dict(json.loads(row.info)))\r\n top_users.append(user)\r\n return top_users","def get_max_rows_per_partition() -> int:\n pass","def limit(self, limit):\n if limit is None:\n return self\n\n self.query = self.query.limit(limit)\n self._has_limit = True\n return self","def limit(self, limit):\n params = join_params(self.parameters, {\"limit\": limit,\n \"url_domain\": self.url_domain,\n \"proxies\": self.proxies})\n\n return self.__class__(**params)","def test_queryset_limit_immutability(self):\r\n query1 = TestModel.objects(test_id=5).limit(1)\r\n assert query1._limit == 1\r\n\r\n query2 = query1.filter(expected_result__gte=1)\r\n assert query2._limit == 1\r\n\r\n query3 = query1.filter(expected_result__gte=1).limit(2)\r\n assert query1._limit == 1\r\n assert query3._limit == 2","def apply_limit(self, entrylist):\r\n\r\n entryset = set(entrylist)\r\n limitset = set(self.limitlist)\r\n if not limitset:\r\n limitset = set(self.indexes())\r\n if isinstance(entrylist, set):\r\n return entryset.intersection(limitset)\r\n if isinstance(entrylist, list):\r\n return list(entryset.intersection(limitset))\r\n return list(entryset.intersection(limitset))"],"string":"[\n \"def set_result_limit(self, data):\\n self.add_payload('resultLimit', data)\\n self._result_limit = self._uni(data)\",\n \"def limit(self, limit):\\n\\n # Return between 1 and 250 results, defaults to 10\\n return max(1, min(250, int(limit) if limit else 10))\",\n \"def max_results(self, max_results: float):\\n\\n self._max_results = max_results\",\n \"def get_select_all_max_items(self):\\n return 1500\",\n \"def paging_results(self):\\n\\n return 30\",\n \"def _determine_limit(self, limit):\\n\\n # Note: +1 is allowed here because it allows\\n # the user to fetch one beyond to see if they\\n # are at the end of the list\\n if not limit:\\n res = conf.api_configuration.max_returned_num + 1\\n else:\\n res = min(conf.api_configuration.max_returned_num + 1, limit)\\n\\n return res\",\n \"def max_results(self) -> str:\\n return pulumi.get(self, \\\"max_results\\\")\",\n \"def test_search_result_limit(self):\\n results = self.searcher.search(\\\"crossfit\\\", 1)\\n expected_results = 6\\n\\n self.assertEqual(results[0].indexable.docid, expected_results)\",\n \"def post_process_result(res, query, max_rank=None):\\n # Sets the rank of result\\n i = 0\\n for r in res:\\n r.rank = _lcs_diff_cent(query, r.address)\\n if not max_rank is None and r.rank > max_rank:\\n res.pop(i)\\n else:\\n i = i + 1\\n\\n # It sorts the result in ascending order of rank\\n res.sort(lambda a,b: a.rank-b.rank)\\n\\n return res\",\n \"def set_options(self, options_list):\\n self._result_limit = options_list['result_limit'].get_value()\",\n \"def limit(requestContext, seriesList, n):\\n return seriesList[0:n]\",\n \"def limit(self, max_num):\\n return QuerySet(itertools.islice(self, max_num))\",\n \"def _Get(self, count):\\n if count > MAXIMUM_RESULTS:\\n count = MAXIMUM_RESULTS\\n entity_list = self._Next(count)\\n while len(entity_list) < count and self.__more_results:\\n next_results = self._Next(count - len(entity_list))\\n if not next_results:\\n break\\n entity_list += next_results\\n return entity_list;\",\n \"def add_result(self, result):\\n if len(self.results) == self.max_size:\\n del self.results[0]\\n self.results.append(result)\",\n \"def number_results(self):\\n pass\",\n \"def test_get_filtered_list_limit(self):\\n flexmock(errata).should_receive(\\\"Advisory\\\").and_return(None)\\n\\n response = flexmock(status_code=200)\\n response.should_receive(\\\"json\\\").and_return(test_structures.example_erratum_filtered_list)\\n\\n flexmock(errata.requests).should_receive(\\\"get\\\").and_return(response)\\n\\n res = errata.get_filtered_list(limit=1)\\n self.assertEqual(1, len(res))\",\n \"def test_get_hyperflex_feature_limit_internal_list(self):\\n pass\",\n \"def make_more(self,result,con):\",\n \"def test_get_hyperflex_feature_limit_external_list(self):\\n pass\",\n \"def apply_limit(tasks, limit):\\n return tasks.limit(limit)\",\n \"def calculate(self, limit):\\r\\n pass\",\n \"def limit(self, limit):\\n raise NotImplementedError(\\\"This should have been implemented.\\\")\",\n \"async def test_txn_list_paginated_by_max_index(self):\\n paging = Mocks.make_paging_response(0, 4)\\n self.stream.preset_response(\\n head_id='d',\\n paging=paging,\\n transactions=Mocks.make_txns('d', 'c', 'b'))\\n\\n response = await self.get_assert_200('/transactions?max=2&count=7')\\n controls = Mocks.make_paging_controls(3, start_index=0)\\n self.stream.assert_valid_request_sent(paging=controls)\\n\\n self.assert_has_valid_head(response, 'd')\\n self.assert_has_valid_link(response, '/transactions?head=d&max=2&count=7')\\n self.assert_has_valid_paging(response, paging,\\n '/transactions?head=d&min=3&count=7')\\n self.assert_has_valid_data_list(response, 3)\\n self.assert_txns_well_formed(response['data'], 'd', 'c', 'b')\",\n \"def test_list_zones_max_items_100(shared_zone_test_context):\\n result = shared_zone_test_context.list_zones_client.list_zones(status=200)\\n assert_that(result[\\\"maxItems\\\"], is_(100))\",\n \"def test_query_train_jobs_with_exceeded_limit(self, client):\\n params = dict(offset=0, limit=1000)\\n url = get_url(BASE_URL, params)\\n response = client.get(url)\\n result = response.get_json()\\n assert result.get('error_code') == '50540002'\",\n \"def get_all_from_top_ten(title,users,max = 3):\\n \\\"\\\"\\\" ten prolific users \\\"\\\"\\\"\\n \\\"\\\"\\\" max : number of user with related followers \\\"\\\"\\\"\\n getAllUsers(users,all_users,users_set,proceeded_users,max,user_cpt,title)\\n for data in users_set:\\n print(data.id)\",\n \"def calculate(self, limit):\\n pass\",\n \"def truncate_ocr_sim_list(token, ocr_sims_list, limit=10):\\n if len(ocr_sims_list) <= limit:\\n return ocr_sims_list\\n\\n ocr_scores = set([sc for sim, sc in ocr_sims_list.items()])\\n\\n # Limit of 10 different scores allowed\\n sorted_ocr_scores = sorted(ocr_scores, reverse=True)[:limit]\\n ocr_list = []\\n for score in sorted_ocr_scores:\\n tmp_ocr_list = [ocr_sims for ocr_sims, ocr_score in ocr_sims_list.items() if ocr_score == score]\\n\\n if len(ocr_list) + len(tmp_ocr_list) > limit:\\n list_len = limit - len(ocr_list)\\n tmp_list = []\\n\\n while len(tmp_list) < list_len:\\n tmp_list += select_lower_edit_distance(token, tmp_ocr_list)\\n\\n if len(ocr_list) + len(tmp_list) == limit: # Final list has exactly 10 elements\\n ocr_list += tmp_list\\n break\\n else: # List has more than 10 arguments (need to chose only the n elements needed)\\n alpha_tmp_list = []\\n\\n while len(alpha_tmp_list) != list_len:\\n alpha_word = select_best_alphabetical_word(token, tmp_list)\\n\\n alpha_tmp_list.append(alpha_word)\\n tmp_list = [tkn for tkn in tmp_list if tkn != alpha_word]\\n\\n ocr_list += alpha_tmp_list\\n break\\n elif len(ocr_list) + len(tmp_ocr_list) == limit:\\n ocr_list += tmp_ocr_list\\n break\\n else: # len(ocr_list) + len(tmp_ocr_list) < limit\\n ocr_list += tmp_ocr_list\\n\\n if len(ocr_list) != limit:\\n raise IndexError(\\\"OCR list is still too big (\\\"+str(len(ocr_list))+\\\"/\\\"+str(limit)+\\\")\\\")\\n\\n return {tkn: ocr_sims_list[tkn] for tkn in ocr_list}\",\n \"def get_results(job, limit):\\n reader = results.ResultsReader(job.results(count=limit))\\n return {\\\"results\\\": [row for row in reader]}\",\n \"def max_results(self) -> float:\\n return self._max_results\",\n \"def _ip_list_pagination(ip_list, results_per_page):\\n paged_response = [ip_list[i:i+results_per_page]\\n for i in range(0, len(ip_list), results_per_page)]\\n return paged_response\",\n \"def __get_top(self, result, top=10):\\n result = result.sort_values(by=\\\"bias_score\\\", ascending=False).drop_duplicates(subset='productid', keep=\\\"first\\\")\\n print(result)\\n result = result[:top].sort_values(by=\\\"final_score\\\", ascending=False).productid\\n\\n return list(result)\",\n \"def limit(self, max_size):\\n return self.__class__(itertools.islice(self, max_size))\",\n \"def get_query_results(QueryExecutionId=None, NextToken=None, MaxResults=None):\\n pass\",\n \"def aggregated_results(self, limit=2000) -> List[dict]:\\n stored_events = []\\n for events in self._iter_events():\\n stored_events.extend(events)\\n if len(stored_events) >= limit:\\n return stored_events[:limit]\\n return stored_events\",\n \"def limit(self, limit: int) -> 'Query':\\n self.limit_index = max(1, limit)\\n return self\",\n \"def test_exceed_limit_request(self):\\n actions.login(ADMIN_EMAIL)\\n ids_list = list(range(SkillAggregateRestHandler.MAX_REQUEST_SIZE))\\n get_url = '%s?%s' % (self.URL, urllib.urlencode({\\n 'ids': ids_list}, True))\\n\\n response = transforms.loads(self.get(get_url).body)\\n self.assertEqual(412, response['status'])\",\n \"async def test_txn_list_paginated_by_max_id(self):\\n paging = Mocks.make_paging_response(1, 4, previous_id='d', next_id='a')\\n self.stream.preset_response(\\n head_id='d',\\n paging=paging,\\n transactions=Mocks.make_txns('c', 'b'))\\n\\n response = await self.get_assert_200('/transactions?max=b&count=2')\\n controls = Mocks.make_paging_controls(2, end_id='b')\\n self.stream.assert_valid_request_sent(paging=controls)\\n\\n self.assert_has_valid_head(response, 'd')\\n self.assert_has_valid_link(response, '/transactions?head=d&max=b&count=2')\\n self.assert_has_valid_paging(response, paging,\\n '/transactions?head=d&min=a&count=2',\\n '/transactions?head=d&max=d&count=2')\\n self.assert_has_valid_data_list(response, 2)\\n self.assert_txns_well_formed(response['data'], 'c', 'b')\",\n \"def limit(self, count):\\n self._limit = count\\n return self\",\n \"def getBatchSize(self, context, obj):\\n return 10\",\n \"def test_listingCatalogEntriesWithLimit(self):\\n expected_results = {\\n '': 2,\\n '?limit=1': 2,\\n }\\n\\n for search, expected in expected_results.items():\\n response = self.client.get(\\n '/api/v1/catalogentry/{}'.format(search)\\n )\\n content = response.content.decode('utf8')\\n json_data = json.loads(content)\\n result = json_data['count']\\n\\n self.assertEqual(result, expected)\",\n \"def test_get_remain_limit(self):\\n finder = FinderInsidePro(self.test_key)\\n limit = finder.get_remain_limit()\\n assert isinstance(limit, int)\\n assert limit > 0\",\n \"def get_iterator_results(self, rest_response):\\n full_response = list()\\n full_response += rest_response['resultList']['result']\\n\\n if rest_response.get('count') and int(rest_response.get('count')) > 0:\\n count = rest_response.get('count')\\n max_page_size = rest_response.get('maxPageSize')\\n if int(count) > int(max_page_size):\\n total_iterations = int(math.ceil(count / float(max_page_size)))\\n iterator_id = rest_response.get('id')\\n # We skip to second page as we already have the first page in\\n # the input param rest_response\\n for x in range(1, total_iterations):\\n start = x * max_page_size + 1\\n end = (x + 1) * max_page_size\\n if end > count:\\n end = count\\n full_response += self.get_iterator_page_list(iterator_id,\\n start, end)\\n return full_response\",\n \"def limit(self, limit):\\n self._evaluated = False\\n self._limit = limit\\n return self\",\n \"def test_get_top_respects_limit(self):\\r\\n\\r\\n ranked_3_app = self.create_app_with_contributors(8, 0, name='three')\\r\\n ranked_2_app = self.create_app_with_contributors(9, 0, name='two')\\r\\n ranked_1_app = self.create_app_with_contributors(10, 0, name='one')\\r\\n ranked_4_app = self.create_app_with_contributors(7, 0, name='four')\\r\\n\\r\\n top_apps = cached_apps.get_top(n=2)\\r\\n\\r\\n assert len(top_apps) is 2, len(top_apps)\",\n \"def get_max_item(self):\\n return self._get_page('maxitem').json()\",\n \"def test_result_query_over_10k(cbcsdk_mock):\\n api = cbcsdk_mock.api\\n cbcsdk_mock.mock_request(\\\"POST\\\",\\n \\\"/livequery/v1/orgs/test/runs/run_id/results/_search\\\",\\n GET_RUN_RESULTS_RESP_OVER_10k)\\n result_query = api.select(Result).run_id(\\\"run_id\\\")\\n\\n list(result_query._perform_query())\\n assert result_query._total_results == 10000\",\n \"def test_get_result_top_files(self):\\n pass\",\n \"def maximumAbove(requestContext, seriesList, n):\\n results = []\\n for series in seriesList:\\n if max(series) > n:\\n results.append(series)\\n return results\",\n \"def resultDetectionQuantitationLimit(self) -> List[DetectionQuantitationLimit]:\\r\\n return self.__resultDetectionQuantitationLimit\",\n \"def test_limit(self) -> None:\\n\\n channel = self.make_request(\\n \\\"GET\\\",\\n self.url + \\\"?limit=5\\\",\\n access_token=self.admin_user_tok,\\n )\\n\\n self.assertEqual(200, channel.code, msg=channel.json_body)\\n self.assertEqual(channel.json_body[\\\"total\\\"], 20)\\n self.assertEqual(len(channel.json_body[\\\"event_reports\\\"]), 5)\\n self.assertEqual(channel.json_body[\\\"next_token\\\"], 5)\\n self._check_fields(channel.json_body[\\\"event_reports\\\"])\",\n \"def limit(self, row_count):\\n self._limit = row_count\\n return self\",\n \"def test_result_size_too_large(fxc, endpoint, size=550000):\\n fn_uuid = fxc.register_function(\\n large_result_producer, endpoint, description=\\\"LargeResultProducer\\\"\\n )\\n task_id = fxc.run(\\n size, # This is the current result size limit\\n endpoint_id=endpoint,\\n function_id=fn_uuid,\\n )\\n\\n with pytest.raises(MaxResultSizeExceeded):\\n wait_for_task(fxc, task_id, walltime=10)\",\n \"def test_limit(self):\\n\\n mapper(Item, items)\\n mapper(Order, orders, properties={\\n 'items':relation(Item, secondary=order_items, lazy=False, order_by=items.c.id)\\n })\\n mapper(User, users, properties={\\n 'addresses':relation(mapper(Address, addresses), lazy=False, order_by=addresses.c.id),\\n 'orders':relation(Order, lazy=True)\\n })\\n\\n sess = create_session()\\n q = sess.query(User)\\n\\n if testing.against('mysql'):\\n l = q.limit(2).all()\\n assert fixtures.user_all_result[:2] == l\\n else:\\n l = q.limit(2).offset(1).order_by(User.id).all()\\n print fixtures.user_all_result[1:3]\\n print l\\n assert fixtures.user_all_result[1:3] == l\",\n \"def GetNextBatchOfResults(self) -> typing.List[Repository.Repository]:\\n while True:\\n try:\\n logging.debug('Requesting page %d', self.next_page_num)\\n page = list(self.query.get_page(self.next_page_num))\\n logging.debug('Page %d contains %d results', self.next_page_num,\\n len(page))\\n self.next_page_num += 1\\n return page\\n except github.RateLimitExceededException:\\n logging.debug('Pausing on GitHub rate limit')\\n time.sleep(3)\\n except github.GithubException:\\n # One possible cause for this exception is when trying to request\\n # a page beyond 1000 results, since GitHub only returns the first\\n # 1000 results for a query.\\n return []\",\n \"def request_more_resources():\\n logger.info(\\\"NEED MORE RESOURCES!!!!\\\")\",\n \"def testRetrievingWithoutSpecifyingLimit(self):\\n cached_items = cached_list_logic.getCachedItems('test_list', start=2)\\n self.assertListEqual([self.item3, self.item4, self.item5], cached_items)\",\n \"def test_allowed_result_sizes(fxc, endpoint, size):\\n fn_uuid = fxc.register_function(\\n large_result_producer, endpoint, description=\\\"LargeResultProducer\\\"\\n )\\n task_id = fxc.run(\\n size, # This is the current result size limit\\n endpoint_id=endpoint,\\n function_id=fn_uuid,\\n )\\n\\n x = wait_for_task(fxc, task_id, walltime=10)\\n assert len(x) == size, \\\"Result size does not match excepted size\\\"\",\n \"def test_list_flavors_limit_results(self):\\n response = self.flavors_client.list_flavors(limit=1)\\n flavors = response.entity\\n self.assertEqual(1, len(flavors))\",\n \"def test_collection_limit(testapp):\\n obj1 = {\\n 'title': \\\"Testing1\\\",\\n 'description': \\\"This is testing object 1\\\",\\n }\\n obj2 = {\\n 'title': \\\"Testing2\\\",\\n 'description': \\\"This is testing object 2\\\",\\n }\\n obj3 = {\\n 'title': \\\"Testing3\\\",\\n 'description': \\\"This is testing object 3\\\",\\n }\\n testapp.post_json('/embedding-tests', obj1, status=201)\\n testapp.post_json('/embedding-tests', obj2, status=201)\\n testapp.post_json('/embedding-tests', obj3, status=201)\\n res_all = testapp.get('/embedding-tests/?limit=all', status=200)\\n res_2 = testapp.get('/embedding-tests/?limit=2', status=200)\\n assert len(res_all.json['@graph']) == 3\\n assert len(res_2.json['@graph']) == 2\",\n \"def test_cell_list_limit_positive_num_success(self, mock_list):\\n self.shell('cell-list -r 1 --limit 1')\\n mock_list.assert_called_once_with(limit=1)\",\n \"def option_max_scans_in_ui(self):\\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/view/optionMaxScansInUI/')))\",\n \"def extract_final_max_q(results):\\n return results.loc[results.index[-1], 'max_q_values']\",\n \"def limit(self, row_count, offset=0):\\n self._limit = (row_count, offset)\\n return self\",\n \"def limit(self, limit):\\n self._limit = limit\",\n \"def get_last(self, count):\",\n \"def maximumBelow(requestContext, seriesList, n):\\n\\n result = []\\n for series in seriesList:\\n if max(series) <= n:\\n result.append(series)\\n return result\",\n \"def limit(self, limit):\\n\\n return self._get(\\\"limit\\\", limit, Table)\",\n \"def get_limit(self):\\n return self.limit\",\n \"def nextNumberOfResults(self, N=10):\\n self.start += self.N\\n self.N = N\",\n \"def calc(results: int, current: int, limit=100) -> List[Tuple[int, int, bool, bool, bool]]:\\n\\n # total number of pages, limit is number of result per page\\n pages = results // limit\\n if results % limit > 0:\\n pages += 1\\n\\n result = []\\n current_page = int(current)\\n min_page = max(current_page - PAGING_LINKS + 1, 0)\\n max_page = min(current_page + PAGING_LINKS, pages)\\n\\n for page in range(min_page, max_page):\\n result.append((page,\\n page * limit,\\n # highlight current page marker\\n True if page == current_page else False,\\n # replace first page number with arrow\\n True if min_page >= 1 else False,\\n True if current_page >= pages - PAGING_LINKS else False))\\n if len(result) == 1:\\n return []\\n return result\",\n \"def limit(iterable, n):\\n for count, element in enumerate(iterable):\\n if count >= n: break\\n else: yield element\",\n \"def test_messenger_limit():\\n all_messages_resp = requests.get(BASE_URL)\\n all_messages = all_messages_resp.json()\\n total_message_count = len(all_messages)\\n message_limit = total_message_count // 2\\n\\n query_params = {\\\"limit\\\": message_limit}\\n limit_resp = requests.get(BASE_URL, params=query_params)\\n limited_messages = limit_resp.json()\\n assert limit_resp.status_code == 200\\n assert len(limited_messages) == message_limit\",\n \"def test_limit(self):\\n user_a = self.Person.objects.create(name=\\\"User A\\\", age=20)\\n _ = self.Person.objects.create(name=\\\"User B\\\", age=30)\\n\\n # Test limit on a new queryset\\n people = list(self.Person.objects.limit(1))\\n assert len(people) == 1\\n assert people[0] == user_a\\n\\n # Test limit on an existing queryset\\n people = self.Person.objects\\n assert len(people) == 2\\n people2 = people.limit(1)\\n assert len(people) == 2\\n assert len(people2) == 1\\n assert people2[0] == user_a\\n\\n # Test limit with 0 as parameter\\n people = self.Person.objects.limit(0)\\n assert people.count(with_limit_and_skip=True) == 2\\n assert len(people) == 2\\n\\n # Test chaining of only after limit\\n person = self.Person.objects().limit(1).only(\\\"name\\\").first()\\n assert person == user_a\\n assert person.name == \\\"User A\\\"\\n assert person.age is None\",\n \"def _limit(self, row):\\n if 'counter' not in self._state:\\n init_value = -1 if self._args.head else 0\\n self._state['counter'] = init_value\\n self._state['counter'] += 1\\n if self._state['counter'] >= self._args.limit:\\n self._leave = True\\n return row\",\n \"def test_returns_limit_projects(self):\\n # Arrange\\n # Create and arrange test projects\\n self.arrange_projects()\\n # Act\\n response = self.client.get(\\n f\\\"{self.url}?limit=1\\\", headers={\\\"Authorization\\\": self.user_session_token}\\n )\\n # Assert\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(len(response.json[\\\"results\\\"]), 1)\",\n \"def output_results(self, model, limit):\\n out = []\\n for key in self.results[model].keys():\\n qId = key\\n count = 1\\n sorted_docs = sorted(self.results[model][key].items(), key=operator.itemgetter(1), reverse=True)[:limit] \\n for val in sorted_docs:\\n dId = val[0]\\n dScore = val[1]\\n dRank = count\\n out.append(str(qId) + \\\" \\\" + \\\"Q0\\\" + \\\" \\\" + str(dId) + \\\" \\\" + str(dRank) + \\\" \\\" + str(dScore) + \\\" \\\" + \\\"Exp\\\")\\n count += 1\\n return out\",\n \"def select_recent_queries(max_rows=5):\\n conn = sqlite3.connect(DB_PATH)\\n cursor = conn.cursor()\\n\\n # Insert a row of data\\n cursor.execute(\\\"\\\"\\\"SELECT search\\n FROM query\\n ORDER BY id DESC\\n LIMIT \\\"\\\"\\\" + str(max_rows) + \\\";\\\")\\n\\n results = cursor.fetchall()\\n\\n # We can also close the connection if we are done with it.\\n conn.close()\\n\\n return results\",\n \"def limit(self, variants):\\n return variants[:self.lim]\",\n \"def test_last_page_is_exactly_page_size():\\n # A page of 2 results, then a second with 1 result. The exception should\\n # not be called.\\n def pages(*args, **kwargs):\\n yield json_to_resp([{'id': 1}, {'id': 2}])\\n yield json_to_resp([{'id': 3}, {'id': 4}])\\n\\n # PW returns 200 OK and empty list, not 404, when past last page.\\n yield json_to_resp([])\\n\\n cn = connect(email='foo', token='bar')\\n cn.session.post = mock.Mock(side_effect=pages())\\n rs = ResultSet(resource_cls=IdResource, page_size=2)\\n assert {r.id for r in rs} == {1, 2, 3, 4}\",\n \"def get_more_records(self):\\n\\n\\t\\treturn self.__more_records\",\n \"def limit_size(self, catalog):\\n if len(catalog)<=self.limit:\\n return catalog\\n mem = {}\\n for instance in catalog:\\n if (instance['vCpu'], math.ceil(instance['memory'])) not in mem:\\n mem[(instance['vCpu'], math.ceil(instance['memory']))] = instance\\n out = [val for val in mem.values()]\\n if len(out)>self.limit:\\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\\n return out[:self.limit]\\n return out\",\n \"def testMax(self):\\n top = 10\\n table = self.auth.table(self.dataset, self.table, top=top)\\n record_count = len(table)\\n self.assertLessEqual(record_count, top)\",\n \"def limit(self):\\n return self._limit\",\n \"def limit(self):\\n return self._limit\",\n \"def test_max_features_paged(\\n self, mp_wfs, mp_get_schema, mp_remote_describefeaturetype,\\n mp_wfs_max_features, mp_remote_wfs_paged_feature):\\n s = BoringSearch()\\n df = s.search(return_fields=['pkey_boring'], max_features=15)\\n assert len(df) == 15\",\n \"def where_above(lst, limit):\\n return [x for x in lst if x > limit]\",\n \"def test_exceeded_limit(self):\\n msg=self.sample_data(\\\"error_exceeded_limit.xml\\\")\\n error = ErrorParser().process_all(msg)\\n assert isinstance(error, PatronLoanLimitReached)\\n eq_(u'Patron cannot loan more than 12 documents', error.message)\",\n \"def test_limit(db_session):\\n query_params = {\\\"limit\\\": \\\"1\\\"}\\n parser = ModelQueryParamParser(query_params)\\n album_resource = AlbumResource(session=db_session)\\n offset_limit_info = parser.parse_offset_limit(page_max_size=30)\\n offset = offset_limit_info.offset\\n limit = offset_limit_info.limit\\n result = album_resource.get_collection(\\n filters=parser.parse_filters(album_resource.model),\\n sorts=parser.parse_sorts(),\\n limit=limit,\\n offset=offset\\n )\\n assert len(result) == 1\",\n \"def getBatchSize(self, context, obj):\\n return 100\",\n \"def testQueryWithLimit(self):\\n for i in range(11):\\n row_name = \\\"aff4:/row:%02d\\\" % i\\n data_store.DB.Set(row_name, \\\"metadata:5\\\", i, token=self.token)\\n data_store.DB.Set(row_name, \\\"aff4:type\\\", \\\"test\\\", token=self.token)\\n\\n # Retrieve all subjects with prefix row1:\\n rows = [row for row in data_store.DB.Query(\\n [], data_store.DB.filter.HasPredicateFilter(\\\"metadata:5\\\"),\\n subject_prefix=\\\"aff4:/row:\\\", limit=(2, 3), token=self.token)]\\n\\n self.assertEqual(len(rows), 3)\\n self.assertEqual(rows[0][\\\"subject\\\"][0][0], \\\"aff4:/row:02\\\")\\n self.assertEqual(rows[1][\\\"subject\\\"][0][0], \\\"aff4:/row:03\\\")\\n self.assertEqual(rows[2][\\\"subject\\\"][0][0], \\\"aff4:/row:04\\\")\",\n \"def limit(self, limit):\\n self._limit = limit\\n return self\",\n \"def extract_q_values(results):\\n return results['max_q_values'].tolist()\",\n \"def queryWithResultSize(self, query_str, result_size):\\n try:\\n conn = MySQLdb.connect(**self.connect)\\n cursor = conn.cursor()\\n cursor.execute(query_str)\\n result = cursor.fetchmany(result_size)\\n conn.commit()\\n conn.close()\\n return result\\n except Exception, e:\\n logger.error(\\\"mysql util error:\\\" + str(e))\",\n \"def get_top(n=10):\\r\\n sql = text('''SELECT \\\"user\\\".id, \\\"user\\\".name, \\\"user\\\".fullname, \\\"user\\\".email_addr,\\r\\n \\\"user\\\".created, \\\"user\\\".info, COUNT(task_run.id) AS task_runs FROM task_run, \\\"user\\\"\\r\\n WHERE \\\"user\\\".id=task_run.user_id GROUP BY \\\"user\\\".id\\r\\n ORDER BY task_runs DESC LIMIT :limit''')\\r\\n results = db.engine.execute(sql, limit=n)\\r\\n top_users = []\\r\\n for row in results:\\r\\n user = dict(id=row.id, name=row.name, fullname=row.fullname,\\r\\n email_addr=row.email_addr,\\r\\n created=row.created,\\r\\n task_runs=row.task_runs,\\r\\n info=dict(json.loads(row.info)))\\r\\n top_users.append(user)\\r\\n return top_users\",\n \"def get_max_rows_per_partition() -> int:\\n pass\",\n \"def limit(self, limit):\\n if limit is None:\\n return self\\n\\n self.query = self.query.limit(limit)\\n self._has_limit = True\\n return self\",\n \"def limit(self, limit):\\n params = join_params(self.parameters, {\\\"limit\\\": limit,\\n \\\"url_domain\\\": self.url_domain,\\n \\\"proxies\\\": self.proxies})\\n\\n return self.__class__(**params)\",\n \"def test_queryset_limit_immutability(self):\\r\\n query1 = TestModel.objects(test_id=5).limit(1)\\r\\n assert query1._limit == 1\\r\\n\\r\\n query2 = query1.filter(expected_result__gte=1)\\r\\n assert query2._limit == 1\\r\\n\\r\\n query3 = query1.filter(expected_result__gte=1).limit(2)\\r\\n assert query1._limit == 1\\r\\n assert query3._limit == 2\",\n \"def apply_limit(self, entrylist):\\r\\n\\r\\n entryset = set(entrylist)\\r\\n limitset = set(self.limitlist)\\r\\n if not limitset:\\r\\n limitset = set(self.indexes())\\r\\n if isinstance(entrylist, set):\\r\\n return entryset.intersection(limitset)\\r\\n if isinstance(entrylist, list):\\r\\n return list(entryset.intersection(limitset))\\r\\n return list(entryset.intersection(limitset))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.673919","0.64389324","0.62612617","0.6195268","0.6166678","0.6131141","0.60837257","0.60630274","0.6061646","0.60587096","0.6015139","0.59791905","0.59546673","0.59319866","0.5929677","0.59154874","0.58818","0.58628124","0.5826882","0.58234286","0.58110976","0.5786281","0.57563883","0.5704733","0.56980354","0.5687688","0.567735","0.5620682","0.5611376","0.5610186","0.5605929","0.5570098","0.5566008","0.5554088","0.55408514","0.5530538","0.5525333","0.5521776","0.5519961","0.55137783","0.54977053","0.5486468","0.54785687","0.5478112","0.5454873","0.5454623","0.5451685","0.5449529","0.54449266","0.5439883","0.54340106","0.54269594","0.54212195","0.54152703","0.5391003","0.5382439","0.5372253","0.5369293","0.5353321","0.5348409","0.534362","0.5332915","0.5321691","0.53203124","0.5316087","0.5315555","0.5307555","0.5304485","0.5301156","0.53009516","0.5294808","0.5286296","0.527092","0.5270463","0.52690834","0.52658814","0.5265833","0.5264647","0.5263671","0.52586734","0.5256657","0.5252831","0.52469337","0.5244337","0.5244337","0.5235068","0.5233825","0.52320194","0.522414","0.52212197","0.52169514","0.52159256","0.520808","0.52035844","0.5201861","0.51952344","0.51898956","0.51898277","0.5184121","0.5178397"],"string":"[\n \"0.673919\",\n \"0.64389324\",\n \"0.62612617\",\n \"0.6195268\",\n \"0.6166678\",\n \"0.6131141\",\n \"0.60837257\",\n \"0.60630274\",\n \"0.6061646\",\n \"0.60587096\",\n \"0.6015139\",\n \"0.59791905\",\n \"0.59546673\",\n \"0.59319866\",\n \"0.5929677\",\n \"0.59154874\",\n \"0.58818\",\n \"0.58628124\",\n \"0.5826882\",\n \"0.58234286\",\n \"0.58110976\",\n \"0.5786281\",\n \"0.57563883\",\n \"0.5704733\",\n \"0.56980354\",\n \"0.5687688\",\n \"0.567735\",\n \"0.5620682\",\n \"0.5611376\",\n \"0.5610186\",\n \"0.5605929\",\n \"0.5570098\",\n \"0.5566008\",\n \"0.5554088\",\n \"0.55408514\",\n \"0.5530538\",\n \"0.5525333\",\n \"0.5521776\",\n \"0.5519961\",\n \"0.55137783\",\n \"0.54977053\",\n \"0.5486468\",\n \"0.54785687\",\n \"0.5478112\",\n \"0.5454873\",\n \"0.5454623\",\n \"0.5451685\",\n \"0.5449529\",\n \"0.54449266\",\n \"0.5439883\",\n \"0.54340106\",\n \"0.54269594\",\n \"0.54212195\",\n \"0.54152703\",\n \"0.5391003\",\n \"0.5382439\",\n \"0.5372253\",\n \"0.5369293\",\n \"0.5353321\",\n \"0.5348409\",\n \"0.534362\",\n \"0.5332915\",\n \"0.5321691\",\n \"0.53203124\",\n \"0.5316087\",\n \"0.5315555\",\n \"0.5307555\",\n \"0.5304485\",\n \"0.5301156\",\n \"0.53009516\",\n \"0.5294808\",\n \"0.5286296\",\n \"0.527092\",\n \"0.5270463\",\n \"0.52690834\",\n \"0.52658814\",\n \"0.5265833\",\n \"0.5264647\",\n \"0.5263671\",\n \"0.52586734\",\n \"0.5256657\",\n \"0.5252831\",\n \"0.52469337\",\n \"0.5244337\",\n \"0.5244337\",\n \"0.5235068\",\n \"0.5233825\",\n \"0.52320194\",\n \"0.522414\",\n \"0.52212197\",\n \"0.52169514\",\n \"0.52159256\",\n \"0.520808\",\n \"0.52035844\",\n \"0.5201861\",\n \"0.51952344\",\n \"0.51898956\",\n \"0.51898277\",\n \"0.5184121\",\n \"0.5178397\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7924585"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74583,"cells":{"query":{"kind":"string","value":"Get an Github Operator"},"document":{"kind":"string","value":"def _github_operator(self, msg):\n token = self[msg.frm.person + \"github_token\"]\n result = GithubOperator(token)\n return result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_github_credendial(cls) -> 'ApiCredential':\n return cls.select_token_for_api(GITHUB_API_NAME)","def get(self, endpoint, params=None):\n res = requests.get(\"https://api.github.com/\" + endpoint,\n auth=requests.auth.HTTPBasicAuth(self.credentials['username'], self.credentials['token']),\n params=params)\n return res.json()","def githubclient(token):\n return github.Github(token)","def _get_github_cred(github_login=None):\n cred_identity = \"%s@github\" % github_login if github_login else \"github\"\n return Token(cred_identity, GITHUB_TOKENS_URL)","def get_pull_request(project, num, github_api=3):\r\n if github_api==2 :\r\n url = \"http://github.com/api/v2/json/pulls/{project}/{num}\".format(project=project, num=num)\r\n elif github_api == 3:\r\n url = \"https://api.github.com/repos/{project}/pulls/{num}\".format(project=project, num=num)\r\n response = requests.get(url)\r\n response.raise_for_status()\r\n if github_api == 2 :\r\n return json.loads(response.text)['pull']\r\n return json.loads(response.text)","def network_get(auth=None, **kwargs):\n cloud = get_operator_cloud(auth)\n kwargs = _clean_kwargs(**kwargs)\n return cloud.get_network(**kwargs)","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> Optional[str]:\n return pulumi.get(self, \"operator\")","def get_pr_info(num):\r\n url = \"https://api.github.com/repos/edx/edx-platform/pulls/{num}\".format(num=num)\r\n username, token = get_github_creds()\r\n headers = {\r\n \"Authorization\": \"token {}\".format(token),\r\n \"User-Agent\": \"edx-release\",\r\n }\r\n response = requests.get(url, headers=headers)\r\n result = response.json()\r\n if not response.ok:\r\n raise requests.exceptions.RequestException(result[\"message\"])\r\n return result","def github_link(self):\n if self.test_type == TestType.commit:\n test_type = 'commit'\n test_id = self.commit\n else:\n test_type = 'pull'\n test_id = self.pr_nr\n\n return f\"{self.fork.github_url}/{test_type}/{test_id}\"","def operator(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"operator\")","def operator(self) -> str:\n return pulumi.get(self, \"operator\")","def operator(self) -> str:\n return pulumi.get(self, \"operator\")","def github(auth=None) -> Github:\n if auth is None:\n token = conf('github.token', None)\n if token:\n return Github(token)\n username = conf('github.username')\n password = conf('github.password', env='MAESTRO_GITHUB_PASSWORD')\n return github((username, password))\n\n if isinstance(auth, (tuple, list)):\n return Github(*auth)\n elif isinstance(auth, str):\n return Github(auth)\n\n raise NotImplementedError","def query_repo_tip(repo_url):\n url = \"%s?tipsonly=1\" % (JSON_PUSHES % {\"repo_url\": repo_url})\n recent_commits = retry(requests.get, args=(url,)).json()\n tip_id = sorted(recent_commits.keys())[-1]\n return Push(push_id=tip_id, push_info=recent_commits[tip_id])","def get_symbol(operator):\r\n if isinstance(operator, AST):\r\n operator = type(operator)\r\n try:\r\n return ALL_SYMBOLS[operator]\r\n except KeyError:\r\n raise LookupError('no known symbol for %r' % operator)","def _get_repo(self, owner, repo):\n url = f\"{BASE_URL}/repos/{owner}/{repo}\"\n status, data, _ = self.get(url)\n if (status == 200):\n return data\n else:\n log.warning(\"GHUB\", f\"Unexpected status code {status} for request {url}.\")","def connect_to_github():\n\n # Authentication\n from os.path import isfile\n if isfile(\"github-logins.json\"):\n with open(\"github-logins.json\", \"r\") as loginfile:\n logins = json.load(loginfile)\n gh = login(username=logins[\"username\"], password=logins[\"password\"])\n else:\n from getpass import getpass\n password = getpass()\n gh = login(username=\"yourusername\", password=password)\n\n # Connect to the repo\n repo = gh.repository(\"ghostofgoes\", \"botnet-example\")\n branch = repo.branch(\"master\")\n return gh, repo, branch","def get_github_equivalent(self):\n\n if self.description:\n match_obj1 = re.search(r'(?<=Repository Name: )(.*?)(?={color})', self.description)\n match_obj2 = re.search(r'(?<=Issue Number: )(.*?)(?={color})', self.description)\n match_obj3 = re.search(r'(?<=Milestone: )(.*?)(?={color})', self.description)\n match_obj4 = re.search(r'(?<=github.com/)(.*?)(?=/)', self.description)\n if not any([match_obj1, match_obj2, match_obj3, match_obj4]):\n logging.warning(f'No GitHub link information was found in the description of issue {self.jira_key}')\n self.github_repo = match_obj1.group(0) if match_obj1 else None\n self.github_key = match_obj2.group(0) if match_obj2 else None\n self.milestone_name = match_obj3.group(0) if match_obj3 else None\n self.github_org = match_obj4.group(0) if match_obj4 else None","def get_link(\n self,\n operator: BaseOperator,\n *,\n ti_key: TaskInstanceKey,\n ) -> str:\n conn = BaseHook.get_connection(\n getattr(operator, \"qubole_conn_id\", None)\n or operator.kwargs[\"qubole_conn_id\"] # type: ignore[attr-defined]\n )\n if conn and conn.host:\n host = re.sub(r\"api$\", \"v2/analyze?command_id=\", conn.host)\n else:\n host = \"https://api.qubole.com/v2/analyze?command_id=\"\n qds_command_id = XCom.get_value(key=\"qbol_cmd_id\", ti_key=ti_key)\n url = host + str(qds_command_id) if qds_command_id else \"\"\n return url","def github(self):\n from nudgebot.thirdparty.github.user import User\n return User.instantiate(self.config_data.github_login)","def get_github_credentials():\n\n p = subprocess.Popen(\"git config github.accesstoken\",\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n rc = p.wait()\n if rc:\n # failure to get config, so return silently\n return None\n token = p.stdout.readline().strip()\n if token:\n log.debug(\"Found github accesstoken %r\", token)\n return token","def repo_info():\n return TEST_REPOS_INFO[0]","def fetch_operators_priority(self, operator):\n priority = self.operators_dict[operator]['priority']\n return priority","def get_op(self, op_complete_url):\n url_parsed = urlsplit(op_complete_url)\n op_url = url_parsed.path\n\n conf, op = self.best_match(op_url)\n if op is not None:\n return Operation(\n op_complete_url,\n op,\n conf[\"conf\"][op],\n conf[\"tp\"],\n conf[\"sparql_http_method\"],\n conf[\"addon\"],\n )\n else:\n sc = 404\n return (\n sc,\n \"HTTP status code %s: the operation requested does not exist\" % sc,\n \"text/plain\",\n )","def get_read_token(repo_names: List[str]) -> None:\n github_app = get_default_app()\n print(github_app.get_read_token(repo_names))","def fetch_operators_function(self, operator):\n operators_function = self.operators_dict[operator]['function']\n return operators_function","def github(code, input):\n syntax = 'Syntax: \\'.github <user|user/repo>\\''\n failed = 'Failed to get data from Githubs API :('\n if len(input.group(2).strip().split()) != 1:\n return code.say(syntax)\n\n spacer = ' {blue}|{c} '\n\n if '/' not in input.group(2):\n # Assume a single username\n try:\n tmp = web.json(user_api % input.group(2).strip())\n response = {}\n # Remove dem ugly nulled values. It's a dictionary so we have to\n # loop differently.\n for key, value in tmp.iteritems():\n if value != '' or len(value) != 0 or value != 'null':\n response[key] = value\n print response\n except:\n return code.say(failed)\n if 'message' in response:\n # Assume failed\n return code.say(failed)\n\n # Here is where we build the response\n output = []\n if 'name' in response:\n output.append('%s (%s)' % (response['name'], response['login']))\n else:\n output.append(response['login'])\n if 'location' in response:\n output.append(response['location'])\n if 'email' in response:\n output.append(response['email'])\n if 'public_repos' in response:\n output.append('%s Repos' % response['public_repos'])\n if 'followers' in response:\n output.append('%s Followers' % response['followers'])\n if 'following' in response:\n output.append('Following %s' % response['following'])\n if 'public_gists' in response:\n output.append('%s Gists' % response['public_gists'])\n if 'html_url' in response:\n output.append(response['html_url'])\n\n return code.say(spacer.join(output))\n\n else:\n # Assume Username/Repo\n try:\n response = jweb.json(repo_api % input.group(2).strip())\n except:\n return code.say(failed)\n if 'message' in response:\n # Assume failed\n return code.say(failed)\n # Here is where we build the response\n output = []\n output.append('%s (%s)' %\n (response['name'], response['owner']['login']))\n output.append(response['description'])\n output.append('%s %s' % (response['stargazers_count'], u'\\u2605'))\n output.append('%s %s' % (response['watchers_count'], u'\\u231A'))\n output.append('%s %s' % (response['forks_count'], u'\\u2442'))\n output.append('%s %s' % (response['open_issues_count'], u'\\u2602'))\n output.append('%s %s' % (response['network_count'], u'\\U0001F46C'))\n output.append('%s %s' % (response['subscribers_count'], u'\\u2764'))\n output.append(response['html_url'])\n return code.say(spacer.join(output))","def _get_odl_info(pop_url, pop_id):\n try:\n graph_db = neo4j.Graph(pop_url)\n index = ('pop', 'uuid', pop_id)\n pop = neo_resource.get_node(graph_db, index)\n if pop:\n properties = dict(pop.properties)\n if 'occi.epa.pop.odl_url' in properties and 'occi.epa.pop.odl_name' in properties \\\n and 'occi.epa.pop.odl_password' in properties:\n return properties['occi.epa.pop.odl_url'], properties['occi.epa.pop.odl_name'],\\\n properties['occi.epa.pop.odl_password']\n\n except Exception:\n raise HTTPError(404, 'Error connecting to graph_url: ' + str(pop_url))\n raise HTTPError(404, 'Resource not found: Epa-Pop-Id: ' + str(pop_id))","def test_fetch_valid_github_repo(self):\n url = 'https://github.com/ivacf/archi'\n repo = GitHubRepoFetcher().fetch(url)\n self.assertEqual('archi', repo['name'])","def get(self, endpoint, page=1, params=None):\n url = 'https://api.github.com/%(endpoint)s?access_token=%(token)s&page=%(page)d' % {\n 'endpoint': endpoint,\n 'token': self.access_token,\n 'page': page,\n }\n if params is not None:\n url += '&' + urlencode(params)\n response = requests.get(url)\n\n # Produce specific error on 404. Generic HTTPError otherwise.\n if response.status_code == 404:\n raise ResourceNotFound\n response.raise_for_status()\n\n return response.json()","def get_repository(post):\n pattern = re.compile(constants.REPOSITORY_REGEX)\n if \"links\" in post.json_metadata.keys():\n for link in post.json_metadata[\"links\"]:\n if link.startswith(\"/exit?url=\"):\n link = link[len(\"/exit?url=\"):]\n\n try:\n result = pattern.search(link).group(0)\n return result\n except AttributeError:\n continue\n else:\n for line in post.body.split():\n try:\n result = pattern.search(line).group(0)\n return result\n except AttributeError:\n continue\n\n return \"\"","def get_label(issue_id):\n auth = subprocess.check_output(\n \"gsutil cat\"\n \" gs://bazel-trusted-encrypted-secrets/github-trusted-token.enc |\"\n \" gcloud kms decrypt --project bazel-public --location global\"\n \" --keyring buildkite --key github-trusted-token --ciphertext-file\"\n \" - --plaintext-file -\", shell=True\n ).decode(\"utf-8\").strip().split(\"\\n\")[0]\n headers = {\n \"Authorization\": \"Bearer \" + auth,\n \"Accept\": \"application/vnd.github+json\",\n }\n response = requests.get(\n \"https://api.github.com/repos/bazelbuild/bazel/issues/\"\n + issue_id + \"/labels\", headers=headers,\n )\n for item in response.json():\n for key, value in item.items():\n if key == \"name\" and \"team-\" in value:\n return value.strip()\n return None","async def pr(ctx, number: Option(int, \"Pull request number\")):\n url = f\"{repo}/issues/{number}\"\n view = discord.ui.View()\n view.add_item(discord.ui.Button(label=\"View Pull Request\", url=url))\n await ctx.respond(f\"Here's a link\", view=view)","def opid(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"opid\")","def current_github_repo(remote='origin'):\n\n proc = run(['git', 'remote', 'get-url', remote], stdout=PIPE)\n url = proc.stdout.strip().decode('utf-8')\n\n if 'github.com' in url:\n match = re.search('([^:/]+)/([^/]+)\\.git$', url)\n org = match.group(1)\n repo = match.group(2)\n return (org, repo)\n else:\n return None","def get_operation(self, project, operation_id):\n headers = {'AppScale-Secret': self.secret}\n operation_url = '{prefix}/{project}/operations/{operation_id}'.format(\n prefix=self.prefix, project=project, operation_id=operation_id)\n response = requests.get(operation_url, headers=headers, verify=False)\n return self.extract_response(response)","async def github(self, ctx):\n await ctx.send('https://github.com/nick411077/nickcan_bot')","def get_operators(self):\n url = self.config['links']['accountAPI'] + 'operators'\n params = {\n 'client': self.client,\n 'country_code': self.locale_suffix\n }\n data = self.make_request(url, 'get', params=params)\n\n return data['data']['operators']","def _get_pull_request(repo, objid):\n if not repo.settings.get(\"pull_requests\", True):\n raise PagureEvException(\n \"No pull-request tracker found for this project\"\n )\n\n session = _get_session()\n request = pagure.lib.query.search_pull_requests(\n session, project_id=repo.id, requestid=objid\n )\n\n if request is None or request.project != repo:\n raise PagureEvException(\"Pull-Request '%s' not found\" % objid)\n\n return request","def button_github(args):\n cell_source = args[\"cell_source\"]\n repo = get_arg_or_fail(args[\"user\"], \"repo\", \"<org/name>\")\n branch = args[\"user\"].get(\"branch\", \"master\")\n docs_dir, rel_path = split_doc_path(args[\"path\"])\n\n # Buttons use OSS URLs.\n if str(docs_dir) == \"g3doc/en\":\n docs_dir = pathlib.Path(\"site/en\")\n\n base_url = f\"github.com/{repo}/blob/{branch}\"\n this_url = \"https://\" + str(base_url / docs_dir / rel_path)\n\n if is_button_cell_re.search(cell_source) and cell_source.find(this_url) != -1:\n return True\n else:\n fail(\n f\"GitHub button URL doesn't match: {this_url}\",\n fix=fix.regex_between_groups_replace_all,\n fix_args=[r\"(href.*)http.*?(\\\\\\\".*GitHub-Mark-32px.png)\", this_url])","def __repr__(self):\n return 'GithubCollector()'","def get_op(ring_size: int, op_str: str) -> Callable[..., Any]:\n ops = RING_SIZE_TO_OP.get(ring_size, None)\n\n if ops is None:\n raise ValueError(f\"Do not have operations for ring size {ring_size}\")\n\n op = ops.get(op_str, None)\n if op is None:\n raise ValueError(\n f\"Operator {op_str} does not exist for ring size {ring_size}\"\n )\n\n return op","def _get_client(self):\n return Github(\n base_url=github_trigger.api_endpoint(),\n login_or_token=self.auth_token if self.auth_token else github_trigger.client_id(),\n password=None if self.auth_token else github_trigger.client_secret(),\n timeout=5,\n )","def get_info(repos):\n info = \"labelord application is master-to-master application for label replication using webhook for GitHub<br>\"\n for i in repos:\n info += i + ' ' + repo_link(i) + '<br>'\n return info","def get_operation(project_id: str, region: str, operation_id: str) -> Operation:\n return get_operation_by_name(\n f\"projects/{project_id}/locations/{region}/operations/{operation_id}\"\n )","def get_own_repo():\n own_repo = GitClass(name='self', url='https://github.com/meganhmoore/github-api-covid-data', owner='meganhmoore',\n repo='github-api-covid-data', branch='develop/new_data')\n return own_repo","def op(self) -> str:\n return self._node.get(\"op\")","def get_operagion(self):\n if self.OP_GID not in self._data_dict:\n return None\n return dao.get_operation_by_gid(self._data_dict.get(self.OP_GID, None))","def get_issue(issue_number):\n backend_name = os.environ[\"ISSUE_BACKEND\"]\n backend_module = importlib.import_module(\n \"issuebranch.backends.{}\".format(backend_name)\n )\n\n return getattr(backend_module, \"Backend\")(issue_number)","def image_get(auth=None, **kwargs):\n cloud = get_operator_cloud(auth)\n kwargs = _clean_kwargs(**kwargs)\n return cloud.get_image(**kwargs)","def get_operator(self):\n distribution = self.get_distribution_operator()\n temp = self.get_unit_conversion_operator()\n aperture = self.get_aperture_integration_operator()\n filter = self.get_filter_operator()\n projection = self.get_projection_operator()\n hwp = self.get_hwp_operator()\n polarizer = self.get_polarizer_operator()\n integ = self.get_detector_integration_operator()\n trans_inst = self.instrument.get_transmission_operator()\n trans_atm = self.scene.atmosphere.transmission\n response = self.get_detector_response_operator()\n\n with rule_manager(inplace=True):\n H = CompositionOperator([\n response, trans_inst, integ, polarizer, hwp * projection,\n filter, aperture, trans_atm, temp, distribution])\n if self.scene == 'QU':\n H = self.get_subtract_grid_operator()(H)\n return H","def getPullRequest(upstream, ref, user=None, fork=None, target=None):\r\n\r\n if user is not None:\r\n user = user.login\r\n\r\n for p in upstream.get_pulls():\r\n # Check candidate request against specified criteria\r\n if p.head.ref != ref:\r\n continue\r\n\r\n if user is not None and p.head.user.login != user:\r\n continue\r\n\r\n if fork is not None and p.head.repo.url != fork.url:\r\n continue\r\n\r\n if target is not None and p.base.ref != target:\r\n continue\r\n\r\n # If we get here, we found a match\r\n return p\r\n\r\n # No match\r\n return None","def gitHubConnect():\n return HUB","def test_github_explore(self):\n python_trending_url = \"https://github.com/trending/python\"\n driver = self.driver\n driver.get(self.base_url)\n search_box = driver.find_element_by_link_text(\"Explore\")\n search_box.send_keys(Keys.RETURN)\n time.sleep(2)\n trending_elem = driver.find_element_by_partial_link_text(\"Trending\")\n trending_elem.send_keys(Keys.ENTER)\n time.sleep(2)\n python_lang = driver.find_element_by_link_text(\"Python\")\n python_lang.send_keys(Keys.ENTER)\n time.sleep(2)\n assert python_trending_url == driver.current_url","def get_github_credentials():\n\n p = subprocess.Popen(\"git config github.token\",\n shell=True,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n rc = p.wait()\n if rc:\n # failure to get config, so return silently\n return None\n token = p.stdout.readline().strip()\n pipe = subprocess.Popen(\"git config github.user\",\n shell=True,\n stdout=subprocess.PIPE).stdout\n login = pipe.readline().strip()\n pipe.close()\n if login and token:\n log.debug(\"Found github credentials for user %r\", login)\n return login, token","def api_repo_get(access_key):\n repo = Repo.query.get(access_key)\n if not repo:\n return jsonify(error=\"Repo not found\"), 404\n \n if repo.is_private and 'working_repo' not in session:\n return jsonify(error=\"Unauthorized\"), 401\n elif repo.is_private and session['working_repo'] != repo.access_key:\n return jsonify(error=\"Unauthorized\"), 403\n elif repo.is_private and session['working_repo'] == repo.access_key:\n return jsonify(repo.to_json())\n else:\n return jsonify(repo.to_json())","def test_get_github_repos_info_positive(self):\n self.assertIsNotNone(app.get_github_repos_info(\"dhh\")[\"repo_info\"])","async def github_repo_info(self, ctx: commands.Context, *repo: str) -> None:\n repo = \"/\".join(repo)\n if repo.count(\"/\") != 1:\n embed = discord.Embed(\n title=random.choice(NEGATIVE_REPLIES),\n description=\"The repository should look like `user/reponame` or `user reponame`.\",\n colour=Colours.soft_red\n )\n\n await ctx.send(embed=embed)\n return\n\n async with ctx.typing():\n repo_data = await self.fetch_data(f\"{GITHUB_API_URL}/repos/{quote(repo)}\")\n\n # There won't be a message key if this repo exists\n if \"message\" in repo_data:\n embed = discord.Embed(\n title=random.choice(NEGATIVE_REPLIES),\n description=\"The requested repository was not found.\",\n colour=Colours.soft_red\n )\n\n await ctx.send(embed=embed)\n return\n\n embed = discord.Embed(\n title=repo_data[\"name\"],\n description=repo_data[\"description\"],\n colour=discord.Colour.blurple(),\n url=repo_data[\"html_url\"]\n )\n\n # If it's a fork, then it will have a parent key\n try:\n parent = repo_data[\"parent\"]\n embed.description += f\"\\n\\nForked from [{parent['full_name']}]({parent['html_url']})\"\n except KeyError:\n log.debug(\"Repository is not a fork.\")\n\n repo_owner = repo_data[\"owner\"]\n\n embed.set_author(\n name=repo_owner[\"login\"],\n url=repo_owner[\"html_url\"],\n icon_url=repo_owner[\"avatar_url\"]\n )\n\n repo_created_at = datetime.strptime(repo_data[\"created_at\"], \"%Y-%m-%dT%H:%M:%SZ\").strftime(\"%d/%m/%Y\")\n last_pushed = datetime.strptime(repo_data[\"pushed_at\"], \"%Y-%m-%dT%H:%M:%SZ\").strftime(\"%d/%m/%Y at %H:%M\")\n\n embed.set_footer(\n text=(\n f\"{repo_data['forks_count']} ⑂ \"\n f\"• {repo_data['stargazers_count']} ⭐ \"\n f\"• Created At {repo_created_at} \"\n f\"• Last Commit {last_pushed}\"\n )\n )\n\n await ctx.send(embed=embed)","async def get_issue(self, issue: int) -> \"AIOGitHubAPIRepositoryIssue\":\n _endpoint = f\"/repos/{self.full_name}/issues/{issue}\"\n\n response = await self.client.get(endpoint=_endpoint)\n return AIOGitHubAPIRepositoryIssue(self.client, response)","def owner_project_from_github_url(url):\n if not re.match('https://github.com/([a-zA-Z0-9-_]*)/[a-zA-Z0-9-_]*', url):\n print(str(url) + \"is not a valid url!\")\n exit(-1)\n elements = url.split('/')\n project_name = elements[-1]\n organization_name = elements[-2]\n return (organization_name, project_name)","def test_grover_operator_getter(self):\n oracle = QuantumCircuit(2)\n oracle.cz(0, 1)\n grover = Grover(oracle=oracle, good_state=[\"11\"])\n constructed = grover.grover_operator\n expected = GroverOperator(oracle)\n self.assertTrue(Operator(constructed).equiv(Operator(expected)))","def get_interface(self, operator_token):\n if operator_token not in self.interfaces:\n auth_token = random_token()\n self.redis.publish('authentication', json.dumps((operator_token, auth_token)))\n interface = OperatorInterface(operator_token)\n self.authentications[auth_token] = interface\n self.interfaces[operator_token] = interface\n return interface","def main():\n\n default_token = os.getenv('GITHUB_API_TOKEN')\n\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=\"\"\"\nShow PRs or a specific PR\n\n github-pr list -r dataxu/test_repo\n github-pr list -r dataxu/test_repo -n 17\n\n - Filters - can be used alone or together\n --filters\n * owner - This will return a list of PRs from the repo that are owned by the github-user\n * status - returns PRs with a specifc status, one of (success, failure, error, pending)\n * label - returns PRs with a specifc label\n * comment - returns PRs that have at least one comment matching a given string\n github-pr list -r dataxu/test_repo --filters 'filter1_name=filter1_value,filter2_name=filter2_value'\n\n github-pr list -r dataxu/test_repo --filters 'owner=frankenstein,status=success,comment=:pitchfork:'\n This returns the number of all PRs in the given repo owned by frankenstein with the status success and containing any comments that have \":pitchfork:\"\n\nCreate a PR\n\n github-pr create -r dataxu/test_repo -t \"PR Title\" --head \"my-test-branch\" --body 'Description Line 1<br/>Line2'\n\nCreate a PR from a fork\n\n github-pr create -r dataxu/test_repo -t \"PR Title\" --head \"my-fork:my-test-branch\"\n\nComment on a PR\n\n github-pr comment -r dataxu/test_repo -n 17 --body \":shipit:\"\n\nMerge a PR by PR number\n\n github-pr merge -r dataxu/test_repo -n 17\n\n github-pr merge -r dataxu/test_repo -n 17 --condition-non-owner-merger --condition-approved-mergers-file=.approved-mergers-file\n This conditional option allows merges to go through checks that validate ownership and team hierarchy.\n ie.\n --condition-approved-mergers user1 user2 user3\n This takes a SPACE-separated list, without quotes or braces\n or\n --condition-approved-mergers-file=.approved-mergers-file\n OPTIONAL: --mergecomment can be set to a different string to search for instead of \":shipit:\"\n\nMerge a PR by branch\n\n github-pr merge -r dataxu/test_repo --head dev-my-branch-name\n github-pr merge -r dataxu/test_repo --head dev-another-branch --base branch-that-is-not-master\n\nDelete a PR\n\n github-pr delete -r dataxu/test_repo -n 17\n\nCheck conditional status checks\n !!!This check only looks at comments AFTER the latest commit, to validate that the\n most recent code (most recent git sha pushed to the PR) has been peer reviewed!!!\n\n github-pr check-condition -r dataxu/dcommand -n 84 --condition-non-owner-merger\n This will check to make sure that the owner can not apply a shipable comment on their own code\n github-pr check-condition -r dataxu/dcommand -n84 --condition-approved-mergers-file=<MAINTAINERS FILE>\n This takes the path to the MAINTAINERS file inside the repo\n Compares commenter to list from a file, single user per line, and checks to make sure they are an approved merger\n github-pr check-condition -r dataxu/dcommand -n 84 --condition-approved-mergers ned_flanders marge_simpson\n This takes a SPACE-separated list, without quotes or braces\n This will check that a comment containing a :shipit: (the default, or other defined comment if merge_comment is set)\n comes from a user in the provided list passed on the commandline. The \"MAINTAINERS file\" option above is the preferred\n convention to use, while this passing the list on the commandline option is primarily for local testing\n when setting up your CD flow.\n \"\"\")\n parser.add_argument('action', choices=['create', 'list', 'merge', 'comment', 'delete', 'update', 'check-condition'], help='action to take')\n parser.add_argument('-r', '--repo', required=True, help='the owner/name of the repository')\n parser.add_argument('-t', '--title', help='the title of the pr')\n parser.add_argument('-f', '--files', action='store_true', default=False, help='list files in the PR')\n parser.add_argument('-n', '--number', type=int, help='pr number')\n parser.add_argument('-l', '--label', nargs='+', help='label(s) to add/apply to the pr (one or more, space separated), or find a list of prs with matching labels (with list action)')\n parser.add_argument('-c', '--comments', action='store_true', help='added to list, to return list of comments')\n parser.add_argument('--filters', help='add this to the list function with collection of options you want to filter your results for', type=str)\n parser.add_argument('--base', default='master', help='branch the pr is against')\n parser.add_argument('--head', help='branch the pr is of')\n parser.add_argument('--body', default='', help='the description of the pr')\n parser.add_argument('--replacelabels', action='store_true', help='replace ALL labels during an update')\n parser.add_argument('--token', default=default_token, help='api token to use')\n parser.add_argument('--numberonly', action='store_true', help='only return the numbers of the PRs during the list action')\n parser.add_argument('--table', action='store_true', help='show a table of output instead of pretty. not compatible with numberonly')\n parser.add_argument('--tableformat', default='simple', help='format of table to use')\n parser.add_argument('--noheaders', action='store_true', help='remove headers from table view. best for programmatic use of this script')\n parser.add_argument('--noratelimit', action='store_true', help=\"don't show the rate limit\")\n parser.add_argument('--mergecomment', default=\":shipit:\", help='string to look for when checking comments for \"shipit\" approval, during MERGE only')\n parser.add_argument('--condition-non-owner-merger', action='store_true', help='stops owner from being able to apply merge comment')\n parser.add_argument('--condition-approved-mergers', default=None, nargs='+', help='list of usernames of approved mergers')\n parser.add_argument('--condition-approved-mergers-file', action='store_true', help='check a file for list of usernames of approved mergers')\n parser.add_argument('--approved-mergers-file-path', default='./MAINTAINERS.txt', help='location of file of usernames of approved mergers')\n parser.add_argument('-v', '--verbose', const=1, default=0, type=int, nargs=\"?\",\n help=\"Logger verbosity: 0 = WARN (default), 1 = INFO, 2 = DEBUG\")\n\n args = vars(parser.parse_args())\n logging.basicConfig(format=\"%(asctime)s %(levelname)-7s %(filename)20s:%(lineno)-4d | %(message)s\")\n\n if args['verbose'] == 0:\n logger.setLevel(logging.WARN) \n elif args['verbose'] == 1:\n logger.setLevel(logging.INFO) \n elif args['verbose'] == 2:\n logger.setLevel(logging.DEBUG)\n\n if 'action' in args and args['action'] == 'create':\n github_create_pr(**args)\n\n elif 'action' in args and args['action'] == 'list':\n github_list_prs(**args)\n\n elif 'action' in args and args['action'] == 'merge':\n if 'number' in args and args['number']:\n github_merge_pr_by_number(**args)\n else:\n github_merge_pr_by_branch(**args)\n\n elif 'action' in args and args['action'] == 'comment':\n github_comment_pr(**args)\n\n elif 'action' in args and args['action'] == 'delete':\n github_delete_pr(**args)\n\n elif 'action' in args and args['action'] == 'update':\n github_update_pr(**args)\n\n elif 'action' in args and args['action'] == 'check-condition':\n github_check_condition(**args)\n\n gh = Github(args['token'])\n\n if (('numberonly' in args) and not args['numberonly']):\n if (('noratelimit' in args) and not args['noratelimit']):\n print \"Github Rate Limiting: %d remaining of max %d\" % (gh.rate_limiting[0], gh.rate_limiting[1])","async def get_repository(session, token, repository_full_name):\n try:\n github = AIOGitHub(token, session)\n repository = await github.get_repo(repository_full_name)\n return repository\n except AIOGitHubException as exception:\n raise HacsException(exception)","def my_operator(self):\n return self._my_operator","def scrape_single_repo(user_repo):\n GP = Github_Profile()\n repo = 'https://api.github.com/repos/%s' % user_repo\n GP.user = repo.split('repos/')[1].split('/')[0]\n r = gf.get_request(repo)\n if r.ok:\n item = json.loads(r.text or r.content)\n signal.alarm(60)\n try:\n if item['fork'] is False: # for now ignore forks\n GP = get_features(item, GP)\n except:\n print('couldnt scrape %s' % repo)\n return GP","def get_nearest_operation(\n db: Redis[bytes], address: hash_t, subdag: Optional[str] = None\n) -> Optional[Operation]:\n root = \"root\"\n art = None\n try:\n node = Operation.grab(db, address)\n return node\n except RuntimeError:\n # one possibility is that address is an artefact...\n try:\n art = Artefact[Any].grab(db, address)\n except RuntimeError:\n raise RuntimeError(\n f\"address {address} neither a valid operation nor a valid artefact.\"\n )\n\n if art.parent == root:\n # We have basically just a single artefact as the network...\n return None\n else:\n node = Operation.grab(db, art.parent)\n return node","def github_api(request):\n if not request.startswith('https://api.github.com/'):\n request = 'https://api.github.com/' + request\n d = time.time() - github_api.last_time\n if d < 1:\n time.sleep(1 - d) # wait at least one second between GitHub API calls\n key = os.getenv('GITHUB_API_KEY')\n req = Request(request)\n req.add_header('User-Agent', github_api.user_agent)\n if key is not None:\n req.add_header('Authorization', 'token %s' % key)\n content = ''\n try:\n response = urlopen(req)\n content = response.read().decode()\n except HTTPError as e:\n print(request)\n print(e.reason)\n print(e.info())\n raise(e)\n github_api.last_time = time.time()\n return json.loads(content)","def __str__(self):\n return 'A Github Data Collector'","def get_write_pr_token(repo_names: List[str]) -> None:\n github_app = get_default_app()\n print(github_app.get_write_pr_token(repo_names))","def get_op_code_str(self, op):\n try:\n from tflite.BuiltinOperator import BuiltinOperator\n except ImportError:\n raise ImportError(\"The tflite package must be installed\")\n\n op_code_list_idx = op.OpcodeIndex()\n op_code_id = self.model.OperatorCodes(op_code_list_idx).BuiltinCode()\n op_code_str = self.builtin_op_code[op_code_id]\n if op_code_id == BuiltinOperator.CUSTOM:\n # Custom operator\n raise NotImplementedError(\"Custom operators are currently not supported\")\n return op_code_str","def get_github_creds():\r\n netrc_auth = requests.utils.get_netrc_auth(\"https://api.github.com\")\r\n if netrc_auth:\r\n return netrc_auth\r\n config_file = path(\"~/.config/edx-release\").expand()\r\n if config_file.isfile():\r\n with open(config_file) as f:\r\n config = json.load(f)\r\n github_creds = config.get(\"credentials\", {}).get(\"api.github.com\", {})\r\n username = github_creds.get(\"username\", \"\")\r\n token = github_creds.get(\"token\", \"\")\r\n if username and token:\r\n return (username, token)\r\n return None","def _load_issue(**args):\n check_required_fields(['token', 'repo', 'number'], **args)\n gh = Github(args['token'])\n repo = gh.get_repo(args['repo'])\n issue = repo.get_issue(args['number'])\n logger.debug(\" ISSUE: %s\", issue.number)\n return issue","def get_repo_url(repo, access_protocol, github_login):\n prop = {\n 'https': repo.clone_url,\n 'ssh': repo.ssh_url\n }[access_protocol]\n if access_protocol == 'https' and github_login:\n # we were provided explicit github login. For ssh access it is\n # impossible to specify different login within ssh RI, but it is\n # possible to do so for https logins\n url = URL(prop)\n assert url.scheme in ('http', 'https')\n url.username = github_login\n prop = url.as_str()\n return prop","def get_token(filename='config.ini'):\n cp = ConfigParser()\n cp.read(filename)\n token = cp.get('githubapi', 'token')\n return token","def pullrequest(self):\n target = self.get_data(\"target\")\n if target[\"type\"] == \"pipeline_pullrequest_target\":\n return PullRequest(\n target[\"pullrequest\"][\"links\"][\"self\"][\"href\"],\n target[\"pullrequest\"],\n **self._new_session_args\n ) # fmt: skip\n else:\n return None","def _query_api(\n master_url=settings.OPENSHIFT_API['NP']['OPENSHIFT_MASTER'],\n api_token=settings.OPENSHIFT_API['NP']['API_TOKEN'],\n endpoint='/oapi/v1/buildconfigs'):\n\n openshift_api_url = 'https://' + master_url\n openshift_api_get_endpoint = openshift_api_url + endpoint\n bearer_token_header = {'Authorization': 'Bearer ' + api_token }\n\n try:\n response = requests.get(openshift_api_get_endpoint,headers=bearer_token_header, timeout=2.0)\n except requests.ConnectTimeout as e:\n logger.error(e)\n return None\n except requests.ConnectionError as e:\n logger.error(e)\n return None\n\n if not response.ok:\n logger.error(response.status_code)\n return None\n else:\n return response","def get_issue(self, context):"],"string":"[\n \"def get_github_credendial(cls) -> 'ApiCredential':\\n return cls.select_token_for_api(GITHUB_API_NAME)\",\n \"def get(self, endpoint, params=None):\\n res = requests.get(\\\"https://api.github.com/\\\" + endpoint,\\n auth=requests.auth.HTTPBasicAuth(self.credentials['username'], self.credentials['token']),\\n params=params)\\n return res.json()\",\n \"def githubclient(token):\\n return github.Github(token)\",\n \"def _get_github_cred(github_login=None):\\n cred_identity = \\\"%s@github\\\" % github_login if github_login else \\\"github\\\"\\n return Token(cred_identity, GITHUB_TOKENS_URL)\",\n \"def get_pull_request(project, num, github_api=3):\\r\\n if github_api==2 :\\r\\n url = \\\"http://github.com/api/v2/json/pulls/{project}/{num}\\\".format(project=project, num=num)\\r\\n elif github_api == 3:\\r\\n url = \\\"https://api.github.com/repos/{project}/pulls/{num}\\\".format(project=project, num=num)\\r\\n response = requests.get(url)\\r\\n response.raise_for_status()\\r\\n if github_api == 2 :\\r\\n return json.loads(response.text)['pull']\\r\\n return json.loads(response.text)\",\n \"def network_get(auth=None, **kwargs):\\n cloud = get_operator_cloud(auth)\\n kwargs = _clean_kwargs(**kwargs)\\n return cloud.get_network(**kwargs)\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> Optional[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def get_pr_info(num):\\r\\n url = \\\"https://api.github.com/repos/edx/edx-platform/pulls/{num}\\\".format(num=num)\\r\\n username, token = get_github_creds()\\r\\n headers = {\\r\\n \\\"Authorization\\\": \\\"token {}\\\".format(token),\\r\\n \\\"User-Agent\\\": \\\"edx-release\\\",\\r\\n }\\r\\n response = requests.get(url, headers=headers)\\r\\n result = response.json()\\r\\n if not response.ok:\\r\\n raise requests.exceptions.RequestException(result[\\\"message\\\"])\\r\\n return result\",\n \"def github_link(self):\\n if self.test_type == TestType.commit:\\n test_type = 'commit'\\n test_id = self.commit\\n else:\\n test_type = 'pull'\\n test_id = self.pr_nr\\n\\n return f\\\"{self.fork.github_url}/{test_type}/{test_id}\\\"\",\n \"def operator(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> str:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def operator(self) -> str:\\n return pulumi.get(self, \\\"operator\\\")\",\n \"def github(auth=None) -> Github:\\n if auth is None:\\n token = conf('github.token', None)\\n if token:\\n return Github(token)\\n username = conf('github.username')\\n password = conf('github.password', env='MAESTRO_GITHUB_PASSWORD')\\n return github((username, password))\\n\\n if isinstance(auth, (tuple, list)):\\n return Github(*auth)\\n elif isinstance(auth, str):\\n return Github(auth)\\n\\n raise NotImplementedError\",\n \"def query_repo_tip(repo_url):\\n url = \\\"%s?tipsonly=1\\\" % (JSON_PUSHES % {\\\"repo_url\\\": repo_url})\\n recent_commits = retry(requests.get, args=(url,)).json()\\n tip_id = sorted(recent_commits.keys())[-1]\\n return Push(push_id=tip_id, push_info=recent_commits[tip_id])\",\n \"def get_symbol(operator):\\r\\n if isinstance(operator, AST):\\r\\n operator = type(operator)\\r\\n try:\\r\\n return ALL_SYMBOLS[operator]\\r\\n except KeyError:\\r\\n raise LookupError('no known symbol for %r' % operator)\",\n \"def _get_repo(self, owner, repo):\\n url = f\\\"{BASE_URL}/repos/{owner}/{repo}\\\"\\n status, data, _ = self.get(url)\\n if (status == 200):\\n return data\\n else:\\n log.warning(\\\"GHUB\\\", f\\\"Unexpected status code {status} for request {url}.\\\")\",\n \"def connect_to_github():\\n\\n # Authentication\\n from os.path import isfile\\n if isfile(\\\"github-logins.json\\\"):\\n with open(\\\"github-logins.json\\\", \\\"r\\\") as loginfile:\\n logins = json.load(loginfile)\\n gh = login(username=logins[\\\"username\\\"], password=logins[\\\"password\\\"])\\n else:\\n from getpass import getpass\\n password = getpass()\\n gh = login(username=\\\"yourusername\\\", password=password)\\n\\n # Connect to the repo\\n repo = gh.repository(\\\"ghostofgoes\\\", \\\"botnet-example\\\")\\n branch = repo.branch(\\\"master\\\")\\n return gh, repo, branch\",\n \"def get_github_equivalent(self):\\n\\n if self.description:\\n match_obj1 = re.search(r'(?<=Repository Name: )(.*?)(?={color})', self.description)\\n match_obj2 = re.search(r'(?<=Issue Number: )(.*?)(?={color})', self.description)\\n match_obj3 = re.search(r'(?<=Milestone: )(.*?)(?={color})', self.description)\\n match_obj4 = re.search(r'(?<=github.com/)(.*?)(?=/)', self.description)\\n if not any([match_obj1, match_obj2, match_obj3, match_obj4]):\\n logging.warning(f'No GitHub link information was found in the description of issue {self.jira_key}')\\n self.github_repo = match_obj1.group(0) if match_obj1 else None\\n self.github_key = match_obj2.group(0) if match_obj2 else None\\n self.milestone_name = match_obj3.group(0) if match_obj3 else None\\n self.github_org = match_obj4.group(0) if match_obj4 else None\",\n \"def get_link(\\n self,\\n operator: BaseOperator,\\n *,\\n ti_key: TaskInstanceKey,\\n ) -> str:\\n conn = BaseHook.get_connection(\\n getattr(operator, \\\"qubole_conn_id\\\", None)\\n or operator.kwargs[\\\"qubole_conn_id\\\"] # type: ignore[attr-defined]\\n )\\n if conn and conn.host:\\n host = re.sub(r\\\"api$\\\", \\\"v2/analyze?command_id=\\\", conn.host)\\n else:\\n host = \\\"https://api.qubole.com/v2/analyze?command_id=\\\"\\n qds_command_id = XCom.get_value(key=\\\"qbol_cmd_id\\\", ti_key=ti_key)\\n url = host + str(qds_command_id) if qds_command_id else \\\"\\\"\\n return url\",\n \"def github(self):\\n from nudgebot.thirdparty.github.user import User\\n return User.instantiate(self.config_data.github_login)\",\n \"def get_github_credentials():\\n\\n p = subprocess.Popen(\\\"git config github.accesstoken\\\",\\n shell=True,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n rc = p.wait()\\n if rc:\\n # failure to get config, so return silently\\n return None\\n token = p.stdout.readline().strip()\\n if token:\\n log.debug(\\\"Found github accesstoken %r\\\", token)\\n return token\",\n \"def repo_info():\\n return TEST_REPOS_INFO[0]\",\n \"def fetch_operators_priority(self, operator):\\n priority = self.operators_dict[operator]['priority']\\n return priority\",\n \"def get_op(self, op_complete_url):\\n url_parsed = urlsplit(op_complete_url)\\n op_url = url_parsed.path\\n\\n conf, op = self.best_match(op_url)\\n if op is not None:\\n return Operation(\\n op_complete_url,\\n op,\\n conf[\\\"conf\\\"][op],\\n conf[\\\"tp\\\"],\\n conf[\\\"sparql_http_method\\\"],\\n conf[\\\"addon\\\"],\\n )\\n else:\\n sc = 404\\n return (\\n sc,\\n \\\"HTTP status code %s: the operation requested does not exist\\\" % sc,\\n \\\"text/plain\\\",\\n )\",\n \"def get_read_token(repo_names: List[str]) -> None:\\n github_app = get_default_app()\\n print(github_app.get_read_token(repo_names))\",\n \"def fetch_operators_function(self, operator):\\n operators_function = self.operators_dict[operator]['function']\\n return operators_function\",\n \"def github(code, input):\\n syntax = 'Syntax: \\\\'.github <user|user/repo>\\\\''\\n failed = 'Failed to get data from Githubs API :('\\n if len(input.group(2).strip().split()) != 1:\\n return code.say(syntax)\\n\\n spacer = ' {blue}|{c} '\\n\\n if '/' not in input.group(2):\\n # Assume a single username\\n try:\\n tmp = web.json(user_api % input.group(2).strip())\\n response = {}\\n # Remove dem ugly nulled values. It's a dictionary so we have to\\n # loop differently.\\n for key, value in tmp.iteritems():\\n if value != '' or len(value) != 0 or value != 'null':\\n response[key] = value\\n print response\\n except:\\n return code.say(failed)\\n if 'message' in response:\\n # Assume failed\\n return code.say(failed)\\n\\n # Here is where we build the response\\n output = []\\n if 'name' in response:\\n output.append('%s (%s)' % (response['name'], response['login']))\\n else:\\n output.append(response['login'])\\n if 'location' in response:\\n output.append(response['location'])\\n if 'email' in response:\\n output.append(response['email'])\\n if 'public_repos' in response:\\n output.append('%s Repos' % response['public_repos'])\\n if 'followers' in response:\\n output.append('%s Followers' % response['followers'])\\n if 'following' in response:\\n output.append('Following %s' % response['following'])\\n if 'public_gists' in response:\\n output.append('%s Gists' % response['public_gists'])\\n if 'html_url' in response:\\n output.append(response['html_url'])\\n\\n return code.say(spacer.join(output))\\n\\n else:\\n # Assume Username/Repo\\n try:\\n response = jweb.json(repo_api % input.group(2).strip())\\n except:\\n return code.say(failed)\\n if 'message' in response:\\n # Assume failed\\n return code.say(failed)\\n # Here is where we build the response\\n output = []\\n output.append('%s (%s)' %\\n (response['name'], response['owner']['login']))\\n output.append(response['description'])\\n output.append('%s %s' % (response['stargazers_count'], u'\\\\u2605'))\\n output.append('%s %s' % (response['watchers_count'], u'\\\\u231A'))\\n output.append('%s %s' % (response['forks_count'], u'\\\\u2442'))\\n output.append('%s %s' % (response['open_issues_count'], u'\\\\u2602'))\\n output.append('%s %s' % (response['network_count'], u'\\\\U0001F46C'))\\n output.append('%s %s' % (response['subscribers_count'], u'\\\\u2764'))\\n output.append(response['html_url'])\\n return code.say(spacer.join(output))\",\n \"def _get_odl_info(pop_url, pop_id):\\n try:\\n graph_db = neo4j.Graph(pop_url)\\n index = ('pop', 'uuid', pop_id)\\n pop = neo_resource.get_node(graph_db, index)\\n if pop:\\n properties = dict(pop.properties)\\n if 'occi.epa.pop.odl_url' in properties and 'occi.epa.pop.odl_name' in properties \\\\\\n and 'occi.epa.pop.odl_password' in properties:\\n return properties['occi.epa.pop.odl_url'], properties['occi.epa.pop.odl_name'],\\\\\\n properties['occi.epa.pop.odl_password']\\n\\n except Exception:\\n raise HTTPError(404, 'Error connecting to graph_url: ' + str(pop_url))\\n raise HTTPError(404, 'Resource not found: Epa-Pop-Id: ' + str(pop_id))\",\n \"def test_fetch_valid_github_repo(self):\\n url = 'https://github.com/ivacf/archi'\\n repo = GitHubRepoFetcher().fetch(url)\\n self.assertEqual('archi', repo['name'])\",\n \"def get(self, endpoint, page=1, params=None):\\n url = 'https://api.github.com/%(endpoint)s?access_token=%(token)s&page=%(page)d' % {\\n 'endpoint': endpoint,\\n 'token': self.access_token,\\n 'page': page,\\n }\\n if params is not None:\\n url += '&' + urlencode(params)\\n response = requests.get(url)\\n\\n # Produce specific error on 404. Generic HTTPError otherwise.\\n if response.status_code == 404:\\n raise ResourceNotFound\\n response.raise_for_status()\\n\\n return response.json()\",\n \"def get_repository(post):\\n pattern = re.compile(constants.REPOSITORY_REGEX)\\n if \\\"links\\\" in post.json_metadata.keys():\\n for link in post.json_metadata[\\\"links\\\"]:\\n if link.startswith(\\\"/exit?url=\\\"):\\n link = link[len(\\\"/exit?url=\\\"):]\\n\\n try:\\n result = pattern.search(link).group(0)\\n return result\\n except AttributeError:\\n continue\\n else:\\n for line in post.body.split():\\n try:\\n result = pattern.search(line).group(0)\\n return result\\n except AttributeError:\\n continue\\n\\n return \\\"\\\"\",\n \"def get_label(issue_id):\\n auth = subprocess.check_output(\\n \\\"gsutil cat\\\"\\n \\\" gs://bazel-trusted-encrypted-secrets/github-trusted-token.enc |\\\"\\n \\\" gcloud kms decrypt --project bazel-public --location global\\\"\\n \\\" --keyring buildkite --key github-trusted-token --ciphertext-file\\\"\\n \\\" - --plaintext-file -\\\", shell=True\\n ).decode(\\\"utf-8\\\").strip().split(\\\"\\\\n\\\")[0]\\n headers = {\\n \\\"Authorization\\\": \\\"Bearer \\\" + auth,\\n \\\"Accept\\\": \\\"application/vnd.github+json\\\",\\n }\\n response = requests.get(\\n \\\"https://api.github.com/repos/bazelbuild/bazel/issues/\\\"\\n + issue_id + \\\"/labels\\\", headers=headers,\\n )\\n for item in response.json():\\n for key, value in item.items():\\n if key == \\\"name\\\" and \\\"team-\\\" in value:\\n return value.strip()\\n return None\",\n \"async def pr(ctx, number: Option(int, \\\"Pull request number\\\")):\\n url = f\\\"{repo}/issues/{number}\\\"\\n view = discord.ui.View()\\n view.add_item(discord.ui.Button(label=\\\"View Pull Request\\\", url=url))\\n await ctx.respond(f\\\"Here's a link\\\", view=view)\",\n \"def opid(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"opid\\\")\",\n \"def current_github_repo(remote='origin'):\\n\\n proc = run(['git', 'remote', 'get-url', remote], stdout=PIPE)\\n url = proc.stdout.strip().decode('utf-8')\\n\\n if 'github.com' in url:\\n match = re.search('([^:/]+)/([^/]+)\\\\.git$', url)\\n org = match.group(1)\\n repo = match.group(2)\\n return (org, repo)\\n else:\\n return None\",\n \"def get_operation(self, project, operation_id):\\n headers = {'AppScale-Secret': self.secret}\\n operation_url = '{prefix}/{project}/operations/{operation_id}'.format(\\n prefix=self.prefix, project=project, operation_id=operation_id)\\n response = requests.get(operation_url, headers=headers, verify=False)\\n return self.extract_response(response)\",\n \"async def github(self, ctx):\\n await ctx.send('https://github.com/nick411077/nickcan_bot')\",\n \"def get_operators(self):\\n url = self.config['links']['accountAPI'] + 'operators'\\n params = {\\n 'client': self.client,\\n 'country_code': self.locale_suffix\\n }\\n data = self.make_request(url, 'get', params=params)\\n\\n return data['data']['operators']\",\n \"def _get_pull_request(repo, objid):\\n if not repo.settings.get(\\\"pull_requests\\\", True):\\n raise PagureEvException(\\n \\\"No pull-request tracker found for this project\\\"\\n )\\n\\n session = _get_session()\\n request = pagure.lib.query.search_pull_requests(\\n session, project_id=repo.id, requestid=objid\\n )\\n\\n if request is None or request.project != repo:\\n raise PagureEvException(\\\"Pull-Request '%s' not found\\\" % objid)\\n\\n return request\",\n \"def button_github(args):\\n cell_source = args[\\\"cell_source\\\"]\\n repo = get_arg_or_fail(args[\\\"user\\\"], \\\"repo\\\", \\\"<org/name>\\\")\\n branch = args[\\\"user\\\"].get(\\\"branch\\\", \\\"master\\\")\\n docs_dir, rel_path = split_doc_path(args[\\\"path\\\"])\\n\\n # Buttons use OSS URLs.\\n if str(docs_dir) == \\\"g3doc/en\\\":\\n docs_dir = pathlib.Path(\\\"site/en\\\")\\n\\n base_url = f\\\"github.com/{repo}/blob/{branch}\\\"\\n this_url = \\\"https://\\\" + str(base_url / docs_dir / rel_path)\\n\\n if is_button_cell_re.search(cell_source) and cell_source.find(this_url) != -1:\\n return True\\n else:\\n fail(\\n f\\\"GitHub button URL doesn't match: {this_url}\\\",\\n fix=fix.regex_between_groups_replace_all,\\n fix_args=[r\\\"(href.*)http.*?(\\\\\\\\\\\\\\\".*GitHub-Mark-32px.png)\\\", this_url])\",\n \"def __repr__(self):\\n return 'GithubCollector()'\",\n \"def get_op(ring_size: int, op_str: str) -> Callable[..., Any]:\\n ops = RING_SIZE_TO_OP.get(ring_size, None)\\n\\n if ops is None:\\n raise ValueError(f\\\"Do not have operations for ring size {ring_size}\\\")\\n\\n op = ops.get(op_str, None)\\n if op is None:\\n raise ValueError(\\n f\\\"Operator {op_str} does not exist for ring size {ring_size}\\\"\\n )\\n\\n return op\",\n \"def _get_client(self):\\n return Github(\\n base_url=github_trigger.api_endpoint(),\\n login_or_token=self.auth_token if self.auth_token else github_trigger.client_id(),\\n password=None if self.auth_token else github_trigger.client_secret(),\\n timeout=5,\\n )\",\n \"def get_info(repos):\\n info = \\\"labelord application is master-to-master application for label replication using webhook for GitHub<br>\\\"\\n for i in repos:\\n info += i + ' ' + repo_link(i) + '<br>'\\n return info\",\n \"def get_operation(project_id: str, region: str, operation_id: str) -> Operation:\\n return get_operation_by_name(\\n f\\\"projects/{project_id}/locations/{region}/operations/{operation_id}\\\"\\n )\",\n \"def get_own_repo():\\n own_repo = GitClass(name='self', url='https://github.com/meganhmoore/github-api-covid-data', owner='meganhmoore',\\n repo='github-api-covid-data', branch='develop/new_data')\\n return own_repo\",\n \"def op(self) -> str:\\n return self._node.get(\\\"op\\\")\",\n \"def get_operagion(self):\\n if self.OP_GID not in self._data_dict:\\n return None\\n return dao.get_operation_by_gid(self._data_dict.get(self.OP_GID, None))\",\n \"def get_issue(issue_number):\\n backend_name = os.environ[\\\"ISSUE_BACKEND\\\"]\\n backend_module = importlib.import_module(\\n \\\"issuebranch.backends.{}\\\".format(backend_name)\\n )\\n\\n return getattr(backend_module, \\\"Backend\\\")(issue_number)\",\n \"def image_get(auth=None, **kwargs):\\n cloud = get_operator_cloud(auth)\\n kwargs = _clean_kwargs(**kwargs)\\n return cloud.get_image(**kwargs)\",\n \"def get_operator(self):\\n distribution = self.get_distribution_operator()\\n temp = self.get_unit_conversion_operator()\\n aperture = self.get_aperture_integration_operator()\\n filter = self.get_filter_operator()\\n projection = self.get_projection_operator()\\n hwp = self.get_hwp_operator()\\n polarizer = self.get_polarizer_operator()\\n integ = self.get_detector_integration_operator()\\n trans_inst = self.instrument.get_transmission_operator()\\n trans_atm = self.scene.atmosphere.transmission\\n response = self.get_detector_response_operator()\\n\\n with rule_manager(inplace=True):\\n H = CompositionOperator([\\n response, trans_inst, integ, polarizer, hwp * projection,\\n filter, aperture, trans_atm, temp, distribution])\\n if self.scene == 'QU':\\n H = self.get_subtract_grid_operator()(H)\\n return H\",\n \"def getPullRequest(upstream, ref, user=None, fork=None, target=None):\\r\\n\\r\\n if user is not None:\\r\\n user = user.login\\r\\n\\r\\n for p in upstream.get_pulls():\\r\\n # Check candidate request against specified criteria\\r\\n if p.head.ref != ref:\\r\\n continue\\r\\n\\r\\n if user is not None and p.head.user.login != user:\\r\\n continue\\r\\n\\r\\n if fork is not None and p.head.repo.url != fork.url:\\r\\n continue\\r\\n\\r\\n if target is not None and p.base.ref != target:\\r\\n continue\\r\\n\\r\\n # If we get here, we found a match\\r\\n return p\\r\\n\\r\\n # No match\\r\\n return None\",\n \"def gitHubConnect():\\n return HUB\",\n \"def test_github_explore(self):\\n python_trending_url = \\\"https://github.com/trending/python\\\"\\n driver = self.driver\\n driver.get(self.base_url)\\n search_box = driver.find_element_by_link_text(\\\"Explore\\\")\\n search_box.send_keys(Keys.RETURN)\\n time.sleep(2)\\n trending_elem = driver.find_element_by_partial_link_text(\\\"Trending\\\")\\n trending_elem.send_keys(Keys.ENTER)\\n time.sleep(2)\\n python_lang = driver.find_element_by_link_text(\\\"Python\\\")\\n python_lang.send_keys(Keys.ENTER)\\n time.sleep(2)\\n assert python_trending_url == driver.current_url\",\n \"def get_github_credentials():\\n\\n p = subprocess.Popen(\\\"git config github.token\\\",\\n shell=True,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n rc = p.wait()\\n if rc:\\n # failure to get config, so return silently\\n return None\\n token = p.stdout.readline().strip()\\n pipe = subprocess.Popen(\\\"git config github.user\\\",\\n shell=True,\\n stdout=subprocess.PIPE).stdout\\n login = pipe.readline().strip()\\n pipe.close()\\n if login and token:\\n log.debug(\\\"Found github credentials for user %r\\\", login)\\n return login, token\",\n \"def api_repo_get(access_key):\\n repo = Repo.query.get(access_key)\\n if not repo:\\n return jsonify(error=\\\"Repo not found\\\"), 404\\n \\n if repo.is_private and 'working_repo' not in session:\\n return jsonify(error=\\\"Unauthorized\\\"), 401\\n elif repo.is_private and session['working_repo'] != repo.access_key:\\n return jsonify(error=\\\"Unauthorized\\\"), 403\\n elif repo.is_private and session['working_repo'] == repo.access_key:\\n return jsonify(repo.to_json())\\n else:\\n return jsonify(repo.to_json())\",\n \"def test_get_github_repos_info_positive(self):\\n self.assertIsNotNone(app.get_github_repos_info(\\\"dhh\\\")[\\\"repo_info\\\"])\",\n \"async def github_repo_info(self, ctx: commands.Context, *repo: str) -> None:\\n repo = \\\"/\\\".join(repo)\\n if repo.count(\\\"/\\\") != 1:\\n embed = discord.Embed(\\n title=random.choice(NEGATIVE_REPLIES),\\n description=\\\"The repository should look like `user/reponame` or `user reponame`.\\\",\\n colour=Colours.soft_red\\n )\\n\\n await ctx.send(embed=embed)\\n return\\n\\n async with ctx.typing():\\n repo_data = await self.fetch_data(f\\\"{GITHUB_API_URL}/repos/{quote(repo)}\\\")\\n\\n # There won't be a message key if this repo exists\\n if \\\"message\\\" in repo_data:\\n embed = discord.Embed(\\n title=random.choice(NEGATIVE_REPLIES),\\n description=\\\"The requested repository was not found.\\\",\\n colour=Colours.soft_red\\n )\\n\\n await ctx.send(embed=embed)\\n return\\n\\n embed = discord.Embed(\\n title=repo_data[\\\"name\\\"],\\n description=repo_data[\\\"description\\\"],\\n colour=discord.Colour.blurple(),\\n url=repo_data[\\\"html_url\\\"]\\n )\\n\\n # If it's a fork, then it will have a parent key\\n try:\\n parent = repo_data[\\\"parent\\\"]\\n embed.description += f\\\"\\\\n\\\\nForked from [{parent['full_name']}]({parent['html_url']})\\\"\\n except KeyError:\\n log.debug(\\\"Repository is not a fork.\\\")\\n\\n repo_owner = repo_data[\\\"owner\\\"]\\n\\n embed.set_author(\\n name=repo_owner[\\\"login\\\"],\\n url=repo_owner[\\\"html_url\\\"],\\n icon_url=repo_owner[\\\"avatar_url\\\"]\\n )\\n\\n repo_created_at = datetime.strptime(repo_data[\\\"created_at\\\"], \\\"%Y-%m-%dT%H:%M:%SZ\\\").strftime(\\\"%d/%m/%Y\\\")\\n last_pushed = datetime.strptime(repo_data[\\\"pushed_at\\\"], \\\"%Y-%m-%dT%H:%M:%SZ\\\").strftime(\\\"%d/%m/%Y at %H:%M\\\")\\n\\n embed.set_footer(\\n text=(\\n f\\\"{repo_data['forks_count']} ⑂ \\\"\\n f\\\"• {repo_data['stargazers_count']} ⭐ \\\"\\n f\\\"• Created At {repo_created_at} \\\"\\n f\\\"• Last Commit {last_pushed}\\\"\\n )\\n )\\n\\n await ctx.send(embed=embed)\",\n \"async def get_issue(self, issue: int) -> \\\"AIOGitHubAPIRepositoryIssue\\\":\\n _endpoint = f\\\"/repos/{self.full_name}/issues/{issue}\\\"\\n\\n response = await self.client.get(endpoint=_endpoint)\\n return AIOGitHubAPIRepositoryIssue(self.client, response)\",\n \"def owner_project_from_github_url(url):\\n if not re.match('https://github.com/([a-zA-Z0-9-_]*)/[a-zA-Z0-9-_]*', url):\\n print(str(url) + \\\"is not a valid url!\\\")\\n exit(-1)\\n elements = url.split('/')\\n project_name = elements[-1]\\n organization_name = elements[-2]\\n return (organization_name, project_name)\",\n \"def test_grover_operator_getter(self):\\n oracle = QuantumCircuit(2)\\n oracle.cz(0, 1)\\n grover = Grover(oracle=oracle, good_state=[\\\"11\\\"])\\n constructed = grover.grover_operator\\n expected = GroverOperator(oracle)\\n self.assertTrue(Operator(constructed).equiv(Operator(expected)))\",\n \"def get_interface(self, operator_token):\\n if operator_token not in self.interfaces:\\n auth_token = random_token()\\n self.redis.publish('authentication', json.dumps((operator_token, auth_token)))\\n interface = OperatorInterface(operator_token)\\n self.authentications[auth_token] = interface\\n self.interfaces[operator_token] = interface\\n return interface\",\n \"def main():\\n\\n default_token = os.getenv('GITHUB_API_TOKEN')\\n\\n parser = argparse.ArgumentParser(\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n description=\\\"\\\"\\\"\\nShow PRs or a specific PR\\n\\n github-pr list -r dataxu/test_repo\\n github-pr list -r dataxu/test_repo -n 17\\n\\n - Filters - can be used alone or together\\n --filters\\n * owner - This will return a list of PRs from the repo that are owned by the github-user\\n * status - returns PRs with a specifc status, one of (success, failure, error, pending)\\n * label - returns PRs with a specifc label\\n * comment - returns PRs that have at least one comment matching a given string\\n github-pr list -r dataxu/test_repo --filters 'filter1_name=filter1_value,filter2_name=filter2_value'\\n\\n github-pr list -r dataxu/test_repo --filters 'owner=frankenstein,status=success,comment=:pitchfork:'\\n This returns the number of all PRs in the given repo owned by frankenstein with the status success and containing any comments that have \\\":pitchfork:\\\"\\n\\nCreate a PR\\n\\n github-pr create -r dataxu/test_repo -t \\\"PR Title\\\" --head \\\"my-test-branch\\\" --body 'Description Line 1<br/>Line2'\\n\\nCreate a PR from a fork\\n\\n github-pr create -r dataxu/test_repo -t \\\"PR Title\\\" --head \\\"my-fork:my-test-branch\\\"\\n\\nComment on a PR\\n\\n github-pr comment -r dataxu/test_repo -n 17 --body \\\":shipit:\\\"\\n\\nMerge a PR by PR number\\n\\n github-pr merge -r dataxu/test_repo -n 17\\n\\n github-pr merge -r dataxu/test_repo -n 17 --condition-non-owner-merger --condition-approved-mergers-file=.approved-mergers-file\\n This conditional option allows merges to go through checks that validate ownership and team hierarchy.\\n ie.\\n --condition-approved-mergers user1 user2 user3\\n This takes a SPACE-separated list, without quotes or braces\\n or\\n --condition-approved-mergers-file=.approved-mergers-file\\n OPTIONAL: --mergecomment can be set to a different string to search for instead of \\\":shipit:\\\"\\n\\nMerge a PR by branch\\n\\n github-pr merge -r dataxu/test_repo --head dev-my-branch-name\\n github-pr merge -r dataxu/test_repo --head dev-another-branch --base branch-that-is-not-master\\n\\nDelete a PR\\n\\n github-pr delete -r dataxu/test_repo -n 17\\n\\nCheck conditional status checks\\n !!!This check only looks at comments AFTER the latest commit, to validate that the\\n most recent code (most recent git sha pushed to the PR) has been peer reviewed!!!\\n\\n github-pr check-condition -r dataxu/dcommand -n 84 --condition-non-owner-merger\\n This will check to make sure that the owner can not apply a shipable comment on their own code\\n github-pr check-condition -r dataxu/dcommand -n84 --condition-approved-mergers-file=<MAINTAINERS FILE>\\n This takes the path to the MAINTAINERS file inside the repo\\n Compares commenter to list from a file, single user per line, and checks to make sure they are an approved merger\\n github-pr check-condition -r dataxu/dcommand -n 84 --condition-approved-mergers ned_flanders marge_simpson\\n This takes a SPACE-separated list, without quotes or braces\\n This will check that a comment containing a :shipit: (the default, or other defined comment if merge_comment is set)\\n comes from a user in the provided list passed on the commandline. The \\\"MAINTAINERS file\\\" option above is the preferred\\n convention to use, while this passing the list on the commandline option is primarily for local testing\\n when setting up your CD flow.\\n \\\"\\\"\\\")\\n parser.add_argument('action', choices=['create', 'list', 'merge', 'comment', 'delete', 'update', 'check-condition'], help='action to take')\\n parser.add_argument('-r', '--repo', required=True, help='the owner/name of the repository')\\n parser.add_argument('-t', '--title', help='the title of the pr')\\n parser.add_argument('-f', '--files', action='store_true', default=False, help='list files in the PR')\\n parser.add_argument('-n', '--number', type=int, help='pr number')\\n parser.add_argument('-l', '--label', nargs='+', help='label(s) to add/apply to the pr (one or more, space separated), or find a list of prs with matching labels (with list action)')\\n parser.add_argument('-c', '--comments', action='store_true', help='added to list, to return list of comments')\\n parser.add_argument('--filters', help='add this to the list function with collection of options you want to filter your results for', type=str)\\n parser.add_argument('--base', default='master', help='branch the pr is against')\\n parser.add_argument('--head', help='branch the pr is of')\\n parser.add_argument('--body', default='', help='the description of the pr')\\n parser.add_argument('--replacelabels', action='store_true', help='replace ALL labels during an update')\\n parser.add_argument('--token', default=default_token, help='api token to use')\\n parser.add_argument('--numberonly', action='store_true', help='only return the numbers of the PRs during the list action')\\n parser.add_argument('--table', action='store_true', help='show a table of output instead of pretty. not compatible with numberonly')\\n parser.add_argument('--tableformat', default='simple', help='format of table to use')\\n parser.add_argument('--noheaders', action='store_true', help='remove headers from table view. best for programmatic use of this script')\\n parser.add_argument('--noratelimit', action='store_true', help=\\\"don't show the rate limit\\\")\\n parser.add_argument('--mergecomment', default=\\\":shipit:\\\", help='string to look for when checking comments for \\\"shipit\\\" approval, during MERGE only')\\n parser.add_argument('--condition-non-owner-merger', action='store_true', help='stops owner from being able to apply merge comment')\\n parser.add_argument('--condition-approved-mergers', default=None, nargs='+', help='list of usernames of approved mergers')\\n parser.add_argument('--condition-approved-mergers-file', action='store_true', help='check a file for list of usernames of approved mergers')\\n parser.add_argument('--approved-mergers-file-path', default='./MAINTAINERS.txt', help='location of file of usernames of approved mergers')\\n parser.add_argument('-v', '--verbose', const=1, default=0, type=int, nargs=\\\"?\\\",\\n help=\\\"Logger verbosity: 0 = WARN (default), 1 = INFO, 2 = DEBUG\\\")\\n\\n args = vars(parser.parse_args())\\n logging.basicConfig(format=\\\"%(asctime)s %(levelname)-7s %(filename)20s:%(lineno)-4d | %(message)s\\\")\\n\\n if args['verbose'] == 0:\\n logger.setLevel(logging.WARN) \\n elif args['verbose'] == 1:\\n logger.setLevel(logging.INFO) \\n elif args['verbose'] == 2:\\n logger.setLevel(logging.DEBUG)\\n\\n if 'action' in args and args['action'] == 'create':\\n github_create_pr(**args)\\n\\n elif 'action' in args and args['action'] == 'list':\\n github_list_prs(**args)\\n\\n elif 'action' in args and args['action'] == 'merge':\\n if 'number' in args and args['number']:\\n github_merge_pr_by_number(**args)\\n else:\\n github_merge_pr_by_branch(**args)\\n\\n elif 'action' in args and args['action'] == 'comment':\\n github_comment_pr(**args)\\n\\n elif 'action' in args and args['action'] == 'delete':\\n github_delete_pr(**args)\\n\\n elif 'action' in args and args['action'] == 'update':\\n github_update_pr(**args)\\n\\n elif 'action' in args and args['action'] == 'check-condition':\\n github_check_condition(**args)\\n\\n gh = Github(args['token'])\\n\\n if (('numberonly' in args) and not args['numberonly']):\\n if (('noratelimit' in args) and not args['noratelimit']):\\n print \\\"Github Rate Limiting: %d remaining of max %d\\\" % (gh.rate_limiting[0], gh.rate_limiting[1])\",\n \"async def get_repository(session, token, repository_full_name):\\n try:\\n github = AIOGitHub(token, session)\\n repository = await github.get_repo(repository_full_name)\\n return repository\\n except AIOGitHubException as exception:\\n raise HacsException(exception)\",\n \"def my_operator(self):\\n return self._my_operator\",\n \"def scrape_single_repo(user_repo):\\n GP = Github_Profile()\\n repo = 'https://api.github.com/repos/%s' % user_repo\\n GP.user = repo.split('repos/')[1].split('/')[0]\\n r = gf.get_request(repo)\\n if r.ok:\\n item = json.loads(r.text or r.content)\\n signal.alarm(60)\\n try:\\n if item['fork'] is False: # for now ignore forks\\n GP = get_features(item, GP)\\n except:\\n print('couldnt scrape %s' % repo)\\n return GP\",\n \"def get_nearest_operation(\\n db: Redis[bytes], address: hash_t, subdag: Optional[str] = None\\n) -> Optional[Operation]:\\n root = \\\"root\\\"\\n art = None\\n try:\\n node = Operation.grab(db, address)\\n return node\\n except RuntimeError:\\n # one possibility is that address is an artefact...\\n try:\\n art = Artefact[Any].grab(db, address)\\n except RuntimeError:\\n raise RuntimeError(\\n f\\\"address {address} neither a valid operation nor a valid artefact.\\\"\\n )\\n\\n if art.parent == root:\\n # We have basically just a single artefact as the network...\\n return None\\n else:\\n node = Operation.grab(db, art.parent)\\n return node\",\n \"def github_api(request):\\n if not request.startswith('https://api.github.com/'):\\n request = 'https://api.github.com/' + request\\n d = time.time() - github_api.last_time\\n if d < 1:\\n time.sleep(1 - d) # wait at least one second between GitHub API calls\\n key = os.getenv('GITHUB_API_KEY')\\n req = Request(request)\\n req.add_header('User-Agent', github_api.user_agent)\\n if key is not None:\\n req.add_header('Authorization', 'token %s' % key)\\n content = ''\\n try:\\n response = urlopen(req)\\n content = response.read().decode()\\n except HTTPError as e:\\n print(request)\\n print(e.reason)\\n print(e.info())\\n raise(e)\\n github_api.last_time = time.time()\\n return json.loads(content)\",\n \"def __str__(self):\\n return 'A Github Data Collector'\",\n \"def get_write_pr_token(repo_names: List[str]) -> None:\\n github_app = get_default_app()\\n print(github_app.get_write_pr_token(repo_names))\",\n \"def get_op_code_str(self, op):\\n try:\\n from tflite.BuiltinOperator import BuiltinOperator\\n except ImportError:\\n raise ImportError(\\\"The tflite package must be installed\\\")\\n\\n op_code_list_idx = op.OpcodeIndex()\\n op_code_id = self.model.OperatorCodes(op_code_list_idx).BuiltinCode()\\n op_code_str = self.builtin_op_code[op_code_id]\\n if op_code_id == BuiltinOperator.CUSTOM:\\n # Custom operator\\n raise NotImplementedError(\\\"Custom operators are currently not supported\\\")\\n return op_code_str\",\n \"def get_github_creds():\\r\\n netrc_auth = requests.utils.get_netrc_auth(\\\"https://api.github.com\\\")\\r\\n if netrc_auth:\\r\\n return netrc_auth\\r\\n config_file = path(\\\"~/.config/edx-release\\\").expand()\\r\\n if config_file.isfile():\\r\\n with open(config_file) as f:\\r\\n config = json.load(f)\\r\\n github_creds = config.get(\\\"credentials\\\", {}).get(\\\"api.github.com\\\", {})\\r\\n username = github_creds.get(\\\"username\\\", \\\"\\\")\\r\\n token = github_creds.get(\\\"token\\\", \\\"\\\")\\r\\n if username and token:\\r\\n return (username, token)\\r\\n return None\",\n \"def _load_issue(**args):\\n check_required_fields(['token', 'repo', 'number'], **args)\\n gh = Github(args['token'])\\n repo = gh.get_repo(args['repo'])\\n issue = repo.get_issue(args['number'])\\n logger.debug(\\\" ISSUE: %s\\\", issue.number)\\n return issue\",\n \"def get_repo_url(repo, access_protocol, github_login):\\n prop = {\\n 'https': repo.clone_url,\\n 'ssh': repo.ssh_url\\n }[access_protocol]\\n if access_protocol == 'https' and github_login:\\n # we were provided explicit github login. For ssh access it is\\n # impossible to specify different login within ssh RI, but it is\\n # possible to do so for https logins\\n url = URL(prop)\\n assert url.scheme in ('http', 'https')\\n url.username = github_login\\n prop = url.as_str()\\n return prop\",\n \"def get_token(filename='config.ini'):\\n cp = ConfigParser()\\n cp.read(filename)\\n token = cp.get('githubapi', 'token')\\n return token\",\n \"def pullrequest(self):\\n target = self.get_data(\\\"target\\\")\\n if target[\\\"type\\\"] == \\\"pipeline_pullrequest_target\\\":\\n return PullRequest(\\n target[\\\"pullrequest\\\"][\\\"links\\\"][\\\"self\\\"][\\\"href\\\"],\\n target[\\\"pullrequest\\\"],\\n **self._new_session_args\\n ) # fmt: skip\\n else:\\n return None\",\n \"def _query_api(\\n master_url=settings.OPENSHIFT_API['NP']['OPENSHIFT_MASTER'],\\n api_token=settings.OPENSHIFT_API['NP']['API_TOKEN'],\\n endpoint='/oapi/v1/buildconfigs'):\\n\\n openshift_api_url = 'https://' + master_url\\n openshift_api_get_endpoint = openshift_api_url + endpoint\\n bearer_token_header = {'Authorization': 'Bearer ' + api_token }\\n\\n try:\\n response = requests.get(openshift_api_get_endpoint,headers=bearer_token_header, timeout=2.0)\\n except requests.ConnectTimeout as e:\\n logger.error(e)\\n return None\\n except requests.ConnectionError as e:\\n logger.error(e)\\n return None\\n\\n if not response.ok:\\n logger.error(response.status_code)\\n return None\\n else:\\n return response\",\n \"def get_issue(self, context):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60614455","0.594694","0.5812482","0.5780396","0.5726921","0.56730396","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.5613916","0.548893","0.54882455","0.53981405","0.53981405","0.53981405","0.5373711","0.5373711","0.5352586","0.5352551","0.5338005","0.53237617","0.52687836","0.52425164","0.52373266","0.52174145","0.5186332","0.51550764","0.5138097","0.51363903","0.5104689","0.50952864","0.5083349","0.5079764","0.5072923","0.50619835","0.5042637","0.5030261","0.50260794","0.5006645","0.50051147","0.4999844","0.49948153","0.49761298","0.4962554","0.496248","0.4960965","0.49592933","0.49507448","0.4934564","0.49305776","0.49073878","0.4895719","0.489162","0.48864713","0.48543066","0.4853076","0.4848924","0.48462155","0.4845009","0.4837816","0.4836046","0.48338538","0.48227996","0.48214212","0.48147848","0.4811177","0.48049733","0.4803246","0.4799536","0.47985166","0.47915557","0.47860137","0.47818977","0.4779368","0.4776615","0.4765765","0.47632927","0.47613916","0.4758606","0.47532994","0.47434646","0.4742736","0.47369012"],"string":"[\n \"0.60614455\",\n \"0.594694\",\n \"0.5812482\",\n \"0.5780396\",\n \"0.5726921\",\n \"0.56730396\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.5613916\",\n \"0.548893\",\n \"0.54882455\",\n \"0.53981405\",\n \"0.53981405\",\n \"0.53981405\",\n \"0.5373711\",\n \"0.5373711\",\n \"0.5352586\",\n \"0.5352551\",\n \"0.5338005\",\n \"0.53237617\",\n \"0.52687836\",\n \"0.52425164\",\n \"0.52373266\",\n \"0.52174145\",\n \"0.5186332\",\n \"0.51550764\",\n \"0.5138097\",\n \"0.51363903\",\n \"0.5104689\",\n \"0.50952864\",\n \"0.5083349\",\n \"0.5079764\",\n \"0.5072923\",\n \"0.50619835\",\n \"0.5042637\",\n \"0.5030261\",\n \"0.50260794\",\n \"0.5006645\",\n \"0.50051147\",\n \"0.4999844\",\n \"0.49948153\",\n \"0.49761298\",\n \"0.4962554\",\n \"0.496248\",\n \"0.4960965\",\n \"0.49592933\",\n \"0.49507448\",\n \"0.4934564\",\n \"0.49305776\",\n \"0.49073878\",\n \"0.4895719\",\n \"0.489162\",\n \"0.48864713\",\n \"0.48543066\",\n \"0.4853076\",\n \"0.4848924\",\n \"0.48462155\",\n \"0.4845009\",\n \"0.4837816\",\n \"0.4836046\",\n \"0.48338538\",\n \"0.48227996\",\n \"0.48214212\",\n \"0.48147848\",\n \"0.4811177\",\n \"0.48049733\",\n \"0.4803246\",\n \"0.4799536\",\n \"0.47985166\",\n \"0.47915557\",\n \"0.47860137\",\n \"0.47818977\",\n \"0.4779368\",\n \"0.4776615\",\n \"0.4765765\",\n \"0.47632927\",\n \"0.47613916\",\n \"0.4758606\",\n \"0.47532994\",\n \"0.47434646\",\n \"0.4742736\",\n \"0.47369012\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73203856"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74584,"cells":{"query":{"kind":"string","value":"Get an Translation Util"},"document":{"kind":"string","value":"def _translation_util(self, msg):\n token = self[msg.frm.person + \"github_token\"]\n return TranslateUtil(REPOSITORY_CONFIG_FILE, token)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_translation(self):","def get_gettext():\n local_path = os.path.realpath(os.path.dirname(sys.argv[0])) + \\\n '/translations'\n langs = []\n lc, encoding = locale.getdefaultlocale()\n if (lc):\n langs = [lc]\n osLanguage = os.environ.get('LANGUAGE', None)\n if (osLanguage):\n langs += osLanguage.split(\":\")\n langs += [\"en_US\"]\n lang = gettext.translation('wicd', local_path, languages=langs, \n fallback=True)\n _ = lang.gettext\n return _","def get_translation(self):\n return self.translation","def gettext_getfunc( lang ):\n # Note: you would get the gettext catalog here and install it in the\n # closure.\n\n def tr( s ):\n # Note: we do not really translate here, we just prepend the\n # language, but you get the idea.\n return '[%s] %s' % (lang, s)\n\n return tr","def translation_unit(self):\r\n # If this triggers an AttributeError, the instance was not properly\r\n # created.\r\n return self._tu","def lang_init():\n _locale, _encoding = locale.getdefaultlocale() # Default system values\n path = os.path.join(os.path.dirname(sys.argv[0]), 'localization/lang')\n if os.path.exists(path):\n lang = gettext.translation('UnrulyPuzzlePython', path, [_locale],\n fallback=True)\n else:\n lang = gettext.translation('UnrulyPuzzlePython', path,\n fallback=True)\n return lang.gettext","def translation_unit(self):\r\n # If this triggers an AttributeError, the instance was not properly\r\n # instantiated.\r\n return self._tu","def GetTranslation(*args):\n return _gdi_.GetTranslation(*args)","def test_simple_translation_using_get(self):\n pass","def init_translations():\n if \"@lang\" in input.load_input():\n lang = input.get_lang()\n try:\n trad = gettext.GNUTranslations(open(\"../course/common_student/$i18n/\" + lang + \".mo\", \"rb\"))\n except FileNotFoundError:\n trad = gettext.NullTranslations()\n trad.install()\n return lang\n trad = gettext.NullTranslations()\n trad.install()\n return \"en\"","def get_translated_tweet(tweet, language='it'):\n translated = GoogleTranslator(source='auto', target=language)\n return translated.translate(tweet)","def get_localization(self, language: str) -> Localization:\n ...","def gettext_for(locale='en'):\n return Translations.load(\n os.path.join(BASEDIR, 'app', 'translations'), [locale]\n ).ugettext","def get_translation ( self ):\n self.verify_post_data ( )\n\n text = request.json[ 'text' ]\n src_lang = request.json[ 'source_lang' ]\n target_lang = request.json[ 'target_lang' ]\n\n # if translation is available in cache, just fetch it from there. Otherwise use translation service.\n translated_text = self.get_set_translation_from_cache ( text, src_lang, target_lang )\n\n return jsonify ( {\"Translation\": translated_text} )","def test_get_translation_resources(self):\n pass","def translation(self):\n return self._translation","def get_translation(self):\n trans_keys = ''.join(self._trans_dict.keys())\n trans_values = ''.join(self._trans_dict.values())\n\n trans_table = string.maketrans(trans_keys, trans_values)\n translation = self._puzzle.translate(trans_table)\n return translation","def get_translation(obj, language_code):\n if not obj or not hasattr(obj, \"get_translation\"):\n return None\n return obj.get_translation(language_code)","def gettext_translate( s ):\n return catalogs.translate(s)","def get_translation(self, command, keyword=None, item=None):\n key = item if item is not None else \\\n keyword if keyword is not None else command\n cata = self.get_catalog(command)\n if cata is not None:\n dtr = cata.definition.get(\"translation\", {})\n trans = dtr.get(key)\n if trans is not None:\n return unicode(trans, 'utf-8')\n return GLOBAL_DICT.get(key, key)","def test_get_machine_translate_settings_for_project_template(self):\n pass","def ugettext(self):\n return self._translations.gettext","def get_translation_from_cache ( self, text, src_lang, target_lang ):\n return self.app_cache.get_translation_from_cache ( text, src_lang, target_lang )","def _lazy_ugettext(text: str):\n try:\n # Test if context is available,\n # cf. https://github.com/tracim/tracim/issues/173\n context = StackedObjectProxy(name=\"context\")\n context.translator\n return ugettext(text)\n except TypeError:\n return text","def GetUtilWrapper(self):\n return ''","def translate():\n pass","def translations(self):\r\n return Translations(self)","def translations(self):\r\n return Translations(self)","def get_translation(self, lang):\n if lang==\"it\":\n url=self.backstring+\"en/\"+self.name\n image=self.backstring+\"img/uk.png\"\n alttext='English version'\n elif lang==\"en\":\n url=self.backstring+\"it/\"+self.name\n image=self.backstring+\"img/it.png\"\n alttext='Italian version'\n img='<img src=\"%s\" height=\"15\" alt=\"%s\"><br>%s' % (image, alttext,alttext, )\n a=A(img, url, \"translation\")\n return str(a)","def fake_ugettext(translations):\r\n def _ugettext(text): # pylint: disable=missing-docstring\r\n return translations.get(text, text)\r\n return _ugettext","def translate(sentence,target,api_key):\n #translate without using googletrans wrapper library\n URL = \"https://translation.googleapis.com/language/translate/v2?target=\"+target+\"&key=\"+api_key+\"&q=\"+sentence\n # sending get request and saving the response as response object \n r = requests.get(url = URL)\n\n if r.status_code == 200:\n # extracting data in json format \n data = r.json()\n return data['data']['translations'][0]['translatedText']","def GetUtilWrapper(self):\n return self.GetTestWrapper()","def resolve_translation(obj, _):\n return obj.translation.decode()","def getTranslationBundle(self, language=None):\n\n if language is None:\n return None\n\n if language in self.__translationBundles:\n return self.__translationBundles[language]\n\n Console.info(\"Creating translation bundle: %s\", language)\n Console.indent()\n\n # Initialize new Translation object with no project assigned\n # This object is used to merge all seperate translation instances later on.\n combined = jasy.item.Translation.TranslationItem(None, id=language)\n relevantLanguages = self.__expandLanguage(language)\n\n # Loop structure is build to prefer finer language matching over project priority\n for currentLanguage in reversed(relevantLanguages):\n for project in self.__projects:\n for translation in project.getTranslations().values():\n if translation.getLanguage() == currentLanguage:\n Console.debug(\"Adding %s entries from %s @ %s...\", len(translation.getTable()), currentLanguage, project.getName())\n combined += translation\n\n Console.info(\"Combined number of translations: %s\", len(combined.getTable()))\n Console.outdent()\n\n self.__translationBundles[language] = combined\n return combined","def _extract_18n_messages():\n BabelCLI().run(['', 'extract', '-F', 'babel.cfg', '-k', '_t', '--no-location', '--sort-output',\n '--omit-header', '-o', os.path.join(I18N_PATH, 'messages.pot'), 'aliquis'])","def buildTranslation(lang,suppressAlert=False):\n global string_cache\n fName = os.path.join(langPath, lang + \".trn\")\n if verifyLangCode(lang) and not lang == \"en_US\":\n data = open(fName, \"rb\").read() + \"\\x00\"\n trnPattern = re.compile(r\"^o\\d+[ ]|^t\\d+[ ]\", re.M|re.S)\n grps = re.finditer(trnPattern, data)\n oStart = -1\n oEnd = -1\n tStart = -1\n tEnd = -1\n org = None\n for grp in grps:\n g = grp.group()\n if g.startswith(\"o\"):\n oStart = grp.end()\n tEnd = grp.start() -1\n elif g.startswith(\"t\"):\n oEnd = grp.start() -1\n tStart = grp.end()\n if oStart > -1 and oEnd > -1 and tStart > tEnd:\n org = data[oStart:oEnd]\n if tStart > -1 and (tEnd > -1):\n if tEnd > tStart:\n string_cache[org] = correctEncoding(data[tStart:tEnd])\n tStart = -1\n tEnd = -1\n string_cache[org] = correctEncoding(data[tStart:tEnd -1])\n return string_cache","def interpolate_insted_of_translate(\n self, msgid, mapping=None, *args, **kw): # pragma: no cover webdriver\n return zope.i18n.interpolate(msgid, mapping)","def getText(str, request, lang, formatted=True):\n # TODO: Should move this into a language instance. request.lang should be a language instance.\n\n # load texts if needed\n global _text_cache\n if not lang in _text_cache:\n (texts, unformatted) = loadLanguage(request, lang)\n # XXX add error handling\n _text_cache[lang] = texts\n _unformatted_text_cache[lang] = unformatted\n\n # get the matching entry in the mapping table\n trans = str\n try:\n if formatted:\n trans = _text_cache[lang][str]\n else:\n trans = _unformatted_text_cache[lang][str]\n except KeyError:\n try:\n language = languages[lang][ENAME]\n dictpagename = \"%sDict\" % language\n dicts = request.dicts\n if dicts.has_dict(dictpagename):\n userdict = dicts.dict(dictpagename)\n trans = userdict[str]\n else:\n raise KeyError\n except KeyError:\n # do not simply return trans with str, but recursively call\n # to get english translation, maybe formatted.\n # if we don't find an english \"translation\", we just format it\n # on the fly (this is needed for cfg.editor_quickhelp).\n if lang != 'en':\n trans = getText(str, request, 'en', formatted)\n elif formatted:\n trans = formatMarkup(request, str)\n\n return trans","def translate(self, language=None):","def get(module, lang=\"en_US\"):\n\n module = import_module(\n \"._lang.{}.{}\".format(subdir(lang), module), package=__package__\n )\n return module","def gettext_translate(string):\n # Try default translation first\n translation = localizer.old_translate(i18n.TranslationString(string))\n if translation == string:\n # translation failed then use FormEncode\n translation = formencode_api._stdtrans(string)\n return translation","def translate_wrapper(atext):\n print(\"translating:\",atext)\n res=\"\"\n res=translate(atext,\"pl\",\"fr\")\n time.sleep(0.5)\n print(\"translation:\",res)\n return res","def fake_ugettext(translations):\n def _ugettext(text):\n return translations.get(text, text)\n return _ugettext","def util():\n pass","def util():\n pass","def get_translation(translated_text_json, language=None):\n if not language:\n language = translation.get_language()\n\n translations = translated_text_json or {}\n en = translations.get('en')\n translated = translations.get(language)\n return translated or en or ''","def localize(self, msg):\n return self.translations.get(msg, msg)","def localize(self, msg):\n return self.translations.get(msg, msg)","def get_set_translation_from_cache ( self, text, src_lang, target_lang ):\n translated_text = self.get_translation_from_cache ( text, src_lang, target_lang )\n if not translated_text:\n translated_text = self.service_creator.get_translation ( text, src_lang, target_lang )\n self.set_translation_to_cache ( text, src_lang, target_lang, translated_text )\n return translated_text","def trans(self):\n return self._base.trans","def translate(self):\n pass","def get_translation(location):\r\n return self.loc_mapper.translate_location(location, published, add_entry_if_missing=True)","def get_subreddit_translation(subreddit, bible_section):\n\n if bible_section == \"Old Testament\":\n section = \"ot_default\"\n elif bible_section == \"New Testament\":\n section = \"nt_default\"\n else:\n section = \"deut_default\"\n with _conn.cursor() as cur:\n cur.execute(\"SELECT %s FROM subreddit_translations WHERE sub = '%s';\" % (section, subreddit.lower()))\n try:\n trans = cur.fetchone()[0]\n return trans\n except (psycopg2.ProgrammingError, TypeError) as e:\n return None","def utility(self) -> str:\n return self._utility","def _translate(self, key):\n return self.TRANSLATE.get(key, key)","def _getTranslationFromURL(self, translation, encoding='utf-8'):\n file_url = self._quranFilePath + translation + '.json'\n data_response = urlopen(file_url)\n json_data = data_response.read()\n json_str = json_data.decode(encoding)\n return json.loads(json_str)","def locale_factory(factory):\n global _get_locale\n _get_locale = factory\n return factory","def getUtilities(self):\n\n return Utilities(self)","def question_new_translate():","def translate():\n text = request.args.get('text')\n\n # Send a request to Google's Translate REST API using your API credentials defined above\n ans = service.translations().list(source='en', target='zh-CN', q=text).execute()\n\n # Return translated text back to user\n return ans['translations'][0]['translatedText']","def get_localization(self):\n return self._request_data(\"/lokarria/localization\")","def get_text(self):\n try:\n return self.get_translation().text\n except MissingTranslation:\n return _(\"(No text)\")","def translate_one(text, lang, format='plain'):\n if not isinstance(text, basestring):\n raise TypeError('Text should be a string, not a %s' % type(text).__name__)\n return translate_many(text, lang, format)[0]","def gettext(self, message):\n if self._translations.has_key(message):\n return self._translations[message]\n return super(Translations, self).gettext(message)","def get_transformer() -> TransformationFunc:\n THREAD_LOCALS = access_thread_locals()\n try:\n transformer = THREAD_LOCALS.XMLSnippet_00000001_transformer_singleton\n except AttributeError:\n THREAD_LOCALS.XMLSnippet_00000001_transformer_singleton = CreateXMLSnippetTransformer()\n transformer = THREAD_LOCALS.XMLSnippet_00000001_transformer_singleton\n return transformer","def get_utility(s, direction):\n new_pos = transition(s, direction)\n new_utility = utilities[new_pos[0]][new_pos[1]]\n return new_utility","def _getText(self, item): # TODO: move this method to more suitable place when possible (scripting base class)\r\n if not isinstance(item, basestring):\r\n return item\r\n\r\n translation = self.phone.getTranslation(item)\r\n if translation != None:\r\n return translation\r\n\r\n return item","def get_unbabel_translation(uid, lang):\n url = \"http://sandbox.unbabel.com/tapi/v2/translation/{}/\".format(uid)\n resp = do_request('GET', url, headers=UNBABEL_HEADERS)\n data = resp.json()\n db.stories.update_one({\"unbabel_uid_{}\".format(lang): uid},\n {\"$set\": {\"unbabel_status_{}\".format(lang): data.get('status')}})\n if \"completed\" == data.get('status'):\n db.stories.update_one({\"unbabel_uid_{}\".format(lang): uid},\n {\"$set\": {\"title_{}\".format(lang): data.get('translatedText')}})\n return [{\"unbabel_uid_{}\".format(lang): uid, \"status\": data.get('status')}]","def setup_listener_gettext(self):\n\t\tth = current_thread()\n\n\t\t#print '>> listener gettext in ', th.name\n\n\t\ttry:\n\t\t\tth._ = th._licornd.langs[th.listener.lang].ugettext\n\n\t\texcept KeyError:\n\t\t\t# the daemon doesn't have the client lang installed. Not a problem.\n\t\t\t# Still, make a shortcut to the daemon's default translator to\n\t\t\t# avoid trigerring an exception at every call of our translator\n\t\t\t# wrapper.\n\t\t\tth._ = __builtin__.__dict__['_orig__']","def get_name_translation(self):\n\t\treturn frappe.get_value(\n\t\t\t\"Translation\",\n\t\t\t{\"source_text\": self.doc_type, \"language\": frappe.local.lang or \"en\"},\n\t\t\t[\"name\", \"translated_text\"],\n\t\t\tas_dict=True,\n\t\t)","def _get_converter(orig, target):\n try:\n func = getattr(utils, f'convert_{orig}_to_{target}')\n except AttributeError:\n func = partial(convert_unit, orig=orig, to=target)\n return func","def translate_as(self, lang):\n trans = ItemTranslation.objects.filter(item=self,\n lang=lang,\n is_active=True)\n if trans: return trans.first()\n else: return self","def getTRSLanguage():\n try:\n return sys.argv[1]\n except IndexError as error:\n print(\"No language argument\\n\")\n sys.exit()","def __loadTranslator(self):\n if self.__ui is not None:\n loc = self.__ui.getLocale()\n if loc and loc != \"C\":\n locale_dir = os.path.join(\n os.path.dirname(__file__), \"ProjectDjango\", \"i18n\")\n translation = \"django_{0}\".format(loc)\n translator = QTranslator(None)\n loaded = translator.load(translation, locale_dir)\n if loaded:\n self.__translator = translator\n e5App().installTranslator(self.__translator)\n else:\n print(\"Warning: translation file '{0}' could not be\"\n \" loaded.\".format(translation))\n print(\"Using default.\")","def get_translation_for_object(self, lang, obj=None, model=None, object_id=None):\n\n # Gets object model and pk if informed\n if obj:\n model = type(obj)\n object_id = obj.pk\n\n cache_key = self.make_cache_key(model, object_id, lang)\n \n # Checks if there is a cached object for this\n from_cache = cache.get(cache_key, None)\n\n if from_cache:\n return from_cache\n\n # Gets the related content type\n c_type = ContentType.objects.get_for_model(model)\n\n # Gets the translation\n trans = self.get(language=lang, content_type=c_type, object_id=object_id)\n\n # Stores in cache\n cache.set(cache_key, trans)\n\n # Returns the translation object\n return trans","def get(self, msgid):\r\n return self.trans.get(msgid, str(msgid))","def get_text(value):\r\n if value is None:\r\n return None\r\n else:\r\n return self.runtime.service(self, \"i18n\").ugettext(value)","def getTranslation(table, language = None):\n\n return cache.codeTableFind(table, language)","def get_translation_db(seqDB):\n try:\n return seqDB.translationDB\n except AttributeError: # create a new TranslationAnnot DB\n tdb = TranslationDB(seqDB)\n try:\n seqDB.translationDB = tdb\n except AttributeError:\n pass # won't let us cache? Just hand back the TranslationDB\n return tdb","def test_supported_translations_retrieval(self):\n\t\t\n\t\thelpers.find_supported_translations()\n\t\tself.assertTrue(helpers.get_supported_translations() != 0)","def try_translation(text):\n try:\n trad = Translator().translate(text = text, dest = 'fr')\n except:\n sleep(random() * 60)\n return False\n \n return trad.text","def __init__(self, language=None):\n self.language = language\n self.translations = {}","def translate(atext,fromlang,tolang):\n rtext=\"\"\n \n base_link = \"http://translate.google.com/m?hl=%s&sl=%s&q=%s\"\n url = base_link % (tolang, fromlang, atext)\n print(url)\n headers={'Accept-Charset': 'utf-8'}\n r = requests.get(url,headers=headers)\n content = r.content.decode('utf-8')\n\n if r.status_code != 200:\n print(\"ERROR\")\n print(r.status_code)\n print(r.headers)\n print(\"content\",content)\n time.sleep(1)\n else:\n soup = bs4.BeautifulSoup(content,'html.parser')\n # print(soup) # div class=\"t0\"\n res=soup.find(\"div\",attrs={\"class\":\"t0\"})\n # print(\"res:\",res)\n print(\"res.text:\",res.text)\n rtext=res.text\n return rtext","def machine_translation(request):\n log.debug(\"Get translation from machine translation service.\")\n\n try:\n text = request.GET['text']\n locale = request.GET['locale']\n check = request.GET['check']\n except MultiValueDictKeyError as e:\n log.error(str(e))\n return HttpResponse(\"error\")\n\n if hasattr(settings, 'MICROSOFT_TRANSLATOR_API_KEY'):\n api_key = settings.MICROSOFT_TRANSLATOR_API_KEY\n else:\n log.error(\"MICROSOFT_TRANSLATOR_API_KEY not set\")\n return HttpResponse(\"apikey\")\n\n obj = {}\n\n # On first run, check if target language supported\n if check == \"true\":\n supported = False\n languages = settings.MICROSOFT_TRANSLATOR_LOCALES\n\n if locale in languages:\n supported = True\n\n else:\n for lang in languages:\n if lang.startswith(locale.split(\"-\")[0]): # Neutral locales\n supported = True\n locale = lang\n break\n\n if not supported:\n log.debug(\"Locale not supported.\")\n return HttpResponse(\"not-supported\")\n\n obj['locale'] = locale\n\n url = \"http://api.microsofttranslator.com/V2/Http.svc/Translate\"\n payload = {\n \"appId\": api_key,\n \"text\": text,\n \"from\": \"en\",\n \"to\": locale,\n \"contentType\": \"text/html\",\n }\n\n try:\n r = requests.get(url, params=payload)\n log.debug(r.content)\n\n # Parse XML response\n root = ET.fromstring(r.content)\n translation = root.text\n obj['translation'] = translation\n\n return HttpResponse(json.dumps(obj), content_type='application/json')\n\n except Exception as e:\n log.error(e)\n return HttpResponse(\"error\")","def get_translation(self):\n self.activate_navigation_constrain()\n return self.tx, self.ty","def translation_key(self) -> str | None:\n return TRANSLATION_KEY_MAPPING.get(self.capability.id)","def tr(self, message):\n # noinspection PyTypeChecker,PyArgumentList,PyCallByClass\n return QCoreApplication.translate('TFB_Tools3', message)","def get_lang(ix):\n\tlang = None\n\tif ix == 0:\n\t\tlang = setting.TLA_ENG\n\telif ix == 1:\n\t\tlang = setting.TLA_JP\n\telse:\n\t\tlang = setting.TLA_VN\n\n\tf = open (f\"lang\\\\{lang}.json\", encoding=setting.TLA_UTF8)\n\tglobal data_json\n\tdata_json = json.load(f)\n\n\treturn lang","def get_user_translation(username, bible_section):\n translation = None\n\n if bible_section == \"Old Testament\":\n section = \"ot_default\"\n elif bible_section == \"New Testament\":\n section = \"nt_default\"\n else:\n section = \"deut_default\"\n with _conn.cursor() as cur:\n cur.execute(\"SELECT %s FROM user_translations WHERE username = '%s';\" % (section, str(username)))\n try:\n translation = cur.fetchone()[0]\n except (psycopg2.ProgrammingError, TypeError) as e:\n translation = None\n cur.execute(\"UPDATE user_translations SET last_used = NOW() WHERE username = '%s'\" % str(username))\n _conn.commit()\n return translation","def main():\n # Parse args\n parser = argparse.ArgumentParser(description=\"Open-source offline translation.\\n\")\n parser.add_argument(\n \"text\",\n nargs=\"?\",\n metavar=\"TEXT\",\n help=\"The text to translate. Read from standard input if missing.\",\n )\n parser.add_argument(\n \"--from-lang\",\n \"-f\",\n help=\"The code for the language to translate from (ISO 639-1)\",\n )\n parser.add_argument(\n \"--to-lang\", \"-t\", help=\"The code for the language to translate to (ISO 639-1)\"\n )\n args = parser.parse_args()\n\n from_and_to_lang_provided = args.from_lang is not None and args.to_lang is not None\n\n # Get text to translate\n if args.text:\n # argos-translate-cli --from-lang en --to-lang es \"Text to translate\"\n text_to_translate = args.text\n elif from_and_to_lang_provided:\n # echo \"Text to translate\" | argos-translate-cli --from-lang en --to-lang es\n text_to_translate = \"\".join(sys.stdin)\n else:\n # argos-translate\n parser.print_help()\n return\n\n # Perform translation\n if from_and_to_lang_provided:\n installed_languages = {\n lang.code: lang for lang in translate.load_installed_languages()\n }\n if args.from_lang not in installed_languages:\n parser.error(\"{!r} is not an installed language.\".format(args.from_lang))\n if args.to_lang not in installed_languages:\n parser.error(\"{!r} is not an installed language.\".format(args.to_lang))\n from_lang = installed_languages[args.from_lang]\n to_lang = installed_languages[args.to_lang]\n translation = from_lang.get_translation(to_lang)\n if translation is None:\n parser.error(\n f\"No translation installed from {args.from_name} to {args.to_name}\"\n )\n else:\n translation = translate.IdentityTranslation(\"\")\n\n # Print translation\n print(translation.translate(text_to_translate))","def fetchTranslation(self, language):\n pass","def get_transformer() -> TransformerFunc:\n THREAD_LOCALS = access_thread_locals()\n try:\n transformer = THREAD_LOCALS.XML_00000001_transformer_singleton\n except AttributeError:\n THREAD_LOCALS.XML_00000001_transformer_singleton = CreateXMLTransformer()\n transformer = THREAD_LOCALS.XML_00000001_transformer_singleton\n return transformer","def translate(self, phrase):\n\n if phrase not in TRANSLATIONS or self.ui_lang_code not in TRANSLATIONS[phrase]:\n return phrase\n return TRANSLATIONS[phrase][self.ui_lang_code]","def translateText(text):\r\n\treturn translator.translate(text, src='en', dest='ro')","def get_translation_table(\n global_table: Union[str, Dict] = None, \n local_table: Union[str, Dict] = None,\n logger = None,\n) -> TranslationTable:\n\n global_tt = {}\n local_tt = {}\n\n if not global_table:\n if local_table:\n raise ValueError(\"Local table without a global table not allowed\")\n else:\n logger.debug(\"No global table used for transform\")\n else:\n\n if isinstance(global_table, str):\n with open(global_table, 'r') as global_tt_fh:\n global_tt = yaml.safe_load(global_tt_fh)\n elif isinstance(global_table, Dict):\n global_tt = global_table\n\n if local_table:\n if isinstance(local_table, str):\n with open(local_table, 'r') as local_tt_fh:\n local_tt = yaml.safe_load(local_tt_fh)\n elif isinstance(local_table, Dict):\n local_tt = local_table\n\n else:\n logger.debug(\"No local table used for transform\")\n\n return TranslationTable(global_tt, local_tt)","def test_translation_smoke():\n english_to_morse = get_translator(\"english\", \"morse\")\n morse_to_english = get_translator(\"morse\", \"english\")\n morse = english_to_morse.translate(\"hello world\")\n english = morse_to_english.translate(morse)\n assert english == \"HELLO WORLD\"","def tr(message):\r\n # noinspection PyTypeChecker,PyArgumentList,PyCallByClass\r\n return QCoreApplication.translate('APIS', message)","def trans_app(value):\n return _(value.title())","def test_i18n(self):\n # If you want to modify this code, please get acquainted with\n # Python's locale module. In particular:\n # http://docs.python.org/library/locale.html#locale.getdefaultlocale\n\n # Set the standard C locale.\n os.environ['LANG'] = 'C'\n os.environ['LC_ALL'] = 'C.UTF-8'\n\n #must be after above\n out1 = tr('Hello!')\n expected1 = 'Hello!'\n msg = 'Expected %s, got %s' % (expected1, out1)\n assert out1 == expected1, msg\n\n # Set the Indonesian locale to test translations.\n os.environ['LANG'] = 'id'\n os.environ['LC_ALL'] = 'id_ID.UTF-8'\n\n #must be after above\n #indoout1 = tr('Hello!') # translate as 'Hi'\n #indoexpected1 = 'Hi!'\n #msg = 'Expected %s, got %s' % (indoexpected1, indoout1)\n #assert indoout1 == indoexpected1, msg","def translate(self):\n raise NotImplementedError('subclass must override this method')"],"string":"[\n \"def get_translation(self):\",\n \"def get_gettext():\\n local_path = os.path.realpath(os.path.dirname(sys.argv[0])) + \\\\\\n '/translations'\\n langs = []\\n lc, encoding = locale.getdefaultlocale()\\n if (lc):\\n langs = [lc]\\n osLanguage = os.environ.get('LANGUAGE', None)\\n if (osLanguage):\\n langs += osLanguage.split(\\\":\\\")\\n langs += [\\\"en_US\\\"]\\n lang = gettext.translation('wicd', local_path, languages=langs, \\n fallback=True)\\n _ = lang.gettext\\n return _\",\n \"def get_translation(self):\\n return self.translation\",\n \"def gettext_getfunc( lang ):\\n # Note: you would get the gettext catalog here and install it in the\\n # closure.\\n\\n def tr( s ):\\n # Note: we do not really translate here, we just prepend the\\n # language, but you get the idea.\\n return '[%s] %s' % (lang, s)\\n\\n return tr\",\n \"def translation_unit(self):\\r\\n # If this triggers an AttributeError, the instance was not properly\\r\\n # created.\\r\\n return self._tu\",\n \"def lang_init():\\n _locale, _encoding = locale.getdefaultlocale() # Default system values\\n path = os.path.join(os.path.dirname(sys.argv[0]), 'localization/lang')\\n if os.path.exists(path):\\n lang = gettext.translation('UnrulyPuzzlePython', path, [_locale],\\n fallback=True)\\n else:\\n lang = gettext.translation('UnrulyPuzzlePython', path,\\n fallback=True)\\n return lang.gettext\",\n \"def translation_unit(self):\\r\\n # If this triggers an AttributeError, the instance was not properly\\r\\n # instantiated.\\r\\n return self._tu\",\n \"def GetTranslation(*args):\\n return _gdi_.GetTranslation(*args)\",\n \"def test_simple_translation_using_get(self):\\n pass\",\n \"def init_translations():\\n if \\\"@lang\\\" in input.load_input():\\n lang = input.get_lang()\\n try:\\n trad = gettext.GNUTranslations(open(\\\"../course/common_student/$i18n/\\\" + lang + \\\".mo\\\", \\\"rb\\\"))\\n except FileNotFoundError:\\n trad = gettext.NullTranslations()\\n trad.install()\\n return lang\\n trad = gettext.NullTranslations()\\n trad.install()\\n return \\\"en\\\"\",\n \"def get_translated_tweet(tweet, language='it'):\\n translated = GoogleTranslator(source='auto', target=language)\\n return translated.translate(tweet)\",\n \"def get_localization(self, language: str) -> Localization:\\n ...\",\n \"def gettext_for(locale='en'):\\n return Translations.load(\\n os.path.join(BASEDIR, 'app', 'translations'), [locale]\\n ).ugettext\",\n \"def get_translation ( self ):\\n self.verify_post_data ( )\\n\\n text = request.json[ 'text' ]\\n src_lang = request.json[ 'source_lang' ]\\n target_lang = request.json[ 'target_lang' ]\\n\\n # if translation is available in cache, just fetch it from there. Otherwise use translation service.\\n translated_text = self.get_set_translation_from_cache ( text, src_lang, target_lang )\\n\\n return jsonify ( {\\\"Translation\\\": translated_text} )\",\n \"def test_get_translation_resources(self):\\n pass\",\n \"def translation(self):\\n return self._translation\",\n \"def get_translation(self):\\n trans_keys = ''.join(self._trans_dict.keys())\\n trans_values = ''.join(self._trans_dict.values())\\n\\n trans_table = string.maketrans(trans_keys, trans_values)\\n translation = self._puzzle.translate(trans_table)\\n return translation\",\n \"def get_translation(obj, language_code):\\n if not obj or not hasattr(obj, \\\"get_translation\\\"):\\n return None\\n return obj.get_translation(language_code)\",\n \"def gettext_translate( s ):\\n return catalogs.translate(s)\",\n \"def get_translation(self, command, keyword=None, item=None):\\n key = item if item is not None else \\\\\\n keyword if keyword is not None else command\\n cata = self.get_catalog(command)\\n if cata is not None:\\n dtr = cata.definition.get(\\\"translation\\\", {})\\n trans = dtr.get(key)\\n if trans is not None:\\n return unicode(trans, 'utf-8')\\n return GLOBAL_DICT.get(key, key)\",\n \"def test_get_machine_translate_settings_for_project_template(self):\\n pass\",\n \"def ugettext(self):\\n return self._translations.gettext\",\n \"def get_translation_from_cache ( self, text, src_lang, target_lang ):\\n return self.app_cache.get_translation_from_cache ( text, src_lang, target_lang )\",\n \"def _lazy_ugettext(text: str):\\n try:\\n # Test if context is available,\\n # cf. https://github.com/tracim/tracim/issues/173\\n context = StackedObjectProxy(name=\\\"context\\\")\\n context.translator\\n return ugettext(text)\\n except TypeError:\\n return text\",\n \"def GetUtilWrapper(self):\\n return ''\",\n \"def translate():\\n pass\",\n \"def translations(self):\\r\\n return Translations(self)\",\n \"def translations(self):\\r\\n return Translations(self)\",\n \"def get_translation(self, lang):\\n if lang==\\\"it\\\":\\n url=self.backstring+\\\"en/\\\"+self.name\\n image=self.backstring+\\\"img/uk.png\\\"\\n alttext='English version'\\n elif lang==\\\"en\\\":\\n url=self.backstring+\\\"it/\\\"+self.name\\n image=self.backstring+\\\"img/it.png\\\"\\n alttext='Italian version'\\n img='<img src=\\\"%s\\\" height=\\\"15\\\" alt=\\\"%s\\\"><br>%s' % (image, alttext,alttext, )\\n a=A(img, url, \\\"translation\\\")\\n return str(a)\",\n \"def fake_ugettext(translations):\\r\\n def _ugettext(text): # pylint: disable=missing-docstring\\r\\n return translations.get(text, text)\\r\\n return _ugettext\",\n \"def translate(sentence,target,api_key):\\n #translate without using googletrans wrapper library\\n URL = \\\"https://translation.googleapis.com/language/translate/v2?target=\\\"+target+\\\"&key=\\\"+api_key+\\\"&q=\\\"+sentence\\n # sending get request and saving the response as response object \\n r = requests.get(url = URL)\\n\\n if r.status_code == 200:\\n # extracting data in json format \\n data = r.json()\\n return data['data']['translations'][0]['translatedText']\",\n \"def GetUtilWrapper(self):\\n return self.GetTestWrapper()\",\n \"def resolve_translation(obj, _):\\n return obj.translation.decode()\",\n \"def getTranslationBundle(self, language=None):\\n\\n if language is None:\\n return None\\n\\n if language in self.__translationBundles:\\n return self.__translationBundles[language]\\n\\n Console.info(\\\"Creating translation bundle: %s\\\", language)\\n Console.indent()\\n\\n # Initialize new Translation object with no project assigned\\n # This object is used to merge all seperate translation instances later on.\\n combined = jasy.item.Translation.TranslationItem(None, id=language)\\n relevantLanguages = self.__expandLanguage(language)\\n\\n # Loop structure is build to prefer finer language matching over project priority\\n for currentLanguage in reversed(relevantLanguages):\\n for project in self.__projects:\\n for translation in project.getTranslations().values():\\n if translation.getLanguage() == currentLanguage:\\n Console.debug(\\\"Adding %s entries from %s @ %s...\\\", len(translation.getTable()), currentLanguage, project.getName())\\n combined += translation\\n\\n Console.info(\\\"Combined number of translations: %s\\\", len(combined.getTable()))\\n Console.outdent()\\n\\n self.__translationBundles[language] = combined\\n return combined\",\n \"def _extract_18n_messages():\\n BabelCLI().run(['', 'extract', '-F', 'babel.cfg', '-k', '_t', '--no-location', '--sort-output',\\n '--omit-header', '-o', os.path.join(I18N_PATH, 'messages.pot'), 'aliquis'])\",\n \"def buildTranslation(lang,suppressAlert=False):\\n global string_cache\\n fName = os.path.join(langPath, lang + \\\".trn\\\")\\n if verifyLangCode(lang) and not lang == \\\"en_US\\\":\\n data = open(fName, \\\"rb\\\").read() + \\\"\\\\x00\\\"\\n trnPattern = re.compile(r\\\"^o\\\\d+[ ]|^t\\\\d+[ ]\\\", re.M|re.S)\\n grps = re.finditer(trnPattern, data)\\n oStart = -1\\n oEnd = -1\\n tStart = -1\\n tEnd = -1\\n org = None\\n for grp in grps:\\n g = grp.group()\\n if g.startswith(\\\"o\\\"):\\n oStart = grp.end()\\n tEnd = grp.start() -1\\n elif g.startswith(\\\"t\\\"):\\n oEnd = grp.start() -1\\n tStart = grp.end()\\n if oStart > -1 and oEnd > -1 and tStart > tEnd:\\n org = data[oStart:oEnd]\\n if tStart > -1 and (tEnd > -1):\\n if tEnd > tStart:\\n string_cache[org] = correctEncoding(data[tStart:tEnd])\\n tStart = -1\\n tEnd = -1\\n string_cache[org] = correctEncoding(data[tStart:tEnd -1])\\n return string_cache\",\n \"def interpolate_insted_of_translate(\\n self, msgid, mapping=None, *args, **kw): # pragma: no cover webdriver\\n return zope.i18n.interpolate(msgid, mapping)\",\n \"def getText(str, request, lang, formatted=True):\\n # TODO: Should move this into a language instance. request.lang should be a language instance.\\n\\n # load texts if needed\\n global _text_cache\\n if not lang in _text_cache:\\n (texts, unformatted) = loadLanguage(request, lang)\\n # XXX add error handling\\n _text_cache[lang] = texts\\n _unformatted_text_cache[lang] = unformatted\\n\\n # get the matching entry in the mapping table\\n trans = str\\n try:\\n if formatted:\\n trans = _text_cache[lang][str]\\n else:\\n trans = _unformatted_text_cache[lang][str]\\n except KeyError:\\n try:\\n language = languages[lang][ENAME]\\n dictpagename = \\\"%sDict\\\" % language\\n dicts = request.dicts\\n if dicts.has_dict(dictpagename):\\n userdict = dicts.dict(dictpagename)\\n trans = userdict[str]\\n else:\\n raise KeyError\\n except KeyError:\\n # do not simply return trans with str, but recursively call\\n # to get english translation, maybe formatted.\\n # if we don't find an english \\\"translation\\\", we just format it\\n # on the fly (this is needed for cfg.editor_quickhelp).\\n if lang != 'en':\\n trans = getText(str, request, 'en', formatted)\\n elif formatted:\\n trans = formatMarkup(request, str)\\n\\n return trans\",\n \"def translate(self, language=None):\",\n \"def get(module, lang=\\\"en_US\\\"):\\n\\n module = import_module(\\n \\\"._lang.{}.{}\\\".format(subdir(lang), module), package=__package__\\n )\\n return module\",\n \"def gettext_translate(string):\\n # Try default translation first\\n translation = localizer.old_translate(i18n.TranslationString(string))\\n if translation == string:\\n # translation failed then use FormEncode\\n translation = formencode_api._stdtrans(string)\\n return translation\",\n \"def translate_wrapper(atext):\\n print(\\\"translating:\\\",atext)\\n res=\\\"\\\"\\n res=translate(atext,\\\"pl\\\",\\\"fr\\\")\\n time.sleep(0.5)\\n print(\\\"translation:\\\",res)\\n return res\",\n \"def fake_ugettext(translations):\\n def _ugettext(text):\\n return translations.get(text, text)\\n return _ugettext\",\n \"def util():\\n pass\",\n \"def util():\\n pass\",\n \"def get_translation(translated_text_json, language=None):\\n if not language:\\n language = translation.get_language()\\n\\n translations = translated_text_json or {}\\n en = translations.get('en')\\n translated = translations.get(language)\\n return translated or en or ''\",\n \"def localize(self, msg):\\n return self.translations.get(msg, msg)\",\n \"def localize(self, msg):\\n return self.translations.get(msg, msg)\",\n \"def get_set_translation_from_cache ( self, text, src_lang, target_lang ):\\n translated_text = self.get_translation_from_cache ( text, src_lang, target_lang )\\n if not translated_text:\\n translated_text = self.service_creator.get_translation ( text, src_lang, target_lang )\\n self.set_translation_to_cache ( text, src_lang, target_lang, translated_text )\\n return translated_text\",\n \"def trans(self):\\n return self._base.trans\",\n \"def translate(self):\\n pass\",\n \"def get_translation(location):\\r\\n return self.loc_mapper.translate_location(location, published, add_entry_if_missing=True)\",\n \"def get_subreddit_translation(subreddit, bible_section):\\n\\n if bible_section == \\\"Old Testament\\\":\\n section = \\\"ot_default\\\"\\n elif bible_section == \\\"New Testament\\\":\\n section = \\\"nt_default\\\"\\n else:\\n section = \\\"deut_default\\\"\\n with _conn.cursor() as cur:\\n cur.execute(\\\"SELECT %s FROM subreddit_translations WHERE sub = '%s';\\\" % (section, subreddit.lower()))\\n try:\\n trans = cur.fetchone()[0]\\n return trans\\n except (psycopg2.ProgrammingError, TypeError) as e:\\n return None\",\n \"def utility(self) -> str:\\n return self._utility\",\n \"def _translate(self, key):\\n return self.TRANSLATE.get(key, key)\",\n \"def _getTranslationFromURL(self, translation, encoding='utf-8'):\\n file_url = self._quranFilePath + translation + '.json'\\n data_response = urlopen(file_url)\\n json_data = data_response.read()\\n json_str = json_data.decode(encoding)\\n return json.loads(json_str)\",\n \"def locale_factory(factory):\\n global _get_locale\\n _get_locale = factory\\n return factory\",\n \"def getUtilities(self):\\n\\n return Utilities(self)\",\n \"def question_new_translate():\",\n \"def translate():\\n text = request.args.get('text')\\n\\n # Send a request to Google's Translate REST API using your API credentials defined above\\n ans = service.translations().list(source='en', target='zh-CN', q=text).execute()\\n\\n # Return translated text back to user\\n return ans['translations'][0]['translatedText']\",\n \"def get_localization(self):\\n return self._request_data(\\\"/lokarria/localization\\\")\",\n \"def get_text(self):\\n try:\\n return self.get_translation().text\\n except MissingTranslation:\\n return _(\\\"(No text)\\\")\",\n \"def translate_one(text, lang, format='plain'):\\n if not isinstance(text, basestring):\\n raise TypeError('Text should be a string, not a %s' % type(text).__name__)\\n return translate_many(text, lang, format)[0]\",\n \"def gettext(self, message):\\n if self._translations.has_key(message):\\n return self._translations[message]\\n return super(Translations, self).gettext(message)\",\n \"def get_transformer() -> TransformationFunc:\\n THREAD_LOCALS = access_thread_locals()\\n try:\\n transformer = THREAD_LOCALS.XMLSnippet_00000001_transformer_singleton\\n except AttributeError:\\n THREAD_LOCALS.XMLSnippet_00000001_transformer_singleton = CreateXMLSnippetTransformer()\\n transformer = THREAD_LOCALS.XMLSnippet_00000001_transformer_singleton\\n return transformer\",\n \"def get_utility(s, direction):\\n new_pos = transition(s, direction)\\n new_utility = utilities[new_pos[0]][new_pos[1]]\\n return new_utility\",\n \"def _getText(self, item): # TODO: move this method to more suitable place when possible (scripting base class)\\r\\n if not isinstance(item, basestring):\\r\\n return item\\r\\n\\r\\n translation = self.phone.getTranslation(item)\\r\\n if translation != None:\\r\\n return translation\\r\\n\\r\\n return item\",\n \"def get_unbabel_translation(uid, lang):\\n url = \\\"http://sandbox.unbabel.com/tapi/v2/translation/{}/\\\".format(uid)\\n resp = do_request('GET', url, headers=UNBABEL_HEADERS)\\n data = resp.json()\\n db.stories.update_one({\\\"unbabel_uid_{}\\\".format(lang): uid},\\n {\\\"$set\\\": {\\\"unbabel_status_{}\\\".format(lang): data.get('status')}})\\n if \\\"completed\\\" == data.get('status'):\\n db.stories.update_one({\\\"unbabel_uid_{}\\\".format(lang): uid},\\n {\\\"$set\\\": {\\\"title_{}\\\".format(lang): data.get('translatedText')}})\\n return [{\\\"unbabel_uid_{}\\\".format(lang): uid, \\\"status\\\": data.get('status')}]\",\n \"def setup_listener_gettext(self):\\n\\t\\tth = current_thread()\\n\\n\\t\\t#print '>> listener gettext in ', th.name\\n\\n\\t\\ttry:\\n\\t\\t\\tth._ = th._licornd.langs[th.listener.lang].ugettext\\n\\n\\t\\texcept KeyError:\\n\\t\\t\\t# the daemon doesn't have the client lang installed. Not a problem.\\n\\t\\t\\t# Still, make a shortcut to the daemon's default translator to\\n\\t\\t\\t# avoid trigerring an exception at every call of our translator\\n\\t\\t\\t# wrapper.\\n\\t\\t\\tth._ = __builtin__.__dict__['_orig__']\",\n \"def get_name_translation(self):\\n\\t\\treturn frappe.get_value(\\n\\t\\t\\t\\\"Translation\\\",\\n\\t\\t\\t{\\\"source_text\\\": self.doc_type, \\\"language\\\": frappe.local.lang or \\\"en\\\"},\\n\\t\\t\\t[\\\"name\\\", \\\"translated_text\\\"],\\n\\t\\t\\tas_dict=True,\\n\\t\\t)\",\n \"def _get_converter(orig, target):\\n try:\\n func = getattr(utils, f'convert_{orig}_to_{target}')\\n except AttributeError:\\n func = partial(convert_unit, orig=orig, to=target)\\n return func\",\n \"def translate_as(self, lang):\\n trans = ItemTranslation.objects.filter(item=self,\\n lang=lang,\\n is_active=True)\\n if trans: return trans.first()\\n else: return self\",\n \"def getTRSLanguage():\\n try:\\n return sys.argv[1]\\n except IndexError as error:\\n print(\\\"No language argument\\\\n\\\")\\n sys.exit()\",\n \"def __loadTranslator(self):\\n if self.__ui is not None:\\n loc = self.__ui.getLocale()\\n if loc and loc != \\\"C\\\":\\n locale_dir = os.path.join(\\n os.path.dirname(__file__), \\\"ProjectDjango\\\", \\\"i18n\\\")\\n translation = \\\"django_{0}\\\".format(loc)\\n translator = QTranslator(None)\\n loaded = translator.load(translation, locale_dir)\\n if loaded:\\n self.__translator = translator\\n e5App().installTranslator(self.__translator)\\n else:\\n print(\\\"Warning: translation file '{0}' could not be\\\"\\n \\\" loaded.\\\".format(translation))\\n print(\\\"Using default.\\\")\",\n \"def get_translation_for_object(self, lang, obj=None, model=None, object_id=None):\\n\\n # Gets object model and pk if informed\\n if obj:\\n model = type(obj)\\n object_id = obj.pk\\n\\n cache_key = self.make_cache_key(model, object_id, lang)\\n \\n # Checks if there is a cached object for this\\n from_cache = cache.get(cache_key, None)\\n\\n if from_cache:\\n return from_cache\\n\\n # Gets the related content type\\n c_type = ContentType.objects.get_for_model(model)\\n\\n # Gets the translation\\n trans = self.get(language=lang, content_type=c_type, object_id=object_id)\\n\\n # Stores in cache\\n cache.set(cache_key, trans)\\n\\n # Returns the translation object\\n return trans\",\n \"def get(self, msgid):\\r\\n return self.trans.get(msgid, str(msgid))\",\n \"def get_text(value):\\r\\n if value is None:\\r\\n return None\\r\\n else:\\r\\n return self.runtime.service(self, \\\"i18n\\\").ugettext(value)\",\n \"def getTranslation(table, language = None):\\n\\n return cache.codeTableFind(table, language)\",\n \"def get_translation_db(seqDB):\\n try:\\n return seqDB.translationDB\\n except AttributeError: # create a new TranslationAnnot DB\\n tdb = TranslationDB(seqDB)\\n try:\\n seqDB.translationDB = tdb\\n except AttributeError:\\n pass # won't let us cache? Just hand back the TranslationDB\\n return tdb\",\n \"def test_supported_translations_retrieval(self):\\n\\t\\t\\n\\t\\thelpers.find_supported_translations()\\n\\t\\tself.assertTrue(helpers.get_supported_translations() != 0)\",\n \"def try_translation(text):\\n try:\\n trad = Translator().translate(text = text, dest = 'fr')\\n except:\\n sleep(random() * 60)\\n return False\\n \\n return trad.text\",\n \"def __init__(self, language=None):\\n self.language = language\\n self.translations = {}\",\n \"def translate(atext,fromlang,tolang):\\n rtext=\\\"\\\"\\n \\n base_link = \\\"http://translate.google.com/m?hl=%s&sl=%s&q=%s\\\"\\n url = base_link % (tolang, fromlang, atext)\\n print(url)\\n headers={'Accept-Charset': 'utf-8'}\\n r = requests.get(url,headers=headers)\\n content = r.content.decode('utf-8')\\n\\n if r.status_code != 200:\\n print(\\\"ERROR\\\")\\n print(r.status_code)\\n print(r.headers)\\n print(\\\"content\\\",content)\\n time.sleep(1)\\n else:\\n soup = bs4.BeautifulSoup(content,'html.parser')\\n # print(soup) # div class=\\\"t0\\\"\\n res=soup.find(\\\"div\\\",attrs={\\\"class\\\":\\\"t0\\\"})\\n # print(\\\"res:\\\",res)\\n print(\\\"res.text:\\\",res.text)\\n rtext=res.text\\n return rtext\",\n \"def machine_translation(request):\\n log.debug(\\\"Get translation from machine translation service.\\\")\\n\\n try:\\n text = request.GET['text']\\n locale = request.GET['locale']\\n check = request.GET['check']\\n except MultiValueDictKeyError as e:\\n log.error(str(e))\\n return HttpResponse(\\\"error\\\")\\n\\n if hasattr(settings, 'MICROSOFT_TRANSLATOR_API_KEY'):\\n api_key = settings.MICROSOFT_TRANSLATOR_API_KEY\\n else:\\n log.error(\\\"MICROSOFT_TRANSLATOR_API_KEY not set\\\")\\n return HttpResponse(\\\"apikey\\\")\\n\\n obj = {}\\n\\n # On first run, check if target language supported\\n if check == \\\"true\\\":\\n supported = False\\n languages = settings.MICROSOFT_TRANSLATOR_LOCALES\\n\\n if locale in languages:\\n supported = True\\n\\n else:\\n for lang in languages:\\n if lang.startswith(locale.split(\\\"-\\\")[0]): # Neutral locales\\n supported = True\\n locale = lang\\n break\\n\\n if not supported:\\n log.debug(\\\"Locale not supported.\\\")\\n return HttpResponse(\\\"not-supported\\\")\\n\\n obj['locale'] = locale\\n\\n url = \\\"http://api.microsofttranslator.com/V2/Http.svc/Translate\\\"\\n payload = {\\n \\\"appId\\\": api_key,\\n \\\"text\\\": text,\\n \\\"from\\\": \\\"en\\\",\\n \\\"to\\\": locale,\\n \\\"contentType\\\": \\\"text/html\\\",\\n }\\n\\n try:\\n r = requests.get(url, params=payload)\\n log.debug(r.content)\\n\\n # Parse XML response\\n root = ET.fromstring(r.content)\\n translation = root.text\\n obj['translation'] = translation\\n\\n return HttpResponse(json.dumps(obj), content_type='application/json')\\n\\n except Exception as e:\\n log.error(e)\\n return HttpResponse(\\\"error\\\")\",\n \"def get_translation(self):\\n self.activate_navigation_constrain()\\n return self.tx, self.ty\",\n \"def translation_key(self) -> str | None:\\n return TRANSLATION_KEY_MAPPING.get(self.capability.id)\",\n \"def tr(self, message):\\n # noinspection PyTypeChecker,PyArgumentList,PyCallByClass\\n return QCoreApplication.translate('TFB_Tools3', message)\",\n \"def get_lang(ix):\\n\\tlang = None\\n\\tif ix == 0:\\n\\t\\tlang = setting.TLA_ENG\\n\\telif ix == 1:\\n\\t\\tlang = setting.TLA_JP\\n\\telse:\\n\\t\\tlang = setting.TLA_VN\\n\\n\\tf = open (f\\\"lang\\\\\\\\{lang}.json\\\", encoding=setting.TLA_UTF8)\\n\\tglobal data_json\\n\\tdata_json = json.load(f)\\n\\n\\treturn lang\",\n \"def get_user_translation(username, bible_section):\\n translation = None\\n\\n if bible_section == \\\"Old Testament\\\":\\n section = \\\"ot_default\\\"\\n elif bible_section == \\\"New Testament\\\":\\n section = \\\"nt_default\\\"\\n else:\\n section = \\\"deut_default\\\"\\n with _conn.cursor() as cur:\\n cur.execute(\\\"SELECT %s FROM user_translations WHERE username = '%s';\\\" % (section, str(username)))\\n try:\\n translation = cur.fetchone()[0]\\n except (psycopg2.ProgrammingError, TypeError) as e:\\n translation = None\\n cur.execute(\\\"UPDATE user_translations SET last_used = NOW() WHERE username = '%s'\\\" % str(username))\\n _conn.commit()\\n return translation\",\n \"def main():\\n # Parse args\\n parser = argparse.ArgumentParser(description=\\\"Open-source offline translation.\\\\n\\\")\\n parser.add_argument(\\n \\\"text\\\",\\n nargs=\\\"?\\\",\\n metavar=\\\"TEXT\\\",\\n help=\\\"The text to translate. Read from standard input if missing.\\\",\\n )\\n parser.add_argument(\\n \\\"--from-lang\\\",\\n \\\"-f\\\",\\n help=\\\"The code for the language to translate from (ISO 639-1)\\\",\\n )\\n parser.add_argument(\\n \\\"--to-lang\\\", \\\"-t\\\", help=\\\"The code for the language to translate to (ISO 639-1)\\\"\\n )\\n args = parser.parse_args()\\n\\n from_and_to_lang_provided = args.from_lang is not None and args.to_lang is not None\\n\\n # Get text to translate\\n if args.text:\\n # argos-translate-cli --from-lang en --to-lang es \\\"Text to translate\\\"\\n text_to_translate = args.text\\n elif from_and_to_lang_provided:\\n # echo \\\"Text to translate\\\" | argos-translate-cli --from-lang en --to-lang es\\n text_to_translate = \\\"\\\".join(sys.stdin)\\n else:\\n # argos-translate\\n parser.print_help()\\n return\\n\\n # Perform translation\\n if from_and_to_lang_provided:\\n installed_languages = {\\n lang.code: lang for lang in translate.load_installed_languages()\\n }\\n if args.from_lang not in installed_languages:\\n parser.error(\\\"{!r} is not an installed language.\\\".format(args.from_lang))\\n if args.to_lang not in installed_languages:\\n parser.error(\\\"{!r} is not an installed language.\\\".format(args.to_lang))\\n from_lang = installed_languages[args.from_lang]\\n to_lang = installed_languages[args.to_lang]\\n translation = from_lang.get_translation(to_lang)\\n if translation is None:\\n parser.error(\\n f\\\"No translation installed from {args.from_name} to {args.to_name}\\\"\\n )\\n else:\\n translation = translate.IdentityTranslation(\\\"\\\")\\n\\n # Print translation\\n print(translation.translate(text_to_translate))\",\n \"def fetchTranslation(self, language):\\n pass\",\n \"def get_transformer() -> TransformerFunc:\\n THREAD_LOCALS = access_thread_locals()\\n try:\\n transformer = THREAD_LOCALS.XML_00000001_transformer_singleton\\n except AttributeError:\\n THREAD_LOCALS.XML_00000001_transformer_singleton = CreateXMLTransformer()\\n transformer = THREAD_LOCALS.XML_00000001_transformer_singleton\\n return transformer\",\n \"def translate(self, phrase):\\n\\n if phrase not in TRANSLATIONS or self.ui_lang_code not in TRANSLATIONS[phrase]:\\n return phrase\\n return TRANSLATIONS[phrase][self.ui_lang_code]\",\n \"def translateText(text):\\r\\n\\treturn translator.translate(text, src='en', dest='ro')\",\n \"def get_translation_table(\\n global_table: Union[str, Dict] = None, \\n local_table: Union[str, Dict] = None,\\n logger = None,\\n) -> TranslationTable:\\n\\n global_tt = {}\\n local_tt = {}\\n\\n if not global_table:\\n if local_table:\\n raise ValueError(\\\"Local table without a global table not allowed\\\")\\n else:\\n logger.debug(\\\"No global table used for transform\\\")\\n else:\\n\\n if isinstance(global_table, str):\\n with open(global_table, 'r') as global_tt_fh:\\n global_tt = yaml.safe_load(global_tt_fh)\\n elif isinstance(global_table, Dict):\\n global_tt = global_table\\n\\n if local_table:\\n if isinstance(local_table, str):\\n with open(local_table, 'r') as local_tt_fh:\\n local_tt = yaml.safe_load(local_tt_fh)\\n elif isinstance(local_table, Dict):\\n local_tt = local_table\\n\\n else:\\n logger.debug(\\\"No local table used for transform\\\")\\n\\n return TranslationTable(global_tt, local_tt)\",\n \"def test_translation_smoke():\\n english_to_morse = get_translator(\\\"english\\\", \\\"morse\\\")\\n morse_to_english = get_translator(\\\"morse\\\", \\\"english\\\")\\n morse = english_to_morse.translate(\\\"hello world\\\")\\n english = morse_to_english.translate(morse)\\n assert english == \\\"HELLO WORLD\\\"\",\n \"def tr(message):\\r\\n # noinspection PyTypeChecker,PyArgumentList,PyCallByClass\\r\\n return QCoreApplication.translate('APIS', message)\",\n \"def trans_app(value):\\n return _(value.title())\",\n \"def test_i18n(self):\\n # If you want to modify this code, please get acquainted with\\n # Python's locale module. In particular:\\n # http://docs.python.org/library/locale.html#locale.getdefaultlocale\\n\\n # Set the standard C locale.\\n os.environ['LANG'] = 'C'\\n os.environ['LC_ALL'] = 'C.UTF-8'\\n\\n #must be after above\\n out1 = tr('Hello!')\\n expected1 = 'Hello!'\\n msg = 'Expected %s, got %s' % (expected1, out1)\\n assert out1 == expected1, msg\\n\\n # Set the Indonesian locale to test translations.\\n os.environ['LANG'] = 'id'\\n os.environ['LC_ALL'] = 'id_ID.UTF-8'\\n\\n #must be after above\\n #indoout1 = tr('Hello!') # translate as 'Hi'\\n #indoexpected1 = 'Hi!'\\n #msg = 'Expected %s, got %s' % (indoexpected1, indoout1)\\n #assert indoout1 == indoexpected1, msg\",\n \"def translate(self):\\n raise NotImplementedError('subclass must override this method')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.692908","0.6249203","0.6228129","0.62209576","0.6138939","0.6135836","0.61316675","0.60002774","0.588955","0.5859885","0.57699263","0.57648146","0.57179856","0.5707253","0.57066154","0.569066","0.56857437","0.5651666","0.56276864","0.5602541","0.55301696","0.5524024","0.55104494","0.54988366","0.5484072","0.54285127","0.5426388","0.5426388","0.5422646","0.54012835","0.5390232","0.5376401","0.535444","0.53518176","0.5303113","0.5271339","0.5269795","0.5249356","0.52431655","0.52408344","0.5240219","0.52333933","0.5227175","0.52109027","0.52109027","0.52000266","0.5197409","0.5197409","0.51939076","0.5189283","0.51806015","0.5176598","0.5165785","0.5165466","0.5134244","0.51311845","0.5124691","0.5098701","0.5089535","0.50894713","0.5088774","0.50729513","0.50728863","0.50706166","0.507037","0.50487626","0.5043052","0.5033359","0.5032883","0.5020289","0.5018651","0.5008871","0.49839553","0.49808523","0.49741524","0.49718273","0.4966837","0.4965813","0.49371108","0.49114162","0.4910368","0.49036586","0.48966393","0.48828515","0.48782364","0.48648366","0.48614895","0.48607382","0.48598713","0.48546135","0.48350853","0.4825878","0.48246127","0.47960958","0.47823563","0.47778392","0.4775271","0.47715738","0.47703195","0.47677222"],"string":"[\n \"0.692908\",\n \"0.6249203\",\n \"0.6228129\",\n \"0.62209576\",\n \"0.6138939\",\n \"0.6135836\",\n \"0.61316675\",\n \"0.60002774\",\n \"0.588955\",\n \"0.5859885\",\n \"0.57699263\",\n \"0.57648146\",\n \"0.57179856\",\n \"0.5707253\",\n \"0.57066154\",\n \"0.569066\",\n \"0.56857437\",\n \"0.5651666\",\n \"0.56276864\",\n \"0.5602541\",\n \"0.55301696\",\n \"0.5524024\",\n \"0.55104494\",\n \"0.54988366\",\n \"0.5484072\",\n \"0.54285127\",\n \"0.5426388\",\n \"0.5426388\",\n \"0.5422646\",\n \"0.54012835\",\n \"0.5390232\",\n \"0.5376401\",\n \"0.535444\",\n \"0.53518176\",\n \"0.5303113\",\n \"0.5271339\",\n \"0.5269795\",\n \"0.5249356\",\n \"0.52431655\",\n \"0.52408344\",\n \"0.5240219\",\n \"0.52333933\",\n \"0.5227175\",\n \"0.52109027\",\n \"0.52109027\",\n \"0.52000266\",\n \"0.5197409\",\n \"0.5197409\",\n \"0.51939076\",\n \"0.5189283\",\n \"0.51806015\",\n \"0.5176598\",\n \"0.5165785\",\n \"0.5165466\",\n \"0.5134244\",\n \"0.51311845\",\n \"0.5124691\",\n \"0.5098701\",\n \"0.5089535\",\n \"0.50894713\",\n \"0.5088774\",\n \"0.50729513\",\n \"0.50728863\",\n \"0.50706166\",\n \"0.507037\",\n \"0.50487626\",\n \"0.5043052\",\n \"0.5033359\",\n \"0.5032883\",\n \"0.5020289\",\n \"0.5018651\",\n \"0.5008871\",\n \"0.49839553\",\n \"0.49808523\",\n \"0.49741524\",\n \"0.49718273\",\n \"0.4966837\",\n \"0.4965813\",\n \"0.49371108\",\n \"0.49114162\",\n \"0.4910368\",\n \"0.49036586\",\n \"0.48966393\",\n \"0.48828515\",\n \"0.48782364\",\n \"0.48648366\",\n \"0.48614895\",\n \"0.48607382\",\n \"0.48598713\",\n \"0.48546135\",\n \"0.48350853\",\n \"0.4825878\",\n \"0.48246127\",\n \"0.47960958\",\n \"0.47823563\",\n \"0.47778392\",\n \"0.4775271\",\n \"0.47715738\",\n \"0.47703195\",\n \"0.47677222\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66220915"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":74585,"cells":{"query":{"kind":"string","value":"List all branches in current repository"},"document":{"kind":"string","value":"def list_branches(self, msg, args):\n trans = self._translation_util(msg)\n yield \"\\n\".join(trans.list_branches(REPOSITORY_NAME))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def branches(self) -> list[str]:\n _args: list[Arg] = []\n _ctx = self._select(\"branches\", _args)\n return _ctx.execute_sync(list[str])","def list_all_branches(self) -> dict:\n try:\n branches_response = self.repo.get_branches()\n branches_list = []\n for branch in branches_response:\n branches_list.append(branch.raw_data.get('name'))\n return make_success_response(200, branches_list)\n except GithubException as github_exc:\n return make_error_response(github_exc.status, github_exc.data)","def list_branches(self) -> List[str]:\n self.__verify_repo_initialized()\n branches = heads.get_branch_names(self._env.branchenv)\n return branches","def list_branches(repository: Repository):\n for branch in repository.branches:\n if branch == repository.head:\n print(\" * {}\".format(branch))\n else:\n print(\" {}\".format(branch))","def get_branches(self):\n\n # gets all branches in repository\n branches_endpoint = f'/repos/{self.repo}/branches'\n response = self._get_request(branches_endpoint)\n # guard condition\n if response.status_code != STATUS_CODE_OK:\n return None\n # deserialize\n branches_page = response.json()\n\n return [\n {\n 'name': branch['displayId']\n } for branch in branches_page['values']\n ]","def __gitBranchList(self):\n self.vcs.gitListTagBranch(self.project.getProjectPath(), False)","def get_branches(self):\n\n # gets all branches in repository\n branches_endpoint = f'/repositories/{self.owner}/{self.repo}/refs/branches'\n filter_param = {'fields': 'values.name'}\n response = self._get_request(branches_endpoint, filter_param)\n # guard condition\n if response.status_code != STATUS_CODE_OK:\n raise BitbucketRequestSenderExc(\n f'Invalid parameter(s) in: owner: {self.owner},'\n f' repo: {self.repo}')\n # deserialize\n branches_page = response.json()\n\n return [\n {\n 'name': branch['name']\n } for branch in branches_page['values']\n ]","def branches(self):\r\n url = '{0}/{1}'.format(self.get_url(), 'branches')\r\n\r\n return http.Request('GET', url), parsers.parse_json","def base_branches() -> list[str]:\n branches = []\n\n default = sh(\"git rev-parse --abbrev-ref origin/HEAD\").removeprefix(\"origin/\")\n branches.append(default)\n\n releases = sh(\n \"git branch --all --sort=-committerdate --list *release/* | head -10\"\n ).splitlines()\n releases = [b.removeprefix(\"*\").strip() for b in releases]\n branches.extend(releases)\n\n return branches","def branches_full(config, args):\n for b in config.repo.branches():\n yield config.repo.branch(b.name)","def get_branches(local_only=False, directory=None):\n cmd = 'git branch --no-color'\n if not local_only:\n cmd += ' -a'\n out = check_output(cmd, shell=True, cwd=directory)\n branches = []\n for line in out.splitlines():\n if line.count('HEAD -> ') > 0:\n continue\n if line.count('(no branch)') > 0:\n continue\n line = line.strip('*').strip()\n branches.append(line)\n return branches","def fetch_branches(self):\n for jrepo in self.json_repos['repos']:\n title = str(jrepo[\"title\"])\n self.branches[title] = str(jrepo['current'])","def branches(self):\r\n url = self.base_url + 'branches/'\r\n return json.loads(self.bb.load_url(url))","def branches(self):\r\n url = '{0}/branches/'.format(self.get_url())\r\n request = http.Request('GET', url)\r\n\r\n return request, parsers.parse_json","def _listBranches(self):\n assert self.wc.exists('branches')\n branches = self.wc.ls('branches')\n\n # Some early release branches used a different naming scheme\n # that doesn't sort properly with new-style release names. We\n # filter those out here, along with empty lines.\n branches = [b.strip('/') for b in branches\n if MELANGE_RELEASE_RE.match(b.strip('/'))]\n\n return sorted(branches)","def show_branches(config, args):\n for item in lib.input_json_lines():\n yield config.repo.branch(item)","def __gitMergedBranchList(self):\n self.vcs.gitListTagBranch(self.project.getProjectPath(), False,\n listAll=False, merged=True)","def get_branches(self, repo_name, cred_hash):\n branch_names = []\n\n url = f'{self.code_cloud_api.branch_api}/{repo_name}/branches?start=0&limit=30'\n response = self.code_cloud_api.get(url=url, cred_hash=cred_hash)\n if not response['status']:\n return response\n \n for item in response.get('data', {}).get('values', {}):\n branch_names.append(item.get('displayId', ''))\n\n return {'status': True, 'data': branch_names}","def get_branch_list(request, project_id):\n if request.method == 'GET':\n project_entry = GitProjectEntry.objects.filter(id=project_id).first()\n if project_entry is None:\n return res.get_response(404, 'project not found', {})\n\n branch_entries = GitBranchEntry.objects.filter(project=project_entry)\n\n branches = []\n for entry in branch_entries:\n obj = entry.as_object()\n merge_target_entry = GitBranchMergeTargetEntry.objects.filter(\n project=project_entry,\n current_branch=entry\n ).first()\n\n if merge_target_entry is not None:\n obj['target_branch_name'] = merge_target_entry.target_branch.name\n\n branches.append(obj)\n\n return res.get_response(200, '', branches)\n\n return res.get_only_get_allowed({})","def list_branches():\n a = App()\n print(tabulate(a.list_branches(), tablefmt=\"fancy_grid\"))","def find_branches(self, remote=False):\n arguments = ['--no-color']\n if remote:\n arguments.append('-r')\n #\n for branch in self.git.branch(*arguments).splitlines():\n branch = branch.replace('*', '').strip()\n if branch:\n yield branch\n #\n #","def make_branches(self, api_json=None):\n if api_json is None:\n return []\n\n obj = simplejson.loads(api_json)\n branches = [item[\"commit\"][\"sha\"] for item in obj]\n\n print branches\n\n return branches","def branches(self):\n return sorted([\n br[20:] for br in self.repo.refs.keys() if (\n br.startswith('refs/remotes/origin/') and\n br[20:] != 'HEAD'\n )\n ])","def branches_by_name(self, username, repository_name, access_token=None):\n return self._complete_request_by_name(\n username, repository_name, \"branches\", access_token)","def repos(request):\n # Clean up garbage created by buggy edits\n bad_branch_keys = models.Branch.query(models.Branch.owner == None).fetch(\n 100, keys_only=True)\n if bad_branch_keys:\n ndb.delete_multi(bad_branch_keys)\n repo_map = {}\n for repo in models.Repository.query().fetch(1000, batch_size=100):\n repo_map[repo.key] = repo\n branches = []\n for branch in models.Branch.query().fetch(2000, batch_size=100):\n repo_key = branch.repo_key\n if repo_key in repo_map:\n branch.repository = repo_map[repo_key]\n branches.append(branch)\n branches.sort(key=lambda b: map(\n unicode.lower, (b.repository.name, b.category, b.name)))\n return respond(request, 'repos.html', {'branches': branches})","def branch_list(repo, remote_name, pattern=None):\n # The return string for a remote reference is a single line with two\n # fields separated by a tab string. The first field is a commit hash.\n # The second field is the reference path. The unique part of the path\n # is the last field.\n #\n # 423f434cd877926ff47f3a710a7b0c414785515e\trefs/heads/enterprise-3.0\n\n lines = repo.git.ls_remote(remote_name, pattern, heads=True).split(\"\\n\")\n return [str(line.split('/')[-1]) for line in lines]","def branches_by_id(self, repository_id, access_token=None):\n return self._complete_request_by_id(\n repository_id, \"branches\", access_token)","def list_branches(self) -> PagingList[Branch]:\n return PagingList(lambda offset, limit: self._generate_branches(None, offset, limit), 128)","def get_branch_names(self):\n return [\n branch.name for branch in self.repo.branches\n ]","def branch(self, current_path):\n p = subprocess.Popen(\n [\"git\", \"show-ref\"],\n stdout=PIPE,\n stderr=PIPE,\n cwd=os.path.join(self.root_dir, current_path),\n )\n output, error = p.communicate()\n if p.returncode == 0:\n results = []\n try:\n current_branch = self.get_current_branch(current_path)\n for line in output.decode(\"utf-8\").splitlines():\n # The format for git show-ref is '<SHA-1 ID> <space> <reference name>'\n # For this method we are only interested in reference name.\n # Reference : https://git-scm.com/docs/git-show-ref#_output\n commit_sha = line.strip().split()[0].strip()\n reference_name = line.strip().split()[1].strip()\n if self._is_branch(reference_name):\n branch_name = self._get_branch_name(reference_name)\n is_current_branch = self._is_current_branch(\n branch_name, current_branch\n )\n is_remote_branch = self._is_remote_branch(reference_name)\n upstream_branch_name = None\n if not is_remote_branch:\n upstream_branch_name = self.get_upstream_branch(\n current_path, branch_name\n )\n tag = self._get_tag(current_path, commit_sha)\n results.append(\n {\n \"is_current_branch\": is_current_branch,\n \"is_remote_branch\": is_remote_branch,\n \"name\": branch_name,\n \"upstream\": upstream_branch_name,\n \"top_commit\": commit_sha,\n \"tag\": tag,\n }\n )\n\n # Remote branch is seleted use 'git branch -a' as fallback machanism\n # to get add detached head on remote branch to preserve older functionality\n # TODO : Revisit this to checkout new local branch with same name as remote\n # when the remote branch is seleted, VS Code git does the same thing.\n if current_branch == \"HEAD\":\n results.append(\n {\n \"is_current_branch\": True,\n \"is_remote_branch\": False,\n \"name\": self._get_detached_head_name(current_path),\n \"upstream\": None,\n \"top_commit\": None,\n \"tag\": None,\n }\n )\n return {\"code\": p.returncode, \"branches\": results}\n except Exception as downstream_error:\n return {\n \"code\": p.returncode,\n \"command\": \"git show-ref\",\n \"message\": str(downstream_error),\n }\n else:\n return {\n \"code\": p.returncode,\n \"command\": \"git show-ref\",\n \"message\": error.decode(\"utf-8\"),\n }","def get_branches(self, *, refs=[\"refs/heads\", \"refs/remotes\"]):\n # type: (Sequence[str]) -> List[Branch]\n stdout = self.git(\n \"for-each-ref\",\n (\n \"--format=\"\n \"%(HEAD)%00\"\n \"%(refname)%00\"\n \"%(upstream)%00\"\n \"%(upstream:remotename)%00\"\n \"%(upstream:track,nobracket)%00\"\n \"%(committerdate:unix)%00\"\n \"%(objectname)%00\"\n \"%(contents:subject)\"\n ),\n *refs\n ) # type: str\n branches = [\n branch\n for branch in (\n self._parse_branch_line(line)\n for line in filter_(stdout.splitlines())\n )\n if branch.name != \"HEAD\"\n ]\n store.update_state(self.repo_path, {\"branches\": branches})\n return branches","def get_branches( self ):\n\n branches = [ self ]\n\n for i in range( len( self.children ) ):\n branches.extend( self.children[i].get_branches() )\n\n return branches","def _get_pdc_branches(session, repo):\n branches_url = '{0}component-branches/'.format(PDCURL)\n params = dict(\n global_component=repo['name'],\n type=PDC_TYPES[repo['namespace']]\n )\n if VERBOSE:\n print('Querying {0} {1}'.format(branches_url, params))\n rv = session.get(branches_url, params=params, timeout=60)\n\n # If the project's branches can't be reported, just return no branches and\n # it will be skipped later on\n if not rv.ok:\n print(('The connection to \"{0}\" failed with the status code {1} and '\n 'output \"{2}\"'.format(branches_url, rv.status_code, rv.text)),\n file=sys.stderr)\n return []\n\n data = rv.json()\n return [branch['name'] for branch in data['results']]","def list_(ctx: click.Context, repository_path):\n root_commands.cmd_list(ctx.obj, repository_path)","def _get_branches(self):\n logging.info('--- Get Branches ---')\n self.local_branches = set(self.find_branches())\n self.remote_branches = set(self.find_branches(remote=True))\n # Tags are remote branches that start with \"tags/\".\n self.tags = {\n single_branch for single_branch in self.remote_branches\n if PRX_SVNTAGS_PREFIX.match(single_branch)}","def __gitShowBranch(self):\n self.vcs.gitShowBranch(self.project.getProjectPath())","def getBranch(self, repo=None):\n repos_list_url = \"/\".join([self.url, \"repos\", repo, \"branches\"])\n print repos_list_url\n request = urllib2.Request(repos_list_url, headers=self.headers)\n response = urllib2.urlopen(request)\n\n return response.read()","def __gitNotMergedBranchList(self):\n self.vcs.gitListTagBranch(self.project.getProjectPath(), False,\n listAll=False, merged=False)","def find_branches(self, commit, repo):\n ref_dict = repo.repo.refs.as_dict()\n branches = []\n for branch, branch_id in [(b, ref_dict[b]) for b in repo.branches]:\n obj = repo.repo[branch_id]\n if commit.id == obj.id:\n branches.append((branch, obj))\n return branches","def checkout_branches(args):\n\n ensure_tracking_branches([])\n if check_dirty([]) and '-f' not in args:\n raise Exception(\"Cannot checkout new branches with dirty projects.\")\n \n man = load_manifest()\n for (name, project) in man.projects.iteritems():\n print >>sys.stderr, \"Checking out tracking branch in project: %s\" % name\n repo = GitRepo(workdir_for_project(project))\n # Check that sucker out\n repo.check_command([\"checkout\", project.tracking_branch])","def repo_list(self):\n\n data, _ = self.helm_client.repo_list()\n return data","def list_all(self) -> List[RemoteInfo]:\n self.__verify_repo_initialized()\n res = []\n names = heads.get_remote_names(self._env.branchenv)\n for name in names:\n address = heads.get_remote_address(self._env.branchenv, name)\n res.append(RemoteInfo(name=name, address=address))\n return res","def search_project_branches(self, project):\n params = {\n 'project': project\n }\n resp = self.sonarqube.make_call('get', API_PROJECT_BRANCHES_LIST_ENDPOINT, **params)\n response = resp.json()\n return response['branches']","def __gitTagList(self):\n self.vcs.gitListTagBranch(self.project.getProjectPath(), True)","def git_ls_tree(branch: str = 'main'):\n branch = quote(branch)\n return f\"git ls-tree -r {branch} --name-only\"","def branches(self, active=False, closed=False):\n \n out = self._client.execute('branches', a=active, c=closed,\n debug=True)\n branches = {}\n\n for line in out.strip().splitlines():\n namerev, node = line.rsplit(':', 1)\n name, rev = namerev.rsplit(' ', 1)\n name = name.strip()\n node = node.split()[0] # To get rid of ' (inactive)'\n branches[name] = self._get_lazy(int(rev), node)\n\n return branches","def ticket_branches(self, msrp, cred_hash):\n branches = []\n repos = self.get_repos()\n if not repos['status']:\n return repos\n\n for repo in repos['data']:\n response = self.find_branch(repo_name=repo['name'], msrp=msrp, cred_hash=cred_hash)\n if response['status']:\n branches.append({'repo': repo['name'], 'branches': response['data'], 'all': response['all']})\n\n if len(branches) > 0:\n return {'status': True, 'data': branches}\n else:\n return {'status': False, 'data': f'No branches found with MSRP {msrp}'}","def get_branches(self):\n branches = []\n for bracket in self.__brackets:\n branches.append(bracket.get_branch())\n return branches","def git_branch():\n result, output = popen('git branch', False, False)\n branch = None\n for line in output:\n if line.startswith('*'):\n branch = line.split('*')[-1].strip()\n break\n return branch","def do_repo_list(self):\n return StringResult(self._repo_list.format_available_repos())","def list_all_repos_info():\n repos = ALL_REPOS\n for repo_name, repo in zip(repos, _repos(repos)):\n repo_name = shorten_path(repo_name)\n print(repo_name)\n try:\n nbr_ahead, nbr_behind = _nbr_commits_ahead_and_behind(repo)\n except git.exc.GitCommandError:\n print(f\" {repo.active_branch.name}\")\n except DetachedHeadError:\n print(f\" HEAD --> {repo.head.commit}\")\n else:\n nb_tabul = 3 if len(repo.active_branch.name) < 6 else 2\n tabuls = \"\\t\" * nb_tabul\n print(f\" {repo.active_branch.name}{tabuls}↓ {nbr_behind} ↑ {nbr_ahead}\")\n if repo.index.diff(None):\n print(\" !!! With unstaged changes !!!\")\n if repo.index.diff(\"HEAD\"):\n print(\" !!! With uncommited changes !!!\")","def branches(tree):\n\n return tree[1:]","def branches(tree):\n return tree[1:]","def dirty_branches(self):\n # If no course index has been set, then no branches have changed\n if self.index is None:\n return []\n\n # If there was no index in the database to start with, then all branches\n # are dirty by definition\n if self.initial_index is None:\n return list(self.index.get('versions', {}).keys())\n\n # Return branches whose ids differ between self.index and self.initial_index\n return [\n branch\n for branch, _id\n in self.index.get('versions', {}).items()\n if self.initial_index.get('versions', {}).get(branch) != _id\n ]","def listRepositories(self):\n return self.mini_catalog.listRepositories()","def get_raven_branches(plugin_type):\n url = f'https://github.com/autognc/ravenML-{plugin_type}-plugins'\n remote_refs = []\n g = git.cmd.Git()\n for ref in g.ls_remote(url).split('\\n'):\n ref = ref.split('\\t')[1]\n if ref.startswith(\"refs/heads/\"):\n remote_refs.append(ref[11:])\n return remote_refs","def get_commits(self):\n\n repo_commits = {}\n\n # gets all branches in repository\n branches = self.get_branches()\n if branches is None:\n return None\n\n # get list of commits pages from all branches in repository\n for branch in branches:\n list_of_branch_commits = self.get_commits_by_branch(branch['name'])\n if list_of_branch_commits is None:\n return None\n\n # adds key 'branches' with branch name in list to every commit in branch,\n # or if key 'branches' is existing add branch name to existing branches list\n for commit_in_branch in list_of_branch_commits:\n commit = repo_commits.get(commit_in_branch['hash'])\n if commit:\n commit['branches'].append(branch['name'])\n else:\n commit_in_branch['branches'] = [branch['name']]\n repo_commits[commit_in_branch['hash']] = commit_in_branch\n list_of_branch_commits.clear()\n\n # sorts all commits in repository by date in reverse order\n sorted_commits = sorted(list(repo_commits.values()), key=lambda x: x['date'], reverse=True)\n\n # forms a list of commits as an 'get commits API' response\n commits_amount = 30 if len(sorted_commits) >= 30 else len(sorted_commits)\n result_list = sorted_commits[:commits_amount]\n\n return result_list","def fetchall(self):\n try:\n self.repo.remotes.origin.fetch()\n except git.exc.GitCommandError as err:\n raise GitError(err)","def get_details(self, repo=None):\n api_json = []\n\n #get all branches from this repo\n branches = self.make_branches(self.getBranch(repo))\n\n today = datetime.date.today()\n yesterday = today - datetime.timedelta(2)\n\n for branch in branches:\n args = {\"per_page\": \"100\",\n \"sha\": branch,\n \"author\": self.username,\n \"since\": yesterday.isoformat()}\n args = self.make_args(args)\n repo_url = \"/\".join([self.url, \"repos\", repo, \"commits\"])\n repo_url = repo_url + args\n\n request = urllib2.Request(repo_url, headers=self.headers)\n response = urllib2.urlopen(request)\n raw_data = response.read()\n commits_info = self.process_factory(simplejson.loads(raw_data))\n api_json = api_json + commits_info\n\n print repo_url\n\n print api_json\n return api_json","def ls(tfr: TFR, pattern, details):\n tfr.hub.fetch_remotes()\n for repo in tfr.hub.ls(pattern):\n echo(repo.name)\n if details:\n echo(repo.html_url)\n echo(repo.ssh_url)","def buildList(self,list_all=False,push_all=False):\n print \"=== Loading\"\n\n ## Walk in repo and get GitRepo\n for repo in self.repos:\n if repo not in self.ignore or list_all:\n a = GitRepo(repo)\n if not self.animate:\n a.branchStatus()\n if a.globalStatus():\n if a.forward and push_all:\n a.push()\n self.gitrepos.append(a)\n\n print \"\\r=== \"+str(len(self.gitrepos))+\" repos scanned\"","def list_repos_cli(api_client, path_prefix, next_page_token):\n content = ReposApi(api_client).list(path_prefix, next_page_token)\n click.echo(pretty_format(content))","def branches(self):\n return self.in_lines + self.out_lines","def checkoutbranch(accountable, options):\n issue = accountable.checkout_branch(options)\n headers = sorted(['id', 'key', 'self'])\n rows = [headers, [itemgetter(header)(issue) for header in headers]]\n print_table(SingleTable(rows))","def branch(self, *arguments, **kwargs):\n return self.get_output('branch', *arguments, **kwargs)","def branch_names(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"branch_names\")","def get_all_commits(self):\n\n repo_commits = {}\n metadata = {}\n\n # gets all branches in repository\n branches = self.get_branches()\n if branches is None:\n return None\n\n # get list of commits pages from all branches in repository\n for branch in branches:\n list_of_branch_commits = self.get_all_commits_by_branch(branch['name'])\n\n if list_of_branch_commits is None:\n return None\n\n # adds key 'branches' with branch name in list to every commit in branch,\n # or if key 'branches' is existing add branch name to existing branches list\n for commit_in_branch in list_of_branch_commits:\n commit = repo_commits.get(commit_in_branch['hash'])\n if commit:\n commit['branches'].append(branch['name'])\n else:\n commit_in_branch['branches'] = [branch['name']]\n repo_commits[commit_in_branch['hash']] = commit_in_branch\n\n # add metadata to method response for further updates by get_updated_all_commits\n metadata[branch['name']] = list_of_branch_commits[0]\n\n list_of_branch_commits.clear()\n\n # sorts all commits in repository by date in reverse order\n sorted_commits = sorted(list(repo_commits.values()), key=lambda x: x['date'], reverse=True)\n\n return {'data': sorted_commits, 'metadata': metadata}","def track_branches(branches=None, directory=None):\n if type(branches) == str:\n branches = [branches]\n debug(\"track_branches(\" + str(branches) + \", \" + str(directory) + \")\")\n if branches == []:\n return\n # Save the current branch\n current_branch = get_current_branch(directory)\n try:\n # Get the local branches\n local_branches = get_branches(local_only=True, directory=directory)\n # Get the remote and local branches\n all_branches = get_branches(local_only=False, directory=directory)\n # Calculate the untracked branches\n untracked_branches = []\n for branch in all_branches:\n if branch.startswith('remotes/'):\n if branch.count('/') >= 2:\n branch = '/'.join(branch.split('/')[2:])\n if branch not in local_branches:\n untracked_branches.append(branch)\n # Prune any untracked branches by specified branches\n if branches is not None:\n branches_to_track = []\n for untracked in untracked_branches:\n if untracked in branches:\n branches_to_track.append(untracked)\n else:\n branches_to_track = untracked_branches\n # Track branches\n debug(\"Tracking branches: \" + str(branches_to_track))\n for branch in branches_to_track:\n checkout(branch, directory=directory)\n finally:\n if current_branch:\n checkout(current_branch, directory=directory)","def do_list(client, args):\n\trepos = client.repos.list(args.user)\n\tprint '%s has the following repositories:' % args.user\n\tprint 'Name - Description'\n\tfor repo in repos:\n\t\tprint '%s - %s' % (repo.name, repo.description)","def __gitSubmodulesList(self):\n self.vcs.gitSubmoduleList(self.project.getProjectPath())","def repo_init(_request):\n python = models.Repository.query(models.Repository.name == 'Python').get()\n if python is None:\n python = models.Repository(name='Python', url=SVN_ROOT)\n python.put()\n pybranches = []\n else:\n pybranches = list(models.Branch.query(models.Branch.repo_key == python.key))\n for category, name, url in BRANCHES:\n url = python.url + url\n for br in pybranches:\n if (br.category, br.name, br.url) == (category, name, url):\n break\n else:\n br = models.Branch(repo_key=python.key, repo_name='Python',\n category=category, name=name, url=url)\n br.put()\n return HttpResponseRedirect(reverse(repos))","def _get_branches_to_merge(branch):\n branches = [(branch, branch.subfolder or '')]\n for dependency in branch.branch_dependency_ids:\n branches.append((dependency.merge_with_branch_id, dependency.merge_subfolder or ''))\n return branches[::-1]","def branchStatus(self):\n ## Set stashed\n if self.repo.git.stash('list') != \"\":\n self.stashed = True\n else:\n self.stashed = False\n\n ## Set forward\n nb_status_ok = 0\n branch_status = {}\n branch_forward = {}\n verbose = []\n active_branch = self.repo.active_branch\n for branch in self.repo.branches:\n try:\n ## Checkout the branch (make it active)\n self.repo.git.checkout(str(branch))\n\n ## Extract the number of forward commit on the local branch\n regex = re.compile(r'Your branch .+ by (\\d+) commits?\\.',re.M)\n status = self.repo.git.status().split(\"\\n\")[1]\n if regex.search(status):\n branch_forward[str(branch)] = int(regex.sub(r'\\1',status))\n\n ## Get the status of the branch\n branch_status[str(branch)] = self.repo.git.status('--porcelain')\n verbose.append(self.repo.git.status())\n except GitCommandError:\n branch_status[str(branch)] = 'Error while retrieving branch infos'\n\n ## Go back to original branch\n self.repo.git.checkout(str(active_branch))\n\n ## Set self.forward\n self.status = branch_status\n self.forward = branch_forward\n self.verbose = verbose","def getSequenceBranches(self, show, sequence):\n branchesXML = XMLUtils.getXMLElementForClass(\"SequenceBranches\")\n mode = Mode(show, sequence)\n editorialFolder = mode.get(\"[editorialFolder]\")\n # Default Branch\n branchXML = ET.Element(\"Branch\")\n branchXML.attrib[\"name\"] = mode.get(\"[defaultBranch]\")\n branchesXML.append(branchXML)\n\n if self.fileService.exists(editorialFolder):\n # Other branches\n matches = mode.getMatches(\"[editorialFLEBranchFolder]\", {\"branch\":\"*\"})\n matches.sort(key=lambda match:match['branch'].lower())\n if len(matches) > 0:\n for i in range(0, len(matches)):\n branchXML = ET.Element(\"Branch\")\n branchXML.attrib[\"name\"] = str(matches[i]['branch'])\n branchesXML.append(branchXML)\n\n output = ET.tostring(branchesXML)\n\n# print \"ServerFlixFunctions - getSequenceBranches output:\", output\n\n return output","def print(self):\n print(\"Repository list: \")\n for repo in self.list:\n print(\"- \" + repo.name)","def get(self, project_slug):\n project = Project.query.filter_by(slug=project_slug).first_or_404()\n\n if not (project.public or current_user.is_authenticated()):\n flask_restful.abort(404)\n\n return [\n dict(name=job.git_branch)\n for job\n in (\n project.jobs.distinct(Job.git_branch)\n .order_by(sqlalchemy.asc(Job.git_branch))\n )\n if job.git_branch is not None\n ]","def branches_containing_commit(self, commit_hash, local_only=True, remote_only=False):\n branches = self.git(\n \"branch\",\n \"-a\" if not local_only and not remote_only else None,\n \"-r\" if remote_only else None,\n \"--contains\",\n commit_hash\n ).strip().split(\"\\n\")\n return [branch.strip() for branch in branches]","def collectAllBranches(self):\n\t\tallBranches = []\n\n\t\tfor unit in self.inputLayer:\n\n\t\t\tfor branch in unit.branchesOut:\n\n\t\t\t\tallBranches.append(branch)\n\n\t\tfor layer in self.hiddenLayers:\n\n\t\t\tfor unit in layer:\n\t\t\t\n\t\t\t\tfor branch in unit.branchesOut:\n\n\t\t\t\t\tallBranches.append(branch)\n\n\t\tfor unit in self.outputLayer:\n\n\t\t\tfor branch in unit.branchesOut:\n\n\t\t\t\tallBranches.append(branch)\n\n\n\t\treturn allBranches","def get_all(self, _filter=None):\n\t\tres = self.run(self._get_all_query(_filter))\n\t\treturn [BigQueryGithubEntry(_id, code, repo_name, path) for _id, code, repo_name, path in res]","def get_branch(self):\n if self._repository:\n return self._repository.dirstate.branch()","def list_repositories(self):\n repos = self.repo_conn.list_repositories()\n return repos","def stale_pr_branches(config, args):\n repo = config.repo\n for pr in repo.pull_requests(state=\"closed\"):\n if pr.head.repo == pr.base.repo and repo.branch(pr.head.ref):\n yield {\n \"html_url\": pr.html_url,\n \"base_branch\": pr.base.ref,\n \"head_branch\": pr.head.ref,\n }","def get_git_branch(pkg):\n git_folder = os.path.join(GIT_FOLDER, '%s.git' % pkg)\n if not os.path.exists(git_folder):\n print 'Could not find %s' % git_folder\n return set()\n\n head_folder = os.path.join(git_folder, 'refs', 'heads')\n return set(os.listdir(head_folder))","def list_repos(self):\n return sorted(self.user_con.list_repos())","def git_branch():\n git_branch = None\n branches = git(['branch']).split('\\n')\n pattern = re.compile(r'^\\*[ ]+(?P<branch>.*)$')\n for branch in branches:\n matches = pattern.match(branch)\n if matches:\n git_branch = matches.group('branch')\n return git_branch, '.', '.'","async def get_tree(\n self, ref: str or None = None\n ) -> [\"AIOGitHubAPIRepositoryTreeContent\"] or list:\n if ref is None:\n raise AIOGitHubAPIException(\"Missing ref\")\n _endpoint = f\"/repos/{self.full_name}/git/trees/{ref}\"\n _params = {\"recursive\": \"1\"}\n\n response = await self.client.get(endpoint=_endpoint, params=_params)\n\n return [\n AIOGitHubAPIRepositoryTreeContent(x, self.full_name, ref)\n for x in response.get(\"tree\", [])\n ]","def checkout_all(self, top_repo_path):\n my_output = subprocess.check_output(\n [\"git\", \"checkout\", \"--\", \".\"], cwd=top_repo_path\n )\n return my_output","def gh_pages_branch(repo):\n repo.git.branch('gh-pages')\n repo.git.checkout('gh-pages')","def current_branch():\n return subprocess.check_output('git branch --show-current'.split()).decode().strip()","def get_commits(self):\n\n repo_commits = {}\n\n # gets all branches in repository\n branches = self.get_branches()\n if branches is None:\n raise BitbucketRequestSenderExc('Can\\'t get branches for get_commits method')\n\n # get list of commits pages from all branches in repository\n for branch in branches:\n list_of_branch_commits = self.get_commits_by_branch(branch['name'])\n if list_of_branch_commits is None:\n raise BitbucketRequestSenderExc(\n 'Can\\'t get commits by branch for get_commits method')\n\n # adds key 'branches' with branch name in list to every commit in branch,\n # or if key 'branches' is existing add branch name to existing branches list\n for commit_in_branch in list_of_branch_commits:\n commit = repo_commits.get(commit_in_branch['hash'])\n if commit:\n commit['branches'].append(branch['name'])\n else:\n commit_in_branch['branches'] = [branch['name']]\n repo_commits[commit_in_branch['hash']] = commit_in_branch\n list_of_branch_commits.clear()\n\n # sorts all commits in repository by date in reverse order\n sorted_commits = sorted(list(repo_commits.values()), key=lambda x: x['date'], reverse=True)\n\n # forms a list of commits as an 'get commits API' response\n commits_amount = 30 if len(sorted_commits) >= 30 else len(sorted_commits)\n result_list = sorted_commits[:commits_amount]\n\n return result_list","def list_brands(self, **kwargs):\n url = self.api_url('brands')\n\n return requests.get(\n url,\n headers=self.auth_header,\n params=kwargs,\n ).json()","def get_branch():\n command = [\"git\", \"branch\", \"--show-current\"]\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\n branch_str = proc.stdout.readline()\n return branch_str.decode(\"utf-8\").rstrip()","def list(self, request, *args, **kwargs):\n queryset = BankConnections.objects.filter(user=self.request.user)\n \n response = [{\n \"connected\": True if connection.isTokenValid else False,\n \"bank\": connection.bank_branch.bank.id\n } for connection in queryset]\n\n return Response(response)","def test_branch_name_get(repository: Repository) -> None:\n branch = repository.branch(repository.head.name)\n assert repository.head.name == branch.name","def fetch_all_repos_info():\n\n def fetch(*args, **kwargs):\n kwargs[\"remote\"].fetch()\n\n repos = ALL_REPOS\n for repo_name, repo in zip(repos, _repos(repos)):\n repo_name = shorten_path(repo_name)\n print(f\"fetching {repo_name}\")\n _try_for_all_remotes(\n repo, fetch, raise_on_exception=False, stop_on_success=False, verbose=True\n )","def fetch_repos(self):\n logging.info(\"Fetching repositories in: %s\" % self.name)\n list_cmd = [\n \"az\",\n \"acr\",\n \"repository\",\n \"list\",\n \"-n\",\n self.name,\n \"-o\",\n \"tsv\",\n ]\n\n result = run_cmd(list_cmd)\n\n if result[\"returncode\"] != 0:\n logging.error(result[\"err_msg\"])\n raise AzureError(result[\"err_msg\"])\n\n logging.info(\"Successfully fetched repositories from: %s\" % self.name)\n repos = result[\"output\"].split(\"\\n\")[:-1]\n logging.info(\"Total number of repositories: %d\" % len(repos))\n\n return repos","def _branch(self):\n printer = Printer(None)\n ci_manager = CIManager(printer)\n return ci_manager.get_branch()","def fetch_repos(connection):\n\n try:\n response = connection.get_json('repository')\n except HTTPRequestError as ex:\n raise exception_from_http_error(ex) from ex\n\n result = response.get('result', [])\n return [Repository(connection, repo['rid'], data=repo) for repo in result]","def get_branch(name: Optional[str] = None,\n project: Optional[str] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetBranchResult:\n __args__ = dict()\n __args__['name'] = name\n __args__['project'] = project\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\n __ret__ = pulumi.runtime.invoke('gitlab:index/getBranch:getBranch', __args__, opts=opts, typ=GetBranchResult).value\n\n return AwaitableGetBranchResult(\n can_push=pulumi.get(__ret__, 'can_push'),\n commits=pulumi.get(__ret__, 'commits'),\n default=pulumi.get(__ret__, 'default'),\n developer_can_merge=pulumi.get(__ret__, 'developer_can_merge'),\n developer_can_push=pulumi.get(__ret__, 'developer_can_push'),\n id=pulumi.get(__ret__, 'id'),\n merged=pulumi.get(__ret__, 'merged'),\n name=pulumi.get(__ret__, 'name'),\n project=pulumi.get(__ret__, 'project'),\n protected=pulumi.get(__ret__, 'protected'),\n web_url=pulumi.get(__ret__, 'web_url'))","def get(self, repo: Repository):\n cache_key = self.cache_key.format(repo_id=repo.id.hex)\n\n result = redis.get(cache_key)\n if result is None:\n vcs = repo.get_vcs()\n if not vcs:\n return self.respond([])\n\n vcs.ensure()\n result = vcs.get_known_branches()\n redis.setex(cache_key, json.dumps(result), self.cache_expire)\n else:\n result = json.loads(result)\n\n return self.respond([{\"name\": r} for r in result])"],"string":"[\n \"def branches(self) -> list[str]:\\n _args: list[Arg] = []\\n _ctx = self._select(\\\"branches\\\", _args)\\n return _ctx.execute_sync(list[str])\",\n \"def list_all_branches(self) -> dict:\\n try:\\n branches_response = self.repo.get_branches()\\n branches_list = []\\n for branch in branches_response:\\n branches_list.append(branch.raw_data.get('name'))\\n return make_success_response(200, branches_list)\\n except GithubException as github_exc:\\n return make_error_response(github_exc.status, github_exc.data)\",\n \"def list_branches(self) -> List[str]:\\n self.__verify_repo_initialized()\\n branches = heads.get_branch_names(self._env.branchenv)\\n return branches\",\n \"def list_branches(repository: Repository):\\n for branch in repository.branches:\\n if branch == repository.head:\\n print(\\\" * {}\\\".format(branch))\\n else:\\n print(\\\" {}\\\".format(branch))\",\n \"def get_branches(self):\\n\\n # gets all branches in repository\\n branches_endpoint = f'/repos/{self.repo}/branches'\\n response = self._get_request(branches_endpoint)\\n # guard condition\\n if response.status_code != STATUS_CODE_OK:\\n return None\\n # deserialize\\n branches_page = response.json()\\n\\n return [\\n {\\n 'name': branch['displayId']\\n } for branch in branches_page['values']\\n ]\",\n \"def __gitBranchList(self):\\n self.vcs.gitListTagBranch(self.project.getProjectPath(), False)\",\n \"def get_branches(self):\\n\\n # gets all branches in repository\\n branches_endpoint = f'/repositories/{self.owner}/{self.repo}/refs/branches'\\n filter_param = {'fields': 'values.name'}\\n response = self._get_request(branches_endpoint, filter_param)\\n # guard condition\\n if response.status_code != STATUS_CODE_OK:\\n raise BitbucketRequestSenderExc(\\n f'Invalid parameter(s) in: owner: {self.owner},'\\n f' repo: {self.repo}')\\n # deserialize\\n branches_page = response.json()\\n\\n return [\\n {\\n 'name': branch['name']\\n } for branch in branches_page['values']\\n ]\",\n \"def branches(self):\\r\\n url = '{0}/{1}'.format(self.get_url(), 'branches')\\r\\n\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def base_branches() -> list[str]:\\n branches = []\\n\\n default = sh(\\\"git rev-parse --abbrev-ref origin/HEAD\\\").removeprefix(\\\"origin/\\\")\\n branches.append(default)\\n\\n releases = sh(\\n \\\"git branch --all --sort=-committerdate --list *release/* | head -10\\\"\\n ).splitlines()\\n releases = [b.removeprefix(\\\"*\\\").strip() for b in releases]\\n branches.extend(releases)\\n\\n return branches\",\n \"def branches_full(config, args):\\n for b in config.repo.branches():\\n yield config.repo.branch(b.name)\",\n \"def get_branches(local_only=False, directory=None):\\n cmd = 'git branch --no-color'\\n if not local_only:\\n cmd += ' -a'\\n out = check_output(cmd, shell=True, cwd=directory)\\n branches = []\\n for line in out.splitlines():\\n if line.count('HEAD -> ') > 0:\\n continue\\n if line.count('(no branch)') > 0:\\n continue\\n line = line.strip('*').strip()\\n branches.append(line)\\n return branches\",\n \"def fetch_branches(self):\\n for jrepo in self.json_repos['repos']:\\n title = str(jrepo[\\\"title\\\"])\\n self.branches[title] = str(jrepo['current'])\",\n \"def branches(self):\\r\\n url = self.base_url + 'branches/'\\r\\n return json.loads(self.bb.load_url(url))\",\n \"def branches(self):\\r\\n url = '{0}/branches/'.format(self.get_url())\\r\\n request = http.Request('GET', url)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def _listBranches(self):\\n assert self.wc.exists('branches')\\n branches = self.wc.ls('branches')\\n\\n # Some early release branches used a different naming scheme\\n # that doesn't sort properly with new-style release names. We\\n # filter those out here, along with empty lines.\\n branches = [b.strip('/') for b in branches\\n if MELANGE_RELEASE_RE.match(b.strip('/'))]\\n\\n return sorted(branches)\",\n \"def show_branches(config, args):\\n for item in lib.input_json_lines():\\n yield config.repo.branch(item)\",\n \"def __gitMergedBranchList(self):\\n self.vcs.gitListTagBranch(self.project.getProjectPath(), False,\\n listAll=False, merged=True)\",\n \"def get_branches(self, repo_name, cred_hash):\\n branch_names = []\\n\\n url = f'{self.code_cloud_api.branch_api}/{repo_name}/branches?start=0&limit=30'\\n response = self.code_cloud_api.get(url=url, cred_hash=cred_hash)\\n if not response['status']:\\n return response\\n \\n for item in response.get('data', {}).get('values', {}):\\n branch_names.append(item.get('displayId', ''))\\n\\n return {'status': True, 'data': branch_names}\",\n \"def get_branch_list(request, project_id):\\n if request.method == 'GET':\\n project_entry = GitProjectEntry.objects.filter(id=project_id).first()\\n if project_entry is None:\\n return res.get_response(404, 'project not found', {})\\n\\n branch_entries = GitBranchEntry.objects.filter(project=project_entry)\\n\\n branches = []\\n for entry in branch_entries:\\n obj = entry.as_object()\\n merge_target_entry = GitBranchMergeTargetEntry.objects.filter(\\n project=project_entry,\\n current_branch=entry\\n ).first()\\n\\n if merge_target_entry is not None:\\n obj['target_branch_name'] = merge_target_entry.target_branch.name\\n\\n branches.append(obj)\\n\\n return res.get_response(200, '', branches)\\n\\n return res.get_only_get_allowed({})\",\n \"def list_branches():\\n a = App()\\n print(tabulate(a.list_branches(), tablefmt=\\\"fancy_grid\\\"))\",\n \"def find_branches(self, remote=False):\\n arguments = ['--no-color']\\n if remote:\\n arguments.append('-r')\\n #\\n for branch in self.git.branch(*arguments).splitlines():\\n branch = branch.replace('*', '').strip()\\n if branch:\\n yield branch\\n #\\n #\",\n \"def make_branches(self, api_json=None):\\n if api_json is None:\\n return []\\n\\n obj = simplejson.loads(api_json)\\n branches = [item[\\\"commit\\\"][\\\"sha\\\"] for item in obj]\\n\\n print branches\\n\\n return branches\",\n \"def branches(self):\\n return sorted([\\n br[20:] for br in self.repo.refs.keys() if (\\n br.startswith('refs/remotes/origin/') and\\n br[20:] != 'HEAD'\\n )\\n ])\",\n \"def branches_by_name(self, username, repository_name, access_token=None):\\n return self._complete_request_by_name(\\n username, repository_name, \\\"branches\\\", access_token)\",\n \"def repos(request):\\n # Clean up garbage created by buggy edits\\n bad_branch_keys = models.Branch.query(models.Branch.owner == None).fetch(\\n 100, keys_only=True)\\n if bad_branch_keys:\\n ndb.delete_multi(bad_branch_keys)\\n repo_map = {}\\n for repo in models.Repository.query().fetch(1000, batch_size=100):\\n repo_map[repo.key] = repo\\n branches = []\\n for branch in models.Branch.query().fetch(2000, batch_size=100):\\n repo_key = branch.repo_key\\n if repo_key in repo_map:\\n branch.repository = repo_map[repo_key]\\n branches.append(branch)\\n branches.sort(key=lambda b: map(\\n unicode.lower, (b.repository.name, b.category, b.name)))\\n return respond(request, 'repos.html', {'branches': branches})\",\n \"def branch_list(repo, remote_name, pattern=None):\\n # The return string for a remote reference is a single line with two\\n # fields separated by a tab string. The first field is a commit hash.\\n # The second field is the reference path. The unique part of the path\\n # is the last field.\\n #\\n # 423f434cd877926ff47f3a710a7b0c414785515e\\trefs/heads/enterprise-3.0\\n\\n lines = repo.git.ls_remote(remote_name, pattern, heads=True).split(\\\"\\\\n\\\")\\n return [str(line.split('/')[-1]) for line in lines]\",\n \"def branches_by_id(self, repository_id, access_token=None):\\n return self._complete_request_by_id(\\n repository_id, \\\"branches\\\", access_token)\",\n \"def list_branches(self) -> PagingList[Branch]:\\n return PagingList(lambda offset, limit: self._generate_branches(None, offset, limit), 128)\",\n \"def get_branch_names(self):\\n return [\\n branch.name for branch in self.repo.branches\\n ]\",\n \"def branch(self, current_path):\\n p = subprocess.Popen(\\n [\\\"git\\\", \\\"show-ref\\\"],\\n stdout=PIPE,\\n stderr=PIPE,\\n cwd=os.path.join(self.root_dir, current_path),\\n )\\n output, error = p.communicate()\\n if p.returncode == 0:\\n results = []\\n try:\\n current_branch = self.get_current_branch(current_path)\\n for line in output.decode(\\\"utf-8\\\").splitlines():\\n # The format for git show-ref is '<SHA-1 ID> <space> <reference name>'\\n # For this method we are only interested in reference name.\\n # Reference : https://git-scm.com/docs/git-show-ref#_output\\n commit_sha = line.strip().split()[0].strip()\\n reference_name = line.strip().split()[1].strip()\\n if self._is_branch(reference_name):\\n branch_name = self._get_branch_name(reference_name)\\n is_current_branch = self._is_current_branch(\\n branch_name, current_branch\\n )\\n is_remote_branch = self._is_remote_branch(reference_name)\\n upstream_branch_name = None\\n if not is_remote_branch:\\n upstream_branch_name = self.get_upstream_branch(\\n current_path, branch_name\\n )\\n tag = self._get_tag(current_path, commit_sha)\\n results.append(\\n {\\n \\\"is_current_branch\\\": is_current_branch,\\n \\\"is_remote_branch\\\": is_remote_branch,\\n \\\"name\\\": branch_name,\\n \\\"upstream\\\": upstream_branch_name,\\n \\\"top_commit\\\": commit_sha,\\n \\\"tag\\\": tag,\\n }\\n )\\n\\n # Remote branch is seleted use 'git branch -a' as fallback machanism\\n # to get add detached head on remote branch to preserve older functionality\\n # TODO : Revisit this to checkout new local branch with same name as remote\\n # when the remote branch is seleted, VS Code git does the same thing.\\n if current_branch == \\\"HEAD\\\":\\n results.append(\\n {\\n \\\"is_current_branch\\\": True,\\n \\\"is_remote_branch\\\": False,\\n \\\"name\\\": self._get_detached_head_name(current_path),\\n \\\"upstream\\\": None,\\n \\\"top_commit\\\": None,\\n \\\"tag\\\": None,\\n }\\n )\\n return {\\\"code\\\": p.returncode, \\\"branches\\\": results}\\n except Exception as downstream_error:\\n return {\\n \\\"code\\\": p.returncode,\\n \\\"command\\\": \\\"git show-ref\\\",\\n \\\"message\\\": str(downstream_error),\\n }\\n else:\\n return {\\n \\\"code\\\": p.returncode,\\n \\\"command\\\": \\\"git show-ref\\\",\\n \\\"message\\\": error.decode(\\\"utf-8\\\"),\\n }\",\n \"def get_branches(self, *, refs=[\\\"refs/heads\\\", \\\"refs/remotes\\\"]):\\n # type: (Sequence[str]) -> List[Branch]\\n stdout = self.git(\\n \\\"for-each-ref\\\",\\n (\\n \\\"--format=\\\"\\n \\\"%(HEAD)%00\\\"\\n \\\"%(refname)%00\\\"\\n \\\"%(upstream)%00\\\"\\n \\\"%(upstream:remotename)%00\\\"\\n \\\"%(upstream:track,nobracket)%00\\\"\\n \\\"%(committerdate:unix)%00\\\"\\n \\\"%(objectname)%00\\\"\\n \\\"%(contents:subject)\\\"\\n ),\\n *refs\\n ) # type: str\\n branches = [\\n branch\\n for branch in (\\n self._parse_branch_line(line)\\n for line in filter_(stdout.splitlines())\\n )\\n if branch.name != \\\"HEAD\\\"\\n ]\\n store.update_state(self.repo_path, {\\\"branches\\\": branches})\\n return branches\",\n \"def get_branches( self ):\\n\\n branches = [ self ]\\n\\n for i in range( len( self.children ) ):\\n branches.extend( self.children[i].get_branches() )\\n\\n return branches\",\n \"def _get_pdc_branches(session, repo):\\n branches_url = '{0}component-branches/'.format(PDCURL)\\n params = dict(\\n global_component=repo['name'],\\n type=PDC_TYPES[repo['namespace']]\\n )\\n if VERBOSE:\\n print('Querying {0} {1}'.format(branches_url, params))\\n rv = session.get(branches_url, params=params, timeout=60)\\n\\n # If the project's branches can't be reported, just return no branches and\\n # it will be skipped later on\\n if not rv.ok:\\n print(('The connection to \\\"{0}\\\" failed with the status code {1} and '\\n 'output \\\"{2}\\\"'.format(branches_url, rv.status_code, rv.text)),\\n file=sys.stderr)\\n return []\\n\\n data = rv.json()\\n return [branch['name'] for branch in data['results']]\",\n \"def list_(ctx: click.Context, repository_path):\\n root_commands.cmd_list(ctx.obj, repository_path)\",\n \"def _get_branches(self):\\n logging.info('--- Get Branches ---')\\n self.local_branches = set(self.find_branches())\\n self.remote_branches = set(self.find_branches(remote=True))\\n # Tags are remote branches that start with \\\"tags/\\\".\\n self.tags = {\\n single_branch for single_branch in self.remote_branches\\n if PRX_SVNTAGS_PREFIX.match(single_branch)}\",\n \"def __gitShowBranch(self):\\n self.vcs.gitShowBranch(self.project.getProjectPath())\",\n \"def getBranch(self, repo=None):\\n repos_list_url = \\\"/\\\".join([self.url, \\\"repos\\\", repo, \\\"branches\\\"])\\n print repos_list_url\\n request = urllib2.Request(repos_list_url, headers=self.headers)\\n response = urllib2.urlopen(request)\\n\\n return response.read()\",\n \"def __gitNotMergedBranchList(self):\\n self.vcs.gitListTagBranch(self.project.getProjectPath(), False,\\n listAll=False, merged=False)\",\n \"def find_branches(self, commit, repo):\\n ref_dict = repo.repo.refs.as_dict()\\n branches = []\\n for branch, branch_id in [(b, ref_dict[b]) for b in repo.branches]:\\n obj = repo.repo[branch_id]\\n if commit.id == obj.id:\\n branches.append((branch, obj))\\n return branches\",\n \"def checkout_branches(args):\\n\\n ensure_tracking_branches([])\\n if check_dirty([]) and '-f' not in args:\\n raise Exception(\\\"Cannot checkout new branches with dirty projects.\\\")\\n \\n man = load_manifest()\\n for (name, project) in man.projects.iteritems():\\n print >>sys.stderr, \\\"Checking out tracking branch in project: %s\\\" % name\\n repo = GitRepo(workdir_for_project(project))\\n # Check that sucker out\\n repo.check_command([\\\"checkout\\\", project.tracking_branch])\",\n \"def repo_list(self):\\n\\n data, _ = self.helm_client.repo_list()\\n return data\",\n \"def list_all(self) -> List[RemoteInfo]:\\n self.__verify_repo_initialized()\\n res = []\\n names = heads.get_remote_names(self._env.branchenv)\\n for name in names:\\n address = heads.get_remote_address(self._env.branchenv, name)\\n res.append(RemoteInfo(name=name, address=address))\\n return res\",\n \"def search_project_branches(self, project):\\n params = {\\n 'project': project\\n }\\n resp = self.sonarqube.make_call('get', API_PROJECT_BRANCHES_LIST_ENDPOINT, **params)\\n response = resp.json()\\n return response['branches']\",\n \"def __gitTagList(self):\\n self.vcs.gitListTagBranch(self.project.getProjectPath(), True)\",\n \"def git_ls_tree(branch: str = 'main'):\\n branch = quote(branch)\\n return f\\\"git ls-tree -r {branch} --name-only\\\"\",\n \"def branches(self, active=False, closed=False):\\n \\n out = self._client.execute('branches', a=active, c=closed,\\n debug=True)\\n branches = {}\\n\\n for line in out.strip().splitlines():\\n namerev, node = line.rsplit(':', 1)\\n name, rev = namerev.rsplit(' ', 1)\\n name = name.strip()\\n node = node.split()[0] # To get rid of ' (inactive)'\\n branches[name] = self._get_lazy(int(rev), node)\\n\\n return branches\",\n \"def ticket_branches(self, msrp, cred_hash):\\n branches = []\\n repos = self.get_repos()\\n if not repos['status']:\\n return repos\\n\\n for repo in repos['data']:\\n response = self.find_branch(repo_name=repo['name'], msrp=msrp, cred_hash=cred_hash)\\n if response['status']:\\n branches.append({'repo': repo['name'], 'branches': response['data'], 'all': response['all']})\\n\\n if len(branches) > 0:\\n return {'status': True, 'data': branches}\\n else:\\n return {'status': False, 'data': f'No branches found with MSRP {msrp}'}\",\n \"def get_branches(self):\\n branches = []\\n for bracket in self.__brackets:\\n branches.append(bracket.get_branch())\\n return branches\",\n \"def git_branch():\\n result, output = popen('git branch', False, False)\\n branch = None\\n for line in output:\\n if line.startswith('*'):\\n branch = line.split('*')[-1].strip()\\n break\\n return branch\",\n \"def do_repo_list(self):\\n return StringResult(self._repo_list.format_available_repos())\",\n \"def list_all_repos_info():\\n repos = ALL_REPOS\\n for repo_name, repo in zip(repos, _repos(repos)):\\n repo_name = shorten_path(repo_name)\\n print(repo_name)\\n try:\\n nbr_ahead, nbr_behind = _nbr_commits_ahead_and_behind(repo)\\n except git.exc.GitCommandError:\\n print(f\\\" {repo.active_branch.name}\\\")\\n except DetachedHeadError:\\n print(f\\\" HEAD --> {repo.head.commit}\\\")\\n else:\\n nb_tabul = 3 if len(repo.active_branch.name) < 6 else 2\\n tabuls = \\\"\\\\t\\\" * nb_tabul\\n print(f\\\" {repo.active_branch.name}{tabuls}↓ {nbr_behind} ↑ {nbr_ahead}\\\")\\n if repo.index.diff(None):\\n print(\\\" !!! With unstaged changes !!!\\\")\\n if repo.index.diff(\\\"HEAD\\\"):\\n print(\\\" !!! With uncommited changes !!!\\\")\",\n \"def branches(tree):\\n\\n return tree[1:]\",\n \"def branches(tree):\\n return tree[1:]\",\n \"def dirty_branches(self):\\n # If no course index has been set, then no branches have changed\\n if self.index is None:\\n return []\\n\\n # If there was no index in the database to start with, then all branches\\n # are dirty by definition\\n if self.initial_index is None:\\n return list(self.index.get('versions', {}).keys())\\n\\n # Return branches whose ids differ between self.index and self.initial_index\\n return [\\n branch\\n for branch, _id\\n in self.index.get('versions', {}).items()\\n if self.initial_index.get('versions', {}).get(branch) != _id\\n ]\",\n \"def listRepositories(self):\\n return self.mini_catalog.listRepositories()\",\n \"def get_raven_branches(plugin_type):\\n url = f'https://github.com/autognc/ravenML-{plugin_type}-plugins'\\n remote_refs = []\\n g = git.cmd.Git()\\n for ref in g.ls_remote(url).split('\\\\n'):\\n ref = ref.split('\\\\t')[1]\\n if ref.startswith(\\\"refs/heads/\\\"):\\n remote_refs.append(ref[11:])\\n return remote_refs\",\n \"def get_commits(self):\\n\\n repo_commits = {}\\n\\n # gets all branches in repository\\n branches = self.get_branches()\\n if branches is None:\\n return None\\n\\n # get list of commits pages from all branches in repository\\n for branch in branches:\\n list_of_branch_commits = self.get_commits_by_branch(branch['name'])\\n if list_of_branch_commits is None:\\n return None\\n\\n # adds key 'branches' with branch name in list to every commit in branch,\\n # or if key 'branches' is existing add branch name to existing branches list\\n for commit_in_branch in list_of_branch_commits:\\n commit = repo_commits.get(commit_in_branch['hash'])\\n if commit:\\n commit['branches'].append(branch['name'])\\n else:\\n commit_in_branch['branches'] = [branch['name']]\\n repo_commits[commit_in_branch['hash']] = commit_in_branch\\n list_of_branch_commits.clear()\\n\\n # sorts all commits in repository by date in reverse order\\n sorted_commits = sorted(list(repo_commits.values()), key=lambda x: x['date'], reverse=True)\\n\\n # forms a list of commits as an 'get commits API' response\\n commits_amount = 30 if len(sorted_commits) >= 30 else len(sorted_commits)\\n result_list = sorted_commits[:commits_amount]\\n\\n return result_list\",\n \"def fetchall(self):\\n try:\\n self.repo.remotes.origin.fetch()\\n except git.exc.GitCommandError as err:\\n raise GitError(err)\",\n \"def get_details(self, repo=None):\\n api_json = []\\n\\n #get all branches from this repo\\n branches = self.make_branches(self.getBranch(repo))\\n\\n today = datetime.date.today()\\n yesterday = today - datetime.timedelta(2)\\n\\n for branch in branches:\\n args = {\\\"per_page\\\": \\\"100\\\",\\n \\\"sha\\\": branch,\\n \\\"author\\\": self.username,\\n \\\"since\\\": yesterday.isoformat()}\\n args = self.make_args(args)\\n repo_url = \\\"/\\\".join([self.url, \\\"repos\\\", repo, \\\"commits\\\"])\\n repo_url = repo_url + args\\n\\n request = urllib2.Request(repo_url, headers=self.headers)\\n response = urllib2.urlopen(request)\\n raw_data = response.read()\\n commits_info = self.process_factory(simplejson.loads(raw_data))\\n api_json = api_json + commits_info\\n\\n print repo_url\\n\\n print api_json\\n return api_json\",\n \"def ls(tfr: TFR, pattern, details):\\n tfr.hub.fetch_remotes()\\n for repo in tfr.hub.ls(pattern):\\n echo(repo.name)\\n if details:\\n echo(repo.html_url)\\n echo(repo.ssh_url)\",\n \"def buildList(self,list_all=False,push_all=False):\\n print \\\"=== Loading\\\"\\n\\n ## Walk in repo and get GitRepo\\n for repo in self.repos:\\n if repo not in self.ignore or list_all:\\n a = GitRepo(repo)\\n if not self.animate:\\n a.branchStatus()\\n if a.globalStatus():\\n if a.forward and push_all:\\n a.push()\\n self.gitrepos.append(a)\\n\\n print \\\"\\\\r=== \\\"+str(len(self.gitrepos))+\\\" repos scanned\\\"\",\n \"def list_repos_cli(api_client, path_prefix, next_page_token):\\n content = ReposApi(api_client).list(path_prefix, next_page_token)\\n click.echo(pretty_format(content))\",\n \"def branches(self):\\n return self.in_lines + self.out_lines\",\n \"def checkoutbranch(accountable, options):\\n issue = accountable.checkout_branch(options)\\n headers = sorted(['id', 'key', 'self'])\\n rows = [headers, [itemgetter(header)(issue) for header in headers]]\\n print_table(SingleTable(rows))\",\n \"def branch(self, *arguments, **kwargs):\\n return self.get_output('branch', *arguments, **kwargs)\",\n \"def branch_names(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"branch_names\\\")\",\n \"def get_all_commits(self):\\n\\n repo_commits = {}\\n metadata = {}\\n\\n # gets all branches in repository\\n branches = self.get_branches()\\n if branches is None:\\n return None\\n\\n # get list of commits pages from all branches in repository\\n for branch in branches:\\n list_of_branch_commits = self.get_all_commits_by_branch(branch['name'])\\n\\n if list_of_branch_commits is None:\\n return None\\n\\n # adds key 'branches' with branch name in list to every commit in branch,\\n # or if key 'branches' is existing add branch name to existing branches list\\n for commit_in_branch in list_of_branch_commits:\\n commit = repo_commits.get(commit_in_branch['hash'])\\n if commit:\\n commit['branches'].append(branch['name'])\\n else:\\n commit_in_branch['branches'] = [branch['name']]\\n repo_commits[commit_in_branch['hash']] = commit_in_branch\\n\\n # add metadata to method response for further updates by get_updated_all_commits\\n metadata[branch['name']] = list_of_branch_commits[0]\\n\\n list_of_branch_commits.clear()\\n\\n # sorts all commits in repository by date in reverse order\\n sorted_commits = sorted(list(repo_commits.values()), key=lambda x: x['date'], reverse=True)\\n\\n return {'data': sorted_commits, 'metadata': metadata}\",\n \"def track_branches(branches=None, directory=None):\\n if type(branches) == str:\\n branches = [branches]\\n debug(\\\"track_branches(\\\" + str(branches) + \\\", \\\" + str(directory) + \\\")\\\")\\n if branches == []:\\n return\\n # Save the current branch\\n current_branch = get_current_branch(directory)\\n try:\\n # Get the local branches\\n local_branches = get_branches(local_only=True, directory=directory)\\n # Get the remote and local branches\\n all_branches = get_branches(local_only=False, directory=directory)\\n # Calculate the untracked branches\\n untracked_branches = []\\n for branch in all_branches:\\n if branch.startswith('remotes/'):\\n if branch.count('/') >= 2:\\n branch = '/'.join(branch.split('/')[2:])\\n if branch not in local_branches:\\n untracked_branches.append(branch)\\n # Prune any untracked branches by specified branches\\n if branches is not None:\\n branches_to_track = []\\n for untracked in untracked_branches:\\n if untracked in branches:\\n branches_to_track.append(untracked)\\n else:\\n branches_to_track = untracked_branches\\n # Track branches\\n debug(\\\"Tracking branches: \\\" + str(branches_to_track))\\n for branch in branches_to_track:\\n checkout(branch, directory=directory)\\n finally:\\n if current_branch:\\n checkout(current_branch, directory=directory)\",\n \"def do_list(client, args):\\n\\trepos = client.repos.list(args.user)\\n\\tprint '%s has the following repositories:' % args.user\\n\\tprint 'Name - Description'\\n\\tfor repo in repos:\\n\\t\\tprint '%s - %s' % (repo.name, repo.description)\",\n \"def __gitSubmodulesList(self):\\n self.vcs.gitSubmoduleList(self.project.getProjectPath())\",\n \"def repo_init(_request):\\n python = models.Repository.query(models.Repository.name == 'Python').get()\\n if python is None:\\n python = models.Repository(name='Python', url=SVN_ROOT)\\n python.put()\\n pybranches = []\\n else:\\n pybranches = list(models.Branch.query(models.Branch.repo_key == python.key))\\n for category, name, url in BRANCHES:\\n url = python.url + url\\n for br in pybranches:\\n if (br.category, br.name, br.url) == (category, name, url):\\n break\\n else:\\n br = models.Branch(repo_key=python.key, repo_name='Python',\\n category=category, name=name, url=url)\\n br.put()\\n return HttpResponseRedirect(reverse(repos))\",\n \"def _get_branches_to_merge(branch):\\n branches = [(branch, branch.subfolder or '')]\\n for dependency in branch.branch_dependency_ids:\\n branches.append((dependency.merge_with_branch_id, dependency.merge_subfolder or ''))\\n return branches[::-1]\",\n \"def branchStatus(self):\\n ## Set stashed\\n if self.repo.git.stash('list') != \\\"\\\":\\n self.stashed = True\\n else:\\n self.stashed = False\\n\\n ## Set forward\\n nb_status_ok = 0\\n branch_status = {}\\n branch_forward = {}\\n verbose = []\\n active_branch = self.repo.active_branch\\n for branch in self.repo.branches:\\n try:\\n ## Checkout the branch (make it active)\\n self.repo.git.checkout(str(branch))\\n\\n ## Extract the number of forward commit on the local branch\\n regex = re.compile(r'Your branch .+ by (\\\\d+) commits?\\\\.',re.M)\\n status = self.repo.git.status().split(\\\"\\\\n\\\")[1]\\n if regex.search(status):\\n branch_forward[str(branch)] = int(regex.sub(r'\\\\1',status))\\n\\n ## Get the status of the branch\\n branch_status[str(branch)] = self.repo.git.status('--porcelain')\\n verbose.append(self.repo.git.status())\\n except GitCommandError:\\n branch_status[str(branch)] = 'Error while retrieving branch infos'\\n\\n ## Go back to original branch\\n self.repo.git.checkout(str(active_branch))\\n\\n ## Set self.forward\\n self.status = branch_status\\n self.forward = branch_forward\\n self.verbose = verbose\",\n \"def getSequenceBranches(self, show, sequence):\\n branchesXML = XMLUtils.getXMLElementForClass(\\\"SequenceBranches\\\")\\n mode = Mode(show, sequence)\\n editorialFolder = mode.get(\\\"[editorialFolder]\\\")\\n # Default Branch\\n branchXML = ET.Element(\\\"Branch\\\")\\n branchXML.attrib[\\\"name\\\"] = mode.get(\\\"[defaultBranch]\\\")\\n branchesXML.append(branchXML)\\n\\n if self.fileService.exists(editorialFolder):\\n # Other branches\\n matches = mode.getMatches(\\\"[editorialFLEBranchFolder]\\\", {\\\"branch\\\":\\\"*\\\"})\\n matches.sort(key=lambda match:match['branch'].lower())\\n if len(matches) > 0:\\n for i in range(0, len(matches)):\\n branchXML = ET.Element(\\\"Branch\\\")\\n branchXML.attrib[\\\"name\\\"] = str(matches[i]['branch'])\\n branchesXML.append(branchXML)\\n\\n output = ET.tostring(branchesXML)\\n\\n# print \\\"ServerFlixFunctions - getSequenceBranches output:\\\", output\\n\\n return output\",\n \"def print(self):\\n print(\\\"Repository list: \\\")\\n for repo in self.list:\\n print(\\\"- \\\" + repo.name)\",\n \"def get(self, project_slug):\\n project = Project.query.filter_by(slug=project_slug).first_or_404()\\n\\n if not (project.public or current_user.is_authenticated()):\\n flask_restful.abort(404)\\n\\n return [\\n dict(name=job.git_branch)\\n for job\\n in (\\n project.jobs.distinct(Job.git_branch)\\n .order_by(sqlalchemy.asc(Job.git_branch))\\n )\\n if job.git_branch is not None\\n ]\",\n \"def branches_containing_commit(self, commit_hash, local_only=True, remote_only=False):\\n branches = self.git(\\n \\\"branch\\\",\\n \\\"-a\\\" if not local_only and not remote_only else None,\\n \\\"-r\\\" if remote_only else None,\\n \\\"--contains\\\",\\n commit_hash\\n ).strip().split(\\\"\\\\n\\\")\\n return [branch.strip() for branch in branches]\",\n \"def collectAllBranches(self):\\n\\t\\tallBranches = []\\n\\n\\t\\tfor unit in self.inputLayer:\\n\\n\\t\\t\\tfor branch in unit.branchesOut:\\n\\n\\t\\t\\t\\tallBranches.append(branch)\\n\\n\\t\\tfor layer in self.hiddenLayers:\\n\\n\\t\\t\\tfor unit in layer:\\n\\t\\t\\t\\n\\t\\t\\t\\tfor branch in unit.branchesOut:\\n\\n\\t\\t\\t\\t\\tallBranches.append(branch)\\n\\n\\t\\tfor unit in self.outputLayer:\\n\\n\\t\\t\\tfor branch in unit.branchesOut:\\n\\n\\t\\t\\t\\tallBranches.append(branch)\\n\\n\\n\\t\\treturn allBranches\",\n \"def get_all(self, _filter=None):\\n\\t\\tres = self.run(self._get_all_query(_filter))\\n\\t\\treturn [BigQueryGithubEntry(_id, code, repo_name, path) for _id, code, repo_name, path in res]\",\n \"def get_branch(self):\\n if self._repository:\\n return self._repository.dirstate.branch()\",\n \"def list_repositories(self):\\n repos = self.repo_conn.list_repositories()\\n return repos\",\n \"def stale_pr_branches(config, args):\\n repo = config.repo\\n for pr in repo.pull_requests(state=\\\"closed\\\"):\\n if pr.head.repo == pr.base.repo and repo.branch(pr.head.ref):\\n yield {\\n \\\"html_url\\\": pr.html_url,\\n \\\"base_branch\\\": pr.base.ref,\\n \\\"head_branch\\\": pr.head.ref,\\n }\",\n \"def get_git_branch(pkg):\\n git_folder = os.path.join(GIT_FOLDER, '%s.git' % pkg)\\n if not os.path.exists(git_folder):\\n print 'Could not find %s' % git_folder\\n return set()\\n\\n head_folder = os.path.join(git_folder, 'refs', 'heads')\\n return set(os.listdir(head_folder))\",\n \"def list_repos(self):\\n return sorted(self.user_con.list_repos())\",\n \"def git_branch():\\n git_branch = None\\n branches = git(['branch']).split('\\\\n')\\n pattern = re.compile(r'^\\\\*[ ]+(?P<branch>.*)$')\\n for branch in branches:\\n matches = pattern.match(branch)\\n if matches:\\n git_branch = matches.group('branch')\\n return git_branch, '.', '.'\",\n \"async def get_tree(\\n self, ref: str or None = None\\n ) -> [\\\"AIOGitHubAPIRepositoryTreeContent\\\"] or list:\\n if ref is None:\\n raise AIOGitHubAPIException(\\\"Missing ref\\\")\\n _endpoint = f\\\"/repos/{self.full_name}/git/trees/{ref}\\\"\\n _params = {\\\"recursive\\\": \\\"1\\\"}\\n\\n response = await self.client.get(endpoint=_endpoint, params=_params)\\n\\n return [\\n AIOGitHubAPIRepositoryTreeContent(x, self.full_name, ref)\\n for x in response.get(\\\"tree\\\", [])\\n ]\",\n \"def checkout_all(self, top_repo_path):\\n my_output = subprocess.check_output(\\n [\\\"git\\\", \\\"checkout\\\", \\\"--\\\", \\\".\\\"], cwd=top_repo_path\\n )\\n return my_output\",\n \"def gh_pages_branch(repo):\\n repo.git.branch('gh-pages')\\n repo.git.checkout('gh-pages')\",\n \"def current_branch():\\n return subprocess.check_output('git branch --show-current'.split()).decode().strip()\",\n \"def get_commits(self):\\n\\n repo_commits = {}\\n\\n # gets all branches in repository\\n branches = self.get_branches()\\n if branches is None:\\n raise BitbucketRequestSenderExc('Can\\\\'t get branches for get_commits method')\\n\\n # get list of commits pages from all branches in repository\\n for branch in branches:\\n list_of_branch_commits = self.get_commits_by_branch(branch['name'])\\n if list_of_branch_commits is None:\\n raise BitbucketRequestSenderExc(\\n 'Can\\\\'t get commits by branch for get_commits method')\\n\\n # adds key 'branches' with branch name in list to every commit in branch,\\n # or if key 'branches' is existing add branch name to existing branches list\\n for commit_in_branch in list_of_branch_commits:\\n commit = repo_commits.get(commit_in_branch['hash'])\\n if commit:\\n commit['branches'].append(branch['name'])\\n else:\\n commit_in_branch['branches'] = [branch['name']]\\n repo_commits[commit_in_branch['hash']] = commit_in_branch\\n list_of_branch_commits.clear()\\n\\n # sorts all commits in repository by date in reverse order\\n sorted_commits = sorted(list(repo_commits.values()), key=lambda x: x['date'], reverse=True)\\n\\n # forms a list of commits as an 'get commits API' response\\n commits_amount = 30 if len(sorted_commits) >= 30 else len(sorted_commits)\\n result_list = sorted_commits[:commits_amount]\\n\\n return result_list\",\n \"def list_brands(self, **kwargs):\\n url = self.api_url('brands')\\n\\n return requests.get(\\n url,\\n headers=self.auth_header,\\n params=kwargs,\\n ).json()\",\n \"def get_branch():\\n command = [\\\"git\\\", \\\"branch\\\", \\\"--show-current\\\"]\\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\\n branch_str = proc.stdout.readline()\\n return branch_str.decode(\\\"utf-8\\\").rstrip()\",\n \"def list(self, request, *args, **kwargs):\\n queryset = BankConnections.objects.filter(user=self.request.user)\\n \\n response = [{\\n \\\"connected\\\": True if connection.isTokenValid else False,\\n \\\"bank\\\": connection.bank_branch.bank.id\\n } for connection in queryset]\\n\\n return Response(response)\",\n \"def test_branch_name_get(repository: Repository) -> None:\\n branch = repository.branch(repository.head.name)\\n assert repository.head.name == branch.name\",\n \"def fetch_all_repos_info():\\n\\n def fetch(*args, **kwargs):\\n kwargs[\\\"remote\\\"].fetch()\\n\\n repos = ALL_REPOS\\n for repo_name, repo in zip(repos, _repos(repos)):\\n repo_name = shorten_path(repo_name)\\n print(f\\\"fetching {repo_name}\\\")\\n _try_for_all_remotes(\\n repo, fetch, raise_on_exception=False, stop_on_success=False, verbose=True\\n )\",\n \"def fetch_repos(self):\\n logging.info(\\\"Fetching repositories in: %s\\\" % self.name)\\n list_cmd = [\\n \\\"az\\\",\\n \\\"acr\\\",\\n \\\"repository\\\",\\n \\\"list\\\",\\n \\\"-n\\\",\\n self.name,\\n \\\"-o\\\",\\n \\\"tsv\\\",\\n ]\\n\\n result = run_cmd(list_cmd)\\n\\n if result[\\\"returncode\\\"] != 0:\\n logging.error(result[\\\"err_msg\\\"])\\n raise AzureError(result[\\\"err_msg\\\"])\\n\\n logging.info(\\\"Successfully fetched repositories from: %s\\\" % self.name)\\n repos = result[\\\"output\\\"].split(\\\"\\\\n\\\")[:-1]\\n logging.info(\\\"Total number of repositories: %d\\\" % len(repos))\\n\\n return repos\",\n \"def _branch(self):\\n printer = Printer(None)\\n ci_manager = CIManager(printer)\\n return ci_manager.get_branch()\",\n \"def fetch_repos(connection):\\n\\n try:\\n response = connection.get_json('repository')\\n except HTTPRequestError as ex:\\n raise exception_from_http_error(ex) from ex\\n\\n result = response.get('result', [])\\n return [Repository(connection, repo['rid'], data=repo) for repo in result]\",\n \"def get_branch(name: Optional[str] = None,\\n project: Optional[str] = None,\\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetBranchResult:\\n __args__ = dict()\\n __args__['name'] = name\\n __args__['project'] = project\\n opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)\\n __ret__ = pulumi.runtime.invoke('gitlab:index/getBranch:getBranch', __args__, opts=opts, typ=GetBranchResult).value\\n\\n return AwaitableGetBranchResult(\\n can_push=pulumi.get(__ret__, 'can_push'),\\n commits=pulumi.get(__ret__, 'commits'),\\n default=pulumi.get(__ret__, 'default'),\\n developer_can_merge=pulumi.get(__ret__, 'developer_can_merge'),\\n developer_can_push=pulumi.get(__ret__, 'developer_can_push'),\\n id=pulumi.get(__ret__, 'id'),\\n merged=pulumi.get(__ret__, 'merged'),\\n name=pulumi.get(__ret__, 'name'),\\n project=pulumi.get(__ret__, 'project'),\\n protected=pulumi.get(__ret__, 'protected'),\\n web_url=pulumi.get(__ret__, 'web_url'))\",\n \"def get(self, repo: Repository):\\n cache_key = self.cache_key.format(repo_id=repo.id.hex)\\n\\n result = redis.get(cache_key)\\n if result is None:\\n vcs = repo.get_vcs()\\n if not vcs:\\n return self.respond([])\\n\\n vcs.ensure()\\n result = vcs.get_known_branches()\\n redis.setex(cache_key, json.dumps(result), self.cache_expire)\\n else:\\n result = json.loads(result)\\n\\n return self.respond([{\\\"name\\\": r} for r in result])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7749532","0.7738944","0.7703959","0.7614574","0.7598028","0.7515656","0.75033426","0.7465316","0.740539","0.73894024","0.72446","0.7178419","0.7082727","0.7047357","0.70421684","0.6949491","0.6943486","0.69368696","0.6858128","0.6777088","0.6739843","0.66384125","0.663819","0.6626993","0.66266525","0.66005796","0.65586066","0.65426385","0.65386206","0.6441791","0.64360327","0.6429957","0.6295401","0.62810594","0.62293893","0.6226013","0.622078","0.62165236","0.6196131","0.61777467","0.60988706","0.609517","0.60853213","0.59112775","0.5902645","0.5816628","0.5814891","0.5760861","0.57507414","0.5725733","0.5707944","0.5703567","0.5691579","0.5641606","0.5627493","0.5609029","0.5608767","0.55929196","0.55723405","0.5562222","0.5555308","0.5552858","0.5546545","0.55441654","0.5543889","0.5542996","0.5542049","0.5525897","0.55096954","0.5496888","0.54544973","0.5443811","0.5434813","0.54228246","0.5416752","0.5410805","0.5409008","0.53932863","0.537843","0.53711176","0.53703785","0.5354493","0.5338384","0.53255147","0.5325198","0.5324823","0.5318229","0.53050625","0.5293084","0.5288614","0.5283077","0.52707577","0.52405334","0.5224529","0.52079463","0.52073085","0.5203451","0.5187832","0.516992","0.5166379"],"string":"[\n \"0.7749532\",\n \"0.7738944\",\n \"0.7703959\",\n \"0.7614574\",\n \"0.7598028\",\n \"0.7515656\",\n \"0.75033426\",\n \"0.7465316\",\n \"0.740539\",\n \"0.73894024\",\n \"0.72446\",\n \"0.7178419\",\n \"0.7082727\",\n \"0.7047357\",\n \"0.70421684\",\n \"0.6949491\",\n \"0.6943486\",\n \"0.69368696\",\n \"0.6858128\",\n \"0.6777088\",\n \"0.6739843\",\n \"0.66384125\",\n \"0.663819\",\n \"0.6626993\",\n \"0.66266525\",\n \"0.66005796\",\n \"0.65586066\",\n \"0.65426385\",\n \"0.65386206\",\n \"0.6441791\",\n \"0.64360327\",\n \"0.6429957\",\n \"0.6295401\",\n \"0.62810594\",\n \"0.62293893\",\n \"0.6226013\",\n \"0.622078\",\n \"0.62165236\",\n \"0.6196131\",\n \"0.61777467\",\n \"0.60988706\",\n \"0.609517\",\n \"0.60853213\",\n \"0.59112775\",\n \"0.5902645\",\n \"0.5816628\",\n \"0.5814891\",\n \"0.5760861\",\n \"0.57507414\",\n \"0.5725733\",\n \"0.5707944\",\n \"0.5703567\",\n \"0.5691579\",\n \"0.5641606\",\n \"0.5627493\",\n \"0.5609029\",\n \"0.5608767\",\n \"0.55929196\",\n \"0.55723405\",\n \"0.5562222\",\n \"0.5555308\",\n \"0.5552858\",\n \"0.5546545\",\n \"0.55441654\",\n \"0.5543889\",\n \"0.5542996\",\n \"0.5542049\",\n \"0.5525897\",\n \"0.55096954\",\n \"0.5496888\",\n \"0.54544973\",\n \"0.5443811\",\n \"0.5434813\",\n \"0.54228246\",\n \"0.5416752\",\n \"0.5410805\",\n \"0.5409008\",\n \"0.53932863\",\n \"0.537843\",\n \"0.53711176\",\n \"0.53703785\",\n \"0.5354493\",\n \"0.5338384\",\n \"0.53255147\",\n \"0.5325198\",\n \"0.5324823\",\n \"0.5318229\",\n \"0.53050625\",\n \"0.5293084\",\n \"0.5288614\",\n \"0.5283077\",\n \"0.52707577\",\n \"0.52405334\",\n \"0.5224529\",\n \"0.52079463\",\n \"0.52073085\",\n \"0.5203451\",\n \"0.5187832\",\n \"0.516992\",\n \"0.5166379\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7706611"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":74586,"cells":{"query":{"kind":"string","value":"Check if user had bound their github token."},"document":{"kind":"string","value":"def _github_bound(self, person):\n result = True\n try:\n result = len(self[person + \"github_token\"]) > 0\n except BaseException:\n result = False\n return result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_token(self):\n api_response = requests.get(self.api_config.get_api_url() + \"greetings/isloggedin\",\n headers={\"Authorization\": \"Bearer \" + self.API_TOKEN})\n\n if api_response.status_code == 401 or 403:\n return False\n else:\n return True","def check_credentials(self) -> None:\n # Checks the GitHub token is defined\n configuration.get_value(ConfigurationVariable.GIT_TOKEN)","def check_token(self, user, token):\n try:\n data = signing.loads(token, max_age=properties.TOKEN_LOGIN_TIMEOUT)\n except signing.BadSignature:\n return False\n\n return (\n (\n (data['last_login'] is None and user.last_login is None) or\n data['last_login'] == user.last_login.strftime('%s')\n ) and\n data['user_id'] == user.pk\n )","async def check_token_works(self) -> bool:\n async with self.web_session.get(url=self._user_endpoint, headers=self._headers) as resp:\n self._expired_token = not resp.status == 200\n return not self._expired_token","def check_token(self):\n return config.outlook_token is not None","def has_token(self):\n user_id = getattr(self, '_id', None)\n user_token = getattr(self, 'token', None)\n if user_id is not None and user_token is not None:\n return True\n return False","def logged_in(self):\n return self._token is not None","def verify_local_token(self, token):\n return token == self.master_local_token.get_token()","def check(self):\n\n us = ServiceLocator.resolve(ServiceLocator.USERS)\n\n user_session = self.get()\n user = self.get_user()\n\n return user is not None and us.verify_auth_token(user_session.token, config.SESSION_EXPIRES)","def verifyLogin():\n global HUB\n\n loginInfo = FloatingTools.userData()['Login']\n if loginInfo['username'] is None or loginInfo['password'] is None:\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\n return False\n try:\n HUB = Github(loginInfo['username'], loginInfo['password'])\n for repo in HUB.get_user().get_repos():\n break\n FloatingTools.Dashboard.setDashboardVariable('logged_in', True)\n return True\n except BadCredentialsException:\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\n return False","def verify_token(*token): # pragma: no cover\n\n if current_app.config.get('IGNORE_AUTH') is True:\n return True\n\n g.user = APITokenModel.verify_token(token[0])\n\n if g.user is None:\n return False\n\n return g.user","async def require_authentication(\n token: str = Depends(oauth2_scheme),\n) -> bool: # coverage: ignore\n\n try:\n google.oauth2.id_token.verify_firebase_token(\n token, requests.Request(), \"munro-leagues\"\n )\n except ValueError:\n raise HTTP_401(\"Invalid Authentication Credentials\")\n\n return True","def auth_check(phenny, nick, target=None):\n global auth_list\n if target == phenny.config.nick:\n return 0\n elif nick in auth_list:\n return 1","def verify_token(self, token):\n return False","def has_user(self):\n\t\treturn len( self.a_token ) > 0 and len( self.a_secret ) > 0","def is_logged_in(self):\n return self.router.token is not None","def _check_auth(self):\n if self.authToken:\n return True\n else:\n msg = \"you need to login\"\n self.raise_error(msg)","def verify_token(auth_token):\n blacklisted_token = TokenBlacklisting.query.filter_by(\n token=str(auth_token)).first()\n if blacklisted_token:\n return True\n return False","def check_token(self, user, token):\n\n # Parse the token\n try:\n ts_b36, hash = token.split(\"-\")\n except ValueError:\n return False\n\n try:\n ts = base36_to_int(ts_b36)\n except ValueError:\n return False\n\n # Check that the timestamp/uid has not been tampered with\n recomputed_token = self._make_token_with_timestamp(user, ts)\n\n log.debug(\"Ricalcolo re_token=%s token=%s\" % (recomputed_token, token))\n if not constant_time_compare(recomputed_token, token):\n return False\n\n # Check the timestamp is within limit\n if (self._num_days(self._today()) - ts) > settings.REFERRAL_TOKEN_RESET_TIMEOUT_DAYS:\n return False\n\n return True","def correct_token(name, token):\n if not User.created(name):\n return False\n user = User.get_user(name)\n return user.info['token'] == token","def get_github_auth_token():\n global _cached_github_token\n if _cached_github_token:\n near_expiry, _ = get_github_token_info(_cached_github_token)\n if not near_expiry:\n return _cached_github_token\n\n github_auth_token = os.getenv('GITHUB_AUTH_TOKEN')\n assert github_auth_token, 'GITHUB_AUTH_TOKEN needs to be set.'\n tokens = github_auth_token.split(',')\n wait_time = None\n g = None\n for i, token in enumerate(tokens):\n g = github.Github(token)\n near_expiry, wait_time = get_github_token_info(g)\n if not near_expiry:\n _cached_github_token = g\n return g\n print(f'Rate limit exceeded, sleeping till reset: {wait_time} seconds.',\n file=sys.stderr)\n time.sleep(wait_time)\n return g","def check_token(token):\n return conn.hget('login:', token)","def verify_token(token):\n try:\n idinfo = client.verify_id_token(token, app.config['GOOGLE_CLIENT_ID'])\n if idinfo['iss'] not in [\n 'accounts.google.com',\n 'https://accounts.google.com'\n ]:\n raise crypt.AppIdentityError(\"Wrong issuer.\")\n except crypt.AppIdentityError:\n return False\n return True","def checkToken( self ):\n\n if ( self.token == None ):\n return False\n else :\n d = {\n \"auth_token\" : str(self.token) ,\n \"method\" : \"flickr.auth.checkToken\",\n \"format\" : \"json\",\n \"nojsoncallback\" : \"1\"\n }\n sig = self.signCall( d )\n\n url = self.urlGen( api.rest, d, sig )\n try:\n res = self.getResponse( url )\n if ( self.isGood( res ) ):\n self.token = res['auth']['token']['_content']\n self.perms = res['auth']['perms']['_content']\n return True\n else :\n self.reportError( res )\n except:\n print(str(sys.exc_info()))\n return False","def checkLogin():\n if 'access_token' in login_session:\n return True\n else:\n return False","def check_token(token):\n token = db.session.query(Token).filter(Token.token==token).first()\n if token == None:\n return False\n #TODO token lifetime\n #if (datetime.datetime.now() - token.date >= datetime.timedelta(day=2)):\n # return False \n return True","def validate_token():\n global vault_token\n global vault_token_time\n\n if vault_token is None:\n return False\n\n return datetime.datetime.now() < vault_token_time","def authenticated(self):\n return self.token is not None","async def check_access_token(self, token):\n async with self._session.get(\n 'https://eu.battle.net/oauth/check_token',\n params={'token': token}) as resp:\n self.request_count += 1\n valid = resp.status == 200\n if valid:\n json = await resp.json()\n exp = datetime.fromtimestamp(json['exp'])\n valid = valid and exp - datetime.now() >= timedelta(hours=1)\n self._access_token_checked = valid\n return self._access_token_checked","def check_auth_token_validity(self):\n endpoint = self.url + 'api/v1/jobs'\n response = requests.get(endpoint, headers=self.authorization())\n if response.status_code != 200:\n self.print_error_response(response, \"detail\")\n return response.status_code == 200","def check_auth_token_validity(self):\n endpoint = self.url + 'api/v1/jobs'\n response = requests.get(endpoint, headers=self.authorization())\n if response.status_code != 200:\n self.print_error_response(response, \"detail\")\n return response.status_code == 200","def check_token(self, token):\n if not token or not self.verification_token:\n return False\n if not constant_time_compare(token, self.verification_token):\n return False\n if self.is_verified:\n return False\n age = timezone.now() - self.added_date\n if age >= timedelta(days=AssociatedEmail.VERIFICATION_TIMEOUT_DAYS):\n return False\n return True","def verify_token(vial_http: urllib3.connectionpool.ConnectionPool) -> bool:\n verify_resp = vial_http.request(\"GET\", \"/api/verifyToken\")\n return verify_resp.status == 200","def logged_in(self):\n return self.auth.get_user_by_session() is not None","def check_auth():","def current_user_has_access(self):\n return self.user_has_access(users.get_current_user())","def verify_token(token):\n if config.API_TOKEN is None:\n logger.error(\n 'API token is not configured, auth will fail!')\n return token == config.API_TOKEN","def verify_token(self, token):\n _now = timezone.now()\n\n if (\n (self.token is not None)\n and (token == self.token)\n and (_now < self.valid_until)\n ):\n self.token = None\n self.valid_until = _now\n self.save()\n\n return True\n else:\n return False","def __check_token(self) -> bool:\r\n\r\n now = datetime.now(self.__tz)\r\n\r\n if (self.__token_expiration_date - now).total_seconds() < 0:\r\n log.debug('Token needs update!')\r\n return self.__update_token()\r\n return False","def google_validate(self, token):\n try:\n payload = client.verify_id_token(token, GOOGLE_USER_ID)\n if payload['iss'] not in ['accounts.google.com', 'https://accounts.google.com'] or payload['aud'] != GOOGLE_USER_ID:\n return False\n else:\n return payload\n except crypt.AppIdentityError:\n return False","def is_token_required(self):\n return any([self.app_id, self._login, self._password])","def validate_owner(model, request):\n auth_token = request.headers.get('Authentication-Token')\n user = _token_loader(auth_token)\n if model.owner != user:\n abort(401)","def _verify_token(self, token, request):\n # First check if this request was already verified.\n # `request.bound_data` is an attribute provided by Kinto to store\n # some data that is shared among sub-requests (e.g. default bucket\n # or batch requests)\n if REIFY_KEY not in request.bound_data:\n user_id = None\n client_name = None\n auth_client = self._get_auth_client(request)\n\n for scope, client in request.registry._fxa_oauth_scope_routing.items():\n try:\n profile = auth_client.verify_token(token=token, scope=aslist(scope))\n user_id = profile['user']\n scope = profile['scope']\n client_name = client\n\n # Make sure the bearer token scopes don't match multiple configs.\n routing_scopes = request.registry._fxa_oauth_scope_routing\n intersecting_scopes = [x for x in routing_scopes.keys()\n if x and set(x.split()).issubset(set(scope))]\n if len(intersecting_scopes) > 1:\n logger.warn(\"Invalid FxA token: {} matches multiple config\" % scope)\n return None, None\n\n break\n except fxa_errors.OutOfProtocolError:\n logger.exception(\"Protocol error\")\n raise httpexceptions.HTTPServiceUnavailable()\n except (fxa_errors.InProtocolError, fxa_errors.TrustError) as e:\n logger.debug(\"Invalid FxA token: %s\" % e)\n\n # Save for next call.\n request.bound_data[REIFY_KEY] = (user_id, client_name)\n\n return request.bound_data[REIFY_KEY]","def is_token_revoked(decoded_token):\n jti = decoded_token['jti']\n token = BlacklistedToken.query.filter_by(jti=jti).first()\n return token is not None","def available(self):\n return self.access_token is not None","def verify_token(event):\n if event['token'] != VERIFICATION_TOKEN:\n print('Presented with invalid token - ignoring message...')\n return False\n return True","def has_user(self, user): # pylint: disable=unused-argument\r\n return False","def verify_privileged(self):\n community_text = self.fetch(self.base_url + \"/community\")\n return \"You must be logged in to see this page.\" not in community_text","def has_permission(self, request, view):\n if isinstance(request.user, TokenUser):\n return True\n\n return super().has_permission(request, view)","def has_token_in(status, token):\n if not hasattr(res, status):\n return False\n return token in getattr(res, status)","def is_revoked(self, token: str) -> bool:\n return token in self.revoked_tokens","def check_if_token_in_blacklist(decrypted_token):\n return (\n decrypted_token[\"jti\"] in BLACKLIST\n ) # if True, go to revoked_token_callback","async def __call__(\n self, http_auth: HTTPAuthorizationCredentials = Depends(HTTPBearer())\n ) -> Optional[bool]:\n is_verified = self.verify_token(http_auth)\n if not is_verified:\n return None\n\n if self.scope_name:\n is_verified_scope = self.verify_scope(http_auth)\n if not is_verified_scope:\n return None\n\n return True","def early_return(bot:Bot, ctx:Context):\n return ctx.message.author.bot or ctx.message.author.id == bot.user.id","def check_google_token():\n #get token from login page and google's token rigamorole\n gtoken = request.form.get(\"idtoken\")\n #validate token\n g_profile = apiapijoyjoy.validate_google_token(gtoken)\n #collect user info from google\n name = g_profile['given_name']\n lname = g_profile['family_name']\n email = g_profile['email']\n # start a session\n session[\"user_id\"] = email\n user = User.query.filter_by(email=email).first()\n #create flags for Flask to return to google's scripts and take frontend action accordingly\n if user:\n return \"FLASK SEES USER\"\n else:\n #create new user in SQLAlchemy using info above from Google. BUT WAIT WHAT?! CODE REVIEW PLS!\n new_user = User(email=email, name=name, lname=lname)\n db.session.add(new_user)\n db.session.commit()\n return \"FLASK SEES NO USER\"","def _is_oauth_token_valid(token: dict, time_key=\"expires_on\") -> bool:\n if \"access_token\" not in token or token.get(\"token_type\", \"\") != \"Bearer\" or time_key not in token:\n raise AirflowException(f\"Can't get necessary data from OAuth token: {token}\")\n\n return int(token[time_key]) > (int(time.time()) + TOKEN_REFRESH_LEAD_TIME)","def get_all_repos(oauth_token, repo_name):\n print('Checking if the user can access the repo...')\n g = Github(oauth_token)\n print('\\tRunning as ' + g.get_user().name)\n all_repos = g.get_user().get_repos()\n all_repos_full_name = []\n for test in list(all_repos):\n all_repos_full_name.append(test.full_name)\n if repo_name in all_repos_full_name:\n return True\n else:\n print('Repo \"' + repo_name + '\" is not in the list')\n print('Here\\'s a list of all repos I can see:')\n for each_repo in all_repos:\n print('\\t' + each_repo.full_name)\n return False","async def cog_check(self, ctx:utils.Context):\n\n if ctx.author.id in self.bot.config['owners']:\n return True\n raise commands.NotOwner","def is_authenticated(self):\n if not self.token:\n return False\n\n try:\n self.lookup_token()\n return True\n except Forbidden:\n return False\n except InvalidPath:\n return False\n except InvalidRequest:\n return False","def gatekeeper():\n\n if user.name in GATEKEEPERS:\n return True\n\n return False","def logged_in(request):\n return request.current_user is not None","def is_blacklisted(token):\n if Revoked.query.filter_by(token=token).first():\n return True\n return False","def check_key(request):\n try:\n access_key = request.session.get('access_key_tw', None)\n if not access_key:\n return False\n except KeyError:\n return False\n return True\n\n\t# User info","def get_github_token_info(g):\n rate_limit = g.get_rate_limit()\n near_expiry = rate_limit.core.remaining < 50\n wait_time = (rate_limit.core.reset - datetime.datetime.utcnow()).seconds\n return near_expiry, wait_time","def verify_password(self, google_token):\n return google_token == self.google_token","def github_complete(request):\n logger.debug(\"Received user returning from Github.\")\n # Exchange code for token.\n # This creates an OpenHumansMember and associated user account.\n code = request.GET.get('code', '')\n ohmember = request.user.openhumansmember\n github_member = github_code_to_member(code=code, ohmember=ohmember)\n\n if github_member:\n messages.info(request, \"Your Github account has been connected\")\n process_github.delay(ohmember.oh_id)\n return redirect('/dashboard')\n\n logger.debug('Invalid code exchange. User returned to starting page.')\n messages.info(request, (\"Something went wrong, please try connecting your \"\n \"Github account again\"))\n return redirect('/dashboard')","def callback_repo_check(self, request, uri, headers, status_code=404):\n self.assertEqual(\n request.headers['Authorization'],\n 'token {0}'.format(self.OAUTH2_TOKEN)\n )\n # Handle the new \"rerun\" repo differently\n if self.TEST_RERUN_REPO in uri:\n status_code = 404\n return (status_code, headers, json.dumps({'message': 'testing'}))","def is_me(self, m):\n return m.author == self.client.user","def is_user_check(fn):\n @wraps(fn)\n def wrapper(*args, **kwargs):\n verify_jwt_in_request()\n token = get_jwt_identity()\n if token['is_user_token'] is False:\n _token_usage_counter_add(token['data_source_token']['id'])\n return ErrorObject.create_response(\n ErrorObject, HTTPStatus.FORBIDDEN,\n 'Unable to access this resource with provided token')\n else:\n return fn(*args, **kwargs)\n\n return wrapper","def token_valid_check(start_time):\n #calculate the time elapsed since token was last refreshed\n elapsed_time = time.time() - start_time\n #take action if token is expired\n if elapsed_time > 3540:\n return False\n return True","def authorizes(self, user):\n return self.owner == user or self.workers.filter(pk=user.id).exists()","def logged_in(self):\n return self.user is not None","def authenticated():\n if 'user_id' in session and 'access_token' in session:\n user = db_session.query(User).filter_by(id=session['user_id']).first()\n\n if user:\n return user.access_token == session['access_token']\n return False","def user_is_same(current_user_number, past_user_numbers):\n if current_user_number == past_user_numbers:\n return True\n else:\n return False","async def bot_check(self, ctx):\n blocked = await self.db.fetchrow(\n \"\"\"\n SELECT *\n FROM blocks\n WHERE user_id=$1\n \"\"\",\n ctx.author.id,\n )\n if blocked is None:\n return True\n raise BlackListed","def validate_sha_github(sha):\n r = requests.head(github_changeset_url % sha)\n return r.status_code == 200","def _validate_token(self):\n if not self.token:\n self.login()\n if not self.token:\n # TODO: create exception for this\n # Access is denied!!\n raise Exception(\"AccessDenied\")","def check_auth_token_validity(self):\n endpoint = self.url + 'api/v1/readiness'\n response = self.perform_get_request(endpoint)\n\n if response.status_code != 200:\n self.print_error_response(response, \"error\")\n return response.status_code == 200","def github():\n try:\n GITHUB_API = \"https://api.github.com/users/{0}/events\".format(self.user.username)\n\n accepted_events = AcceptedEvent.objects.values_list(\"name\", flat=True)\n events = json.loads(requests.get(GITHUB_API).text)\n for event in events:\n # it needs to be either a commit or a pull request\n # it must also be after the last update.\n if event[\"type\"] in accepted_events \\\n and self.date < dateutil.parser.parse(event[\"created_at\"]):\n return True\n else:\n return False\n except:\n return False","def accessCheck(self) -> None:\n\n if self.access_token:\n return\n self.access_token = self.login()","def _check_token_is_revoked(self, jti: str) -> None:\n redis = self._conn_redis()\n entry = redis.get(jti)\n if entry and entry == 'true':\n raise HTTPException(status_code=401,detail=\"Token has been revoked\")","def auth_check():\n try:\n access_token = session_get('access_token')\n access_token_secret = session_get('access_token_secret')\n\n if access_token and access_token_secret:\n tk = get_twitter_keys()\n client = UserClient(\n tk.consumer_key,\n tk.consumer_secret,\n access_token=access_token,\n access_token_secret=access_token_secret)\n \"\"\"\n We need to make a call to verify_credentials in case the user\n has revoked access for this application. This is a rate-limited\n call and so this approach might not be ideal. If we end up\n having rate-limiting problems, we might try giving each user\n a unique application ID that is kept in local storage and used\n as a lookup for Twitter creds (vs. session data which is domain-\n specific and thus problematic for our extension-approach). This\n might allow us to consolidate Twitter creds per user rather than\n storing them for each domain visited.\"\"\"\n verif = client.api.account.verify_credentials.get()\n if verif.headers['status'].split()[0] == '200':\n return jsonify({'is_auth': 1})\n else:\n # possibly revoked access, although this will probably\n # get handled by the TwitterAuthError catch\n remove_session_credentials()\n return jsonify({'is_auth': 0})\n tk = get_twitter_keys()\n client = UserClient(tk.consumer_key, tk.consumer_secret)\n callback = 'http://'+request.host+url_for('auth_verify')\n token = client.get_authorize_token(callback)\n session_set('auth_token', token.oauth_token)\n session_set('auth_token_secret', token.oauth_token_secret)\n session_set('auth_redirect',\n request.args.get('redirect') or '')\n if (\n 'html' in request.headers['Accept']\n and request.args.get('_format') != 'json'):\n return redirect(token.auth_url)\n else:\n data = {'is_auth': 0, 'auth_url': token.auth_url}\n return jsonify(data)\n except TwitterAuthError:\n remove_session_credentials()\n return jsonify({'is_auth': 0})\n except Exception, e:\n traceback.print_exc()\n return jsonify({'error': str(e)})","def is_authorized():\n return CentralStorageClient.token is not None","def check_token(api_key, required_scopes):\n return {\"uid\": \"\"}","def check_for_token(token):\n try:\n decode_token(token)\n return True\n except:\n return False","def check_auth(uid, token, ts_check):\n if token is None:\n token_row = TokenAuth.objects.filter(user_id=uid).order_by(\"-created_at\")[:1]\n else:\n token_row = TokenAuth.objects.filter(user_id=uid, token=token).order_by(\"-created_at\")[:1]\n\n if not token_row:\n return False, None\n\n difference = ts_check - timezone.now()\n\n if difference.days > 90:\n return False, token_row[0].token\n return True, token_row[0].token","def authorized(token):\n url = \"https://api.github.com/user?access_token=\" + str(token)\n req = requests.get(url)\n github_username = req.json()['login']\n\n redirect_to = request.args.get('next') or url_for('index')\n if token is None:\n return redirect(redirect_to)\n\n user = db_session.query(User)\\\n .filter_by(github_username=github_username).first()\n if user is None:\n user = User(github_username=github_username)\n db_session.add(user)\n\n user.access_token = token\n db_session.commit()\n\n session['user_id'] = user.id\n session['access_token'] = user.access_token\n session['username'] = user.github_username\n\n return redirect(url_for('index'))","def is_token_revoked(decoded_token):\r\n jti = decoded_token['jti']\r\n try:\r\n token = TokenBlacklist.query.filter_by(jti=jti).one()\r\n return token.revoked\r\n except NoResultFound:\r\n return True","def requires_auth(self):\n return True","def user_in_session():\n return 'user_id' in login_session","def is_public_token(token):\n return (\n token.payload\n and token.payload.get(\"roles\") == [NONE]\n and token.payload.get(\"context_id\") is None\n and token.payload.get(\"consumer_site\") is None\n )","def check_blacklisted_token(token):\n token = models.TokenBlackList.query.filter_by(token=token).first()\n if token:\n return True\n return False","def test_verification_with_valid_token(self) -> None:\n\n secret_key = str(self.author.secret_key)\n verification_url = reverse('author:verify', kwargs={'secret_key': str(secret_key)})\n\n # Make sure URL's don't change.\n self.assertEqual(verification_url, f'/api/authors/verify/{secret_key}/')\n\n # Make valid request and get response\n response: Response = self.client.get(verification_url)\n\n self.assertEqual(response.status_code, 302)\n\n # Now test if the method \"verify\" was called\n self.assertEqual(Author.objects.get().verified, True)\n # We don't wanna give him too many privileges\n self.assertEqual(self.author.is_staff, False)","def is_token_valid(self,pk,request):\n\n pass","def test_get_github_name_positive(self):\n self.assertIsNotNone(app.get_github_name(\"dhh\")[\"user\"])","def authenticated(self, user_token, **validation_context):\n token = self.token_storage.get(user_token)\n if token and token.validate(user_token, **validation_context):\n return True\n\n return False","def test_correct_token_working(self, client, token, update_repo_mock):\n assert_hook_status(client, status=204, token=token)\n assert update_repo_mock.called","def is_authenticated(self):\n return bool(get_auth_token())","def check_user():\n token = request.headers['Authorization'].replace('Bearer ', '')\n return jsonify({\"access_token\": token}), 200","async def check_discord_user(ctx: Context):\n user = PostgreSQL.get_discord_user(ctx.message.author.id)\n if user is None:\n await ctx.send(f'You have not been verified yet. Please visit {WEBSITE} to get verified (Aber VPN is required off campus)')\n return\n return user"],"string":"[\n \"def test_token(self):\\n api_response = requests.get(self.api_config.get_api_url() + \\\"greetings/isloggedin\\\",\\n headers={\\\"Authorization\\\": \\\"Bearer \\\" + self.API_TOKEN})\\n\\n if api_response.status_code == 401 or 403:\\n return False\\n else:\\n return True\",\n \"def check_credentials(self) -> None:\\n # Checks the GitHub token is defined\\n configuration.get_value(ConfigurationVariable.GIT_TOKEN)\",\n \"def check_token(self, user, token):\\n try:\\n data = signing.loads(token, max_age=properties.TOKEN_LOGIN_TIMEOUT)\\n except signing.BadSignature:\\n return False\\n\\n return (\\n (\\n (data['last_login'] is None and user.last_login is None) or\\n data['last_login'] == user.last_login.strftime('%s')\\n ) and\\n data['user_id'] == user.pk\\n )\",\n \"async def check_token_works(self) -> bool:\\n async with self.web_session.get(url=self._user_endpoint, headers=self._headers) as resp:\\n self._expired_token = not resp.status == 200\\n return not self._expired_token\",\n \"def check_token(self):\\n return config.outlook_token is not None\",\n \"def has_token(self):\\n user_id = getattr(self, '_id', None)\\n user_token = getattr(self, 'token', None)\\n if user_id is not None and user_token is not None:\\n return True\\n return False\",\n \"def logged_in(self):\\n return self._token is not None\",\n \"def verify_local_token(self, token):\\n return token == self.master_local_token.get_token()\",\n \"def check(self):\\n\\n us = ServiceLocator.resolve(ServiceLocator.USERS)\\n\\n user_session = self.get()\\n user = self.get_user()\\n\\n return user is not None and us.verify_auth_token(user_session.token, config.SESSION_EXPIRES)\",\n \"def verifyLogin():\\n global HUB\\n\\n loginInfo = FloatingTools.userData()['Login']\\n if loginInfo['username'] is None or loginInfo['password'] is None:\\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\\n return False\\n try:\\n HUB = Github(loginInfo['username'], loginInfo['password'])\\n for repo in HUB.get_user().get_repos():\\n break\\n FloatingTools.Dashboard.setDashboardVariable('logged_in', True)\\n return True\\n except BadCredentialsException:\\n FloatingTools.Dashboard.setDashboardVariable('logged_in', False)\\n return False\",\n \"def verify_token(*token): # pragma: no cover\\n\\n if current_app.config.get('IGNORE_AUTH') is True:\\n return True\\n\\n g.user = APITokenModel.verify_token(token[0])\\n\\n if g.user is None:\\n return False\\n\\n return g.user\",\n \"async def require_authentication(\\n token: str = Depends(oauth2_scheme),\\n) -> bool: # coverage: ignore\\n\\n try:\\n google.oauth2.id_token.verify_firebase_token(\\n token, requests.Request(), \\\"munro-leagues\\\"\\n )\\n except ValueError:\\n raise HTTP_401(\\\"Invalid Authentication Credentials\\\")\\n\\n return True\",\n \"def auth_check(phenny, nick, target=None):\\n global auth_list\\n if target == phenny.config.nick:\\n return 0\\n elif nick in auth_list:\\n return 1\",\n \"def verify_token(self, token):\\n return False\",\n \"def has_user(self):\\n\\t\\treturn len( self.a_token ) > 0 and len( self.a_secret ) > 0\",\n \"def is_logged_in(self):\\n return self.router.token is not None\",\n \"def _check_auth(self):\\n if self.authToken:\\n return True\\n else:\\n msg = \\\"you need to login\\\"\\n self.raise_error(msg)\",\n \"def verify_token(auth_token):\\n blacklisted_token = TokenBlacklisting.query.filter_by(\\n token=str(auth_token)).first()\\n if blacklisted_token:\\n return True\\n return False\",\n \"def check_token(self, user, token):\\n\\n # Parse the token\\n try:\\n ts_b36, hash = token.split(\\\"-\\\")\\n except ValueError:\\n return False\\n\\n try:\\n ts = base36_to_int(ts_b36)\\n except ValueError:\\n return False\\n\\n # Check that the timestamp/uid has not been tampered with\\n recomputed_token = self._make_token_with_timestamp(user, ts)\\n\\n log.debug(\\\"Ricalcolo re_token=%s token=%s\\\" % (recomputed_token, token))\\n if not constant_time_compare(recomputed_token, token):\\n return False\\n\\n # Check the timestamp is within limit\\n if (self._num_days(self._today()) - ts) > settings.REFERRAL_TOKEN_RESET_TIMEOUT_DAYS:\\n return False\\n\\n return True\",\n \"def correct_token(name, token):\\n if not User.created(name):\\n return False\\n user = User.get_user(name)\\n return user.info['token'] == token\",\n \"def get_github_auth_token():\\n global _cached_github_token\\n if _cached_github_token:\\n near_expiry, _ = get_github_token_info(_cached_github_token)\\n if not near_expiry:\\n return _cached_github_token\\n\\n github_auth_token = os.getenv('GITHUB_AUTH_TOKEN')\\n assert github_auth_token, 'GITHUB_AUTH_TOKEN needs to be set.'\\n tokens = github_auth_token.split(',')\\n wait_time = None\\n g = None\\n for i, token in enumerate(tokens):\\n g = github.Github(token)\\n near_expiry, wait_time = get_github_token_info(g)\\n if not near_expiry:\\n _cached_github_token = g\\n return g\\n print(f'Rate limit exceeded, sleeping till reset: {wait_time} seconds.',\\n file=sys.stderr)\\n time.sleep(wait_time)\\n return g\",\n \"def check_token(token):\\n return conn.hget('login:', token)\",\n \"def verify_token(token):\\n try:\\n idinfo = client.verify_id_token(token, app.config['GOOGLE_CLIENT_ID'])\\n if idinfo['iss'] not in [\\n 'accounts.google.com',\\n 'https://accounts.google.com'\\n ]:\\n raise crypt.AppIdentityError(\\\"Wrong issuer.\\\")\\n except crypt.AppIdentityError:\\n return False\\n return True\",\n \"def checkToken( self ):\\n\\n if ( self.token == None ):\\n return False\\n else :\\n d = {\\n \\\"auth_token\\\" : str(self.token) ,\\n \\\"method\\\" : \\\"flickr.auth.checkToken\\\",\\n \\\"format\\\" : \\\"json\\\",\\n \\\"nojsoncallback\\\" : \\\"1\\\"\\n }\\n sig = self.signCall( d )\\n\\n url = self.urlGen( api.rest, d, sig )\\n try:\\n res = self.getResponse( url )\\n if ( self.isGood( res ) ):\\n self.token = res['auth']['token']['_content']\\n self.perms = res['auth']['perms']['_content']\\n return True\\n else :\\n self.reportError( res )\\n except:\\n print(str(sys.exc_info()))\\n return False\",\n \"def checkLogin():\\n if 'access_token' in login_session:\\n return True\\n else:\\n return False\",\n \"def check_token(token):\\n token = db.session.query(Token).filter(Token.token==token).first()\\n if token == None:\\n return False\\n #TODO token lifetime\\n #if (datetime.datetime.now() - token.date >= datetime.timedelta(day=2)):\\n # return False \\n return True\",\n \"def validate_token():\\n global vault_token\\n global vault_token_time\\n\\n if vault_token is None:\\n return False\\n\\n return datetime.datetime.now() < vault_token_time\",\n \"def authenticated(self):\\n return self.token is not None\",\n \"async def check_access_token(self, token):\\n async with self._session.get(\\n 'https://eu.battle.net/oauth/check_token',\\n params={'token': token}) as resp:\\n self.request_count += 1\\n valid = resp.status == 200\\n if valid:\\n json = await resp.json()\\n exp = datetime.fromtimestamp(json['exp'])\\n valid = valid and exp - datetime.now() >= timedelta(hours=1)\\n self._access_token_checked = valid\\n return self._access_token_checked\",\n \"def check_auth_token_validity(self):\\n endpoint = self.url + 'api/v1/jobs'\\n response = requests.get(endpoint, headers=self.authorization())\\n if response.status_code != 200:\\n self.print_error_response(response, \\\"detail\\\")\\n return response.status_code == 200\",\n \"def check_auth_token_validity(self):\\n endpoint = self.url + 'api/v1/jobs'\\n response = requests.get(endpoint, headers=self.authorization())\\n if response.status_code != 200:\\n self.print_error_response(response, \\\"detail\\\")\\n return response.status_code == 200\",\n \"def check_token(self, token):\\n if not token or not self.verification_token:\\n return False\\n if not constant_time_compare(token, self.verification_token):\\n return False\\n if self.is_verified:\\n return False\\n age = timezone.now() - self.added_date\\n if age >= timedelta(days=AssociatedEmail.VERIFICATION_TIMEOUT_DAYS):\\n return False\\n return True\",\n \"def verify_token(vial_http: urllib3.connectionpool.ConnectionPool) -> bool:\\n verify_resp = vial_http.request(\\\"GET\\\", \\\"/api/verifyToken\\\")\\n return verify_resp.status == 200\",\n \"def logged_in(self):\\n return self.auth.get_user_by_session() is not None\",\n \"def check_auth():\",\n \"def current_user_has_access(self):\\n return self.user_has_access(users.get_current_user())\",\n \"def verify_token(token):\\n if config.API_TOKEN is None:\\n logger.error(\\n 'API token is not configured, auth will fail!')\\n return token == config.API_TOKEN\",\n \"def verify_token(self, token):\\n _now = timezone.now()\\n\\n if (\\n (self.token is not None)\\n and (token == self.token)\\n and (_now < self.valid_until)\\n ):\\n self.token = None\\n self.valid_until = _now\\n self.save()\\n\\n return True\\n else:\\n return False\",\n \"def __check_token(self) -> bool:\\r\\n\\r\\n now = datetime.now(self.__tz)\\r\\n\\r\\n if (self.__token_expiration_date - now).total_seconds() < 0:\\r\\n log.debug('Token needs update!')\\r\\n return self.__update_token()\\r\\n return False\",\n \"def google_validate(self, token):\\n try:\\n payload = client.verify_id_token(token, GOOGLE_USER_ID)\\n if payload['iss'] not in ['accounts.google.com', 'https://accounts.google.com'] or payload['aud'] != GOOGLE_USER_ID:\\n return False\\n else:\\n return payload\\n except crypt.AppIdentityError:\\n return False\",\n \"def is_token_required(self):\\n return any([self.app_id, self._login, self._password])\",\n \"def validate_owner(model, request):\\n auth_token = request.headers.get('Authentication-Token')\\n user = _token_loader(auth_token)\\n if model.owner != user:\\n abort(401)\",\n \"def _verify_token(self, token, request):\\n # First check if this request was already verified.\\n # `request.bound_data` is an attribute provided by Kinto to store\\n # some data that is shared among sub-requests (e.g. default bucket\\n # or batch requests)\\n if REIFY_KEY not in request.bound_data:\\n user_id = None\\n client_name = None\\n auth_client = self._get_auth_client(request)\\n\\n for scope, client in request.registry._fxa_oauth_scope_routing.items():\\n try:\\n profile = auth_client.verify_token(token=token, scope=aslist(scope))\\n user_id = profile['user']\\n scope = profile['scope']\\n client_name = client\\n\\n # Make sure the bearer token scopes don't match multiple configs.\\n routing_scopes = request.registry._fxa_oauth_scope_routing\\n intersecting_scopes = [x for x in routing_scopes.keys()\\n if x and set(x.split()).issubset(set(scope))]\\n if len(intersecting_scopes) > 1:\\n logger.warn(\\\"Invalid FxA token: {} matches multiple config\\\" % scope)\\n return None, None\\n\\n break\\n except fxa_errors.OutOfProtocolError:\\n logger.exception(\\\"Protocol error\\\")\\n raise httpexceptions.HTTPServiceUnavailable()\\n except (fxa_errors.InProtocolError, fxa_errors.TrustError) as e:\\n logger.debug(\\\"Invalid FxA token: %s\\\" % e)\\n\\n # Save for next call.\\n request.bound_data[REIFY_KEY] = (user_id, client_name)\\n\\n return request.bound_data[REIFY_KEY]\",\n \"def is_token_revoked(decoded_token):\\n jti = decoded_token['jti']\\n token = BlacklistedToken.query.filter_by(jti=jti).first()\\n return token is not None\",\n \"def available(self):\\n return self.access_token is not None\",\n \"def verify_token(event):\\n if event['token'] != VERIFICATION_TOKEN:\\n print('Presented with invalid token - ignoring message...')\\n return False\\n return True\",\n \"def has_user(self, user): # pylint: disable=unused-argument\\r\\n return False\",\n \"def verify_privileged(self):\\n community_text = self.fetch(self.base_url + \\\"/community\\\")\\n return \\\"You must be logged in to see this page.\\\" not in community_text\",\n \"def has_permission(self, request, view):\\n if isinstance(request.user, TokenUser):\\n return True\\n\\n return super().has_permission(request, view)\",\n \"def has_token_in(status, token):\\n if not hasattr(res, status):\\n return False\\n return token in getattr(res, status)\",\n \"def is_revoked(self, token: str) -> bool:\\n return token in self.revoked_tokens\",\n \"def check_if_token_in_blacklist(decrypted_token):\\n return (\\n decrypted_token[\\\"jti\\\"] in BLACKLIST\\n ) # if True, go to revoked_token_callback\",\n \"async def __call__(\\n self, http_auth: HTTPAuthorizationCredentials = Depends(HTTPBearer())\\n ) -> Optional[bool]:\\n is_verified = self.verify_token(http_auth)\\n if not is_verified:\\n return None\\n\\n if self.scope_name:\\n is_verified_scope = self.verify_scope(http_auth)\\n if not is_verified_scope:\\n return None\\n\\n return True\",\n \"def early_return(bot:Bot, ctx:Context):\\n return ctx.message.author.bot or ctx.message.author.id == bot.user.id\",\n \"def check_google_token():\\n #get token from login page and google's token rigamorole\\n gtoken = request.form.get(\\\"idtoken\\\")\\n #validate token\\n g_profile = apiapijoyjoy.validate_google_token(gtoken)\\n #collect user info from google\\n name = g_profile['given_name']\\n lname = g_profile['family_name']\\n email = g_profile['email']\\n # start a session\\n session[\\\"user_id\\\"] = email\\n user = User.query.filter_by(email=email).first()\\n #create flags for Flask to return to google's scripts and take frontend action accordingly\\n if user:\\n return \\\"FLASK SEES USER\\\"\\n else:\\n #create new user in SQLAlchemy using info above from Google. BUT WAIT WHAT?! CODE REVIEW PLS!\\n new_user = User(email=email, name=name, lname=lname)\\n db.session.add(new_user)\\n db.session.commit()\\n return \\\"FLASK SEES NO USER\\\"\",\n \"def _is_oauth_token_valid(token: dict, time_key=\\\"expires_on\\\") -> bool:\\n if \\\"access_token\\\" not in token or token.get(\\\"token_type\\\", \\\"\\\") != \\\"Bearer\\\" or time_key not in token:\\n raise AirflowException(f\\\"Can't get necessary data from OAuth token: {token}\\\")\\n\\n return int(token[time_key]) > (int(time.time()) + TOKEN_REFRESH_LEAD_TIME)\",\n \"def get_all_repos(oauth_token, repo_name):\\n print('Checking if the user can access the repo...')\\n g = Github(oauth_token)\\n print('\\\\tRunning as ' + g.get_user().name)\\n all_repos = g.get_user().get_repos()\\n all_repos_full_name = []\\n for test in list(all_repos):\\n all_repos_full_name.append(test.full_name)\\n if repo_name in all_repos_full_name:\\n return True\\n else:\\n print('Repo \\\"' + repo_name + '\\\" is not in the list')\\n print('Here\\\\'s a list of all repos I can see:')\\n for each_repo in all_repos:\\n print('\\\\t' + each_repo.full_name)\\n return False\",\n \"async def cog_check(self, ctx:utils.Context):\\n\\n if ctx.author.id in self.bot.config['owners']:\\n return True\\n raise commands.NotOwner\",\n \"def is_authenticated(self):\\n if not self.token:\\n return False\\n\\n try:\\n self.lookup_token()\\n return True\\n except Forbidden:\\n return False\\n except InvalidPath:\\n return False\\n except InvalidRequest:\\n return False\",\n \"def gatekeeper():\\n\\n if user.name in GATEKEEPERS:\\n return True\\n\\n return False\",\n \"def logged_in(request):\\n return request.current_user is not None\",\n \"def is_blacklisted(token):\\n if Revoked.query.filter_by(token=token).first():\\n return True\\n return False\",\n \"def check_key(request):\\n try:\\n access_key = request.session.get('access_key_tw', None)\\n if not access_key:\\n return False\\n except KeyError:\\n return False\\n return True\\n\\n\\t# User info\",\n \"def get_github_token_info(g):\\n rate_limit = g.get_rate_limit()\\n near_expiry = rate_limit.core.remaining < 50\\n wait_time = (rate_limit.core.reset - datetime.datetime.utcnow()).seconds\\n return near_expiry, wait_time\",\n \"def verify_password(self, google_token):\\n return google_token == self.google_token\",\n \"def github_complete(request):\\n logger.debug(\\\"Received user returning from Github.\\\")\\n # Exchange code for token.\\n # This creates an OpenHumansMember and associated user account.\\n code = request.GET.get('code', '')\\n ohmember = request.user.openhumansmember\\n github_member = github_code_to_member(code=code, ohmember=ohmember)\\n\\n if github_member:\\n messages.info(request, \\\"Your Github account has been connected\\\")\\n process_github.delay(ohmember.oh_id)\\n return redirect('/dashboard')\\n\\n logger.debug('Invalid code exchange. User returned to starting page.')\\n messages.info(request, (\\\"Something went wrong, please try connecting your \\\"\\n \\\"Github account again\\\"))\\n return redirect('/dashboard')\",\n \"def callback_repo_check(self, request, uri, headers, status_code=404):\\n self.assertEqual(\\n request.headers['Authorization'],\\n 'token {0}'.format(self.OAUTH2_TOKEN)\\n )\\n # Handle the new \\\"rerun\\\" repo differently\\n if self.TEST_RERUN_REPO in uri:\\n status_code = 404\\n return (status_code, headers, json.dumps({'message': 'testing'}))\",\n \"def is_me(self, m):\\n return m.author == self.client.user\",\n \"def is_user_check(fn):\\n @wraps(fn)\\n def wrapper(*args, **kwargs):\\n verify_jwt_in_request()\\n token = get_jwt_identity()\\n if token['is_user_token'] is False:\\n _token_usage_counter_add(token['data_source_token']['id'])\\n return ErrorObject.create_response(\\n ErrorObject, HTTPStatus.FORBIDDEN,\\n 'Unable to access this resource with provided token')\\n else:\\n return fn(*args, **kwargs)\\n\\n return wrapper\",\n \"def token_valid_check(start_time):\\n #calculate the time elapsed since token was last refreshed\\n elapsed_time = time.time() - start_time\\n #take action if token is expired\\n if elapsed_time > 3540:\\n return False\\n return True\",\n \"def authorizes(self, user):\\n return self.owner == user or self.workers.filter(pk=user.id).exists()\",\n \"def logged_in(self):\\n return self.user is not None\",\n \"def authenticated():\\n if 'user_id' in session and 'access_token' in session:\\n user = db_session.query(User).filter_by(id=session['user_id']).first()\\n\\n if user:\\n return user.access_token == session['access_token']\\n return False\",\n \"def user_is_same(current_user_number, past_user_numbers):\\n if current_user_number == past_user_numbers:\\n return True\\n else:\\n return False\",\n \"async def bot_check(self, ctx):\\n blocked = await self.db.fetchrow(\\n \\\"\\\"\\\"\\n SELECT *\\n FROM blocks\\n WHERE user_id=$1\\n \\\"\\\"\\\",\\n ctx.author.id,\\n )\\n if blocked is None:\\n return True\\n raise BlackListed\",\n \"def validate_sha_github(sha):\\n r = requests.head(github_changeset_url % sha)\\n return r.status_code == 200\",\n \"def _validate_token(self):\\n if not self.token:\\n self.login()\\n if not self.token:\\n # TODO: create exception for this\\n # Access is denied!!\\n raise Exception(\\\"AccessDenied\\\")\",\n \"def check_auth_token_validity(self):\\n endpoint = self.url + 'api/v1/readiness'\\n response = self.perform_get_request(endpoint)\\n\\n if response.status_code != 200:\\n self.print_error_response(response, \\\"error\\\")\\n return response.status_code == 200\",\n \"def github():\\n try:\\n GITHUB_API = \\\"https://api.github.com/users/{0}/events\\\".format(self.user.username)\\n\\n accepted_events = AcceptedEvent.objects.values_list(\\\"name\\\", flat=True)\\n events = json.loads(requests.get(GITHUB_API).text)\\n for event in events:\\n # it needs to be either a commit or a pull request\\n # it must also be after the last update.\\n if event[\\\"type\\\"] in accepted_events \\\\\\n and self.date < dateutil.parser.parse(event[\\\"created_at\\\"]):\\n return True\\n else:\\n return False\\n except:\\n return False\",\n \"def accessCheck(self) -> None:\\n\\n if self.access_token:\\n return\\n self.access_token = self.login()\",\n \"def _check_token_is_revoked(self, jti: str) -> None:\\n redis = self._conn_redis()\\n entry = redis.get(jti)\\n if entry and entry == 'true':\\n raise HTTPException(status_code=401,detail=\\\"Token has been revoked\\\")\",\n \"def auth_check():\\n try:\\n access_token = session_get('access_token')\\n access_token_secret = session_get('access_token_secret')\\n\\n if access_token and access_token_secret:\\n tk = get_twitter_keys()\\n client = UserClient(\\n tk.consumer_key,\\n tk.consumer_secret,\\n access_token=access_token,\\n access_token_secret=access_token_secret)\\n \\\"\\\"\\\"\\n We need to make a call to verify_credentials in case the user\\n has revoked access for this application. This is a rate-limited\\n call and so this approach might not be ideal. If we end up\\n having rate-limiting problems, we might try giving each user\\n a unique application ID that is kept in local storage and used\\n as a lookup for Twitter creds (vs. session data which is domain-\\n specific and thus problematic for our extension-approach). This\\n might allow us to consolidate Twitter creds per user rather than\\n storing them for each domain visited.\\\"\\\"\\\"\\n verif = client.api.account.verify_credentials.get()\\n if verif.headers['status'].split()[0] == '200':\\n return jsonify({'is_auth': 1})\\n else:\\n # possibly revoked access, although this will probably\\n # get handled by the TwitterAuthError catch\\n remove_session_credentials()\\n return jsonify({'is_auth': 0})\\n tk = get_twitter_keys()\\n client = UserClient(tk.consumer_key, tk.consumer_secret)\\n callback = 'http://'+request.host+url_for('auth_verify')\\n token = client.get_authorize_token(callback)\\n session_set('auth_token', token.oauth_token)\\n session_set('auth_token_secret', token.oauth_token_secret)\\n session_set('auth_redirect',\\n request.args.get('redirect') or '')\\n if (\\n 'html' in request.headers['Accept']\\n and request.args.get('_format') != 'json'):\\n return redirect(token.auth_url)\\n else:\\n data = {'is_auth': 0, 'auth_url': token.auth_url}\\n return jsonify(data)\\n except TwitterAuthError:\\n remove_session_credentials()\\n return jsonify({'is_auth': 0})\\n except Exception, e:\\n traceback.print_exc()\\n return jsonify({'error': str(e)})\",\n \"def is_authorized():\\n return CentralStorageClient.token is not None\",\n \"def check_token(api_key, required_scopes):\\n return {\\\"uid\\\": \\\"\\\"}\",\n \"def check_for_token(token):\\n try:\\n decode_token(token)\\n return True\\n except:\\n return False\",\n \"def check_auth(uid, token, ts_check):\\n if token is None:\\n token_row = TokenAuth.objects.filter(user_id=uid).order_by(\\\"-created_at\\\")[:1]\\n else:\\n token_row = TokenAuth.objects.filter(user_id=uid, token=token).order_by(\\\"-created_at\\\")[:1]\\n\\n if not token_row:\\n return False, None\\n\\n difference = ts_check - timezone.now()\\n\\n if difference.days > 90:\\n return False, token_row[0].token\\n return True, token_row[0].token\",\n \"def authorized(token):\\n url = \\\"https://api.github.com/user?access_token=\\\" + str(token)\\n req = requests.get(url)\\n github_username = req.json()['login']\\n\\n redirect_to = request.args.get('next') or url_for('index')\\n if token is None:\\n return redirect(redirect_to)\\n\\n user = db_session.query(User)\\\\\\n .filter_by(github_username=github_username).first()\\n if user is None:\\n user = User(github_username=github_username)\\n db_session.add(user)\\n\\n user.access_token = token\\n db_session.commit()\\n\\n session['user_id'] = user.id\\n session['access_token'] = user.access_token\\n session['username'] = user.github_username\\n\\n return redirect(url_for('index'))\",\n \"def is_token_revoked(decoded_token):\\r\\n jti = decoded_token['jti']\\r\\n try:\\r\\n token = TokenBlacklist.query.filter_by(jti=jti).one()\\r\\n return token.revoked\\r\\n except NoResultFound:\\r\\n return True\",\n \"def requires_auth(self):\\n return True\",\n \"def user_in_session():\\n return 'user_id' in login_session\",\n \"def is_public_token(token):\\n return (\\n token.payload\\n and token.payload.get(\\\"roles\\\") == [NONE]\\n and token.payload.get(\\\"context_id\\\") is None\\n and token.payload.get(\\\"consumer_site\\\") is None\\n )\",\n \"def check_blacklisted_token(token):\\n token = models.TokenBlackList.query.filter_by(token=token).first()\\n if token:\\n return True\\n return False\",\n \"def test_verification_with_valid_token(self) -> None:\\n\\n secret_key = str(self.author.secret_key)\\n verification_url = reverse('author:verify', kwargs={'secret_key': str(secret_key)})\\n\\n # Make sure URL's don't change.\\n self.assertEqual(verification_url, f'/api/authors/verify/{secret_key}/')\\n\\n # Make valid request and get response\\n response: Response = self.client.get(verification_url)\\n\\n self.assertEqual(response.status_code, 302)\\n\\n # Now test if the method \\\"verify\\\" was called\\n self.assertEqual(Author.objects.get().verified, True)\\n # We don't wanna give him too many privileges\\n self.assertEqual(self.author.is_staff, False)\",\n \"def is_token_valid(self,pk,request):\\n\\n pass\",\n \"def test_get_github_name_positive(self):\\n self.assertIsNotNone(app.get_github_name(\\\"dhh\\\")[\\\"user\\\"])\",\n \"def authenticated(self, user_token, **validation_context):\\n token = self.token_storage.get(user_token)\\n if token and token.validate(user_token, **validation_context):\\n return True\\n\\n return False\",\n \"def test_correct_token_working(self, client, token, update_repo_mock):\\n assert_hook_status(client, status=204, token=token)\\n assert update_repo_mock.called\",\n \"def is_authenticated(self):\\n return bool(get_auth_token())\",\n \"def check_user():\\n token = request.headers['Authorization'].replace('Bearer ', '')\\n return jsonify({\\\"access_token\\\": token}), 200\",\n \"async def check_discord_user(ctx: Context):\\n user = PostgreSQL.get_discord_user(ctx.message.author.id)\\n if user is None:\\n await ctx.send(f'You have not been verified yet. Please visit {WEBSITE} to get verified (Aber VPN is required off campus)')\\n return\\n return user\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65147024","0.6480705","0.63269347","0.6250972","0.62163246","0.6209365","0.6175841","0.6166921","0.6119698","0.6116468","0.6107161","0.6025452","0.6002667","0.5976334","0.59564173","0.59537226","0.59510547","0.59139603","0.5908455","0.5898309","0.58285254","0.5825101","0.5805655","0.58043355","0.5799294","0.57725435","0.5771588","0.57593256","0.57247657","0.5711031","0.5711031","0.5710035","0.5696743","0.56835985","0.5682184","0.56718236","0.56635743","0.5658053","0.5656704","0.5653456","0.56441844","0.56258285","0.5624824","0.5589715","0.5584055","0.5582305","0.55397725","0.5499762","0.54827166","0.54714984","0.54601216","0.5415695","0.5410101","0.54080534","0.54007834","0.54007065","0.53940207","0.53891754","0.53864044","0.5386322","0.5384934","0.5384617","0.53831506","0.53775495","0.53740394","0.5373388","0.5369358","0.5357557","0.5356118","0.535562","0.53542894","0.53475636","0.53435993","0.5338428","0.53268325","0.5309836","0.53048855","0.5302104","0.5301944","0.530041","0.5281641","0.52772635","0.52701396","0.52622795","0.52553964","0.52478296","0.5245895","0.5243444","0.5243246","0.524062","0.5235482","0.5235306","0.5227904","0.5221113","0.52131194","0.52128214","0.52075654","0.52073765","0.5201873","0.5196842"],"string":"[\n \"0.65147024\",\n \"0.6480705\",\n \"0.63269347\",\n \"0.6250972\",\n \"0.62163246\",\n \"0.6209365\",\n \"0.6175841\",\n \"0.6166921\",\n \"0.6119698\",\n \"0.6116468\",\n \"0.6107161\",\n \"0.6025452\",\n \"0.6002667\",\n \"0.5976334\",\n \"0.59564173\",\n \"0.59537226\",\n \"0.59510547\",\n \"0.59139603\",\n \"0.5908455\",\n \"0.5898309\",\n \"0.58285254\",\n \"0.5825101\",\n \"0.5805655\",\n \"0.58043355\",\n \"0.5799294\",\n \"0.57725435\",\n \"0.5771588\",\n \"0.57593256\",\n \"0.57247657\",\n \"0.5711031\",\n \"0.5711031\",\n \"0.5710035\",\n \"0.5696743\",\n \"0.56835985\",\n \"0.5682184\",\n \"0.56718236\",\n \"0.56635743\",\n \"0.5658053\",\n \"0.5656704\",\n \"0.5653456\",\n \"0.56441844\",\n \"0.56258285\",\n \"0.5624824\",\n \"0.5589715\",\n \"0.5584055\",\n \"0.5582305\",\n \"0.55397725\",\n \"0.5499762\",\n \"0.54827166\",\n \"0.54714984\",\n \"0.54601216\",\n \"0.5415695\",\n \"0.5410101\",\n \"0.54080534\",\n \"0.54007834\",\n \"0.54007065\",\n \"0.53940207\",\n \"0.53891754\",\n \"0.53864044\",\n \"0.5386322\",\n \"0.5384934\",\n \"0.5384617\",\n \"0.53831506\",\n \"0.53775495\",\n \"0.53740394\",\n \"0.5373388\",\n \"0.5369358\",\n \"0.5357557\",\n \"0.5356118\",\n \"0.535562\",\n \"0.53542894\",\n \"0.53475636\",\n \"0.53435993\",\n \"0.5338428\",\n \"0.53268325\",\n \"0.5309836\",\n \"0.53048855\",\n \"0.5302104\",\n \"0.5301944\",\n \"0.530041\",\n \"0.5281641\",\n \"0.52772635\",\n \"0.52701396\",\n \"0.52622795\",\n \"0.52553964\",\n \"0.52478296\",\n \"0.5245895\",\n \"0.5243444\",\n \"0.5243246\",\n \"0.524062\",\n \"0.5235482\",\n \"0.5235306\",\n \"0.5227904\",\n \"0.5221113\",\n \"0.52131194\",\n \"0.52128214\",\n \"0.52075654\",\n \"0.52073765\",\n \"0.5201873\",\n \"0.5196842\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77639025"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74587,"cells":{"query":{"kind":"string","value":"Find issues with the label \"welcome\""},"document":{"kind":"string","value":"def whatsnew(self, msg, args):\n self._asset_bind(msg)\n client = self._github_operator(msg)\n cmd = \"repo:{} label:welcome is:open type:issue\".format(\n task_repository_name())\n issue_list = client.search_issue(cmd, 10)\n result = limit_result(\n [\"{}: {}\".format(i.number, i.title)\n for i in issue_list])\n yield \"\\n\".join(result)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_issue_get_label(self):\n pass","def test_issue_get_labels(self):\n pass","def test_issue_list_labels(self):\n pass","def test_issue_add_label(self):\n pass","def test_issue_replace_labels(self):\n pass","def test_issue_create_label(self):\n pass","def _find_errors_in_page(self, response):\n if response.status_code == 403:\n return \"Could not check for errors, as response was a 403 response\\\n forbidden. User asking for this url did not have permission.\"\n \n \n errors = re.search('<ul class=\"errorlist\">(.*)</ul>', \n response.content, \n re.IGNORECASE)\n\n if errors: \n #show a little around the actual error to scan for variables that\n # might have caused it\n span = errors.span()\n wide_start = max(span[0]-200,0)\n wide_end = min(span[1]+200,len(response.content)) \n wide_error = response.content[wide_start:wide_end]\n return wide_error\n \n return \"\"","def label_milestone_issues():\n session = GithubSession()\n\n labels = list(session.get_labels())\n labels_by_name = dict([(x['name'], x) for x in labels])\n\n milestones = list(session.get_milestones())\n\n for milestone in milestones:\n label_data = labels_by_name[f'epic:{milestone[\"title\"].strip()}']\n\n for issue in session.get_issues(milestone=milestone[\"number\"], state='all'):\n session.add_label(label_data, number=issue['number'])","def test_issue_remove_label(self):\n pass","def test_labels(self):\n self.compliance_tester.test_labels(self.oi)","def test_label_indices_unknown():\n with pytest.raises(ValueError):\n util.get_label_indices(3, \"random_str\")","def test_issue_edit_label(self):\n pass","def get_labels():\n return if_found(dao.get_labels())","def _FindLabels(self):\n texs = \" \".join(glob.glob(\"*.tex\"))\n cat_process = subprocess.Popen(shlex.split(\"cat %s\" % texs),\n stdout=subprocess.PIPE)\n grep_process = subprocess.Popen(shlex.split(r\"grep \\\\\\\\label\"),\n stdin=cat_process.stdout,\n stdout=subprocess.PIPE)\n cat_process.stdout.close()\n\n lines = grep_process.communicate()[0]\n\n ret = []\n for label in lines.split(\"\\n\"):\n ret.append(responses.BuildCompletionData(\n re.sub(r\".*\\label{(.*)}.*\", r\"\\1\", label)\n )\n )\n\n return ret","def test_labels(ruler: SpaczzRuler) -> None:\n assert all(\n [label in ruler.labels for label in [\"GPE\", \"STREET\", \"DRUG\", \"NAME\", \"BAND\"]]\n )\n assert len(ruler.labels) == 5","def labels_list(issue):\n return [x['name'] for x in issue['labels']]","def labels(self):\r\n return labels.IssueLabels(self)","def labels_validation(ele,actultext):\r\n msg, status = \"\", True\r\n\r\n try:\r\n 'Here we will pass objectidentifier for required element'\r\n text_heading = ui_controls.text_view(get_obj_identifier(ele))\r\n print \"Current label returned by UI is==>\"+str(text_heading)\r\n\r\n 'To check if it is returning empty value here'\r\n if not text_heading:\r\n print str(actultext)+\" label does not exist and it is returning empty value.\"\r\n return False, msg\r\n\r\n 'Comparing text retrieved from UI with validation text'\r\n if actultext.strip() == text_heading.strip():\r\n print (str(actultext)+\" label has been found!!!\")\r\n else:\r\n print(\"Sorry!!!lable has been mismatched,it should be \"+str(actultext))\r\n print (\"label shown at UI is==>\"+str(text_heading))\r\n return False, msg \r\n\r\n except Exception as excp:\r\n traceback.print_exc()\r\n msg += str(excp)\r\n status = False\r\n return status, msg","def test_issue_search_issues(self):\n pass","def user_labels_find(*args):\n return _ida_hexrays.user_labels_find(*args)","def test_welcome_exploration(self):\n self.init_player(\n '0', 'Welcome to Oppia!', 'do you know where the name \\'Oppia\\'')\n self.submit_and_compare(\n '0', 'Yes!', 'In fact, the word Oppia means \\'learn\\'.')\n self.submit_and_compare('Finish', 'Check your spelling!', '')\n self.submit_and_compare(\n 'Finnish', 'Yes! Oppia is the Finnish word for learn.',\n 'What is the value of')","def labels(self):\n raise WTFError(\"is this your first day?\")","def get_label(repo, title, verbose=None):\n if verbose:\n print \"Checking for label...\"\n label = None\n label_text = None\n try:\n label_start = 1 + title.index('(')\n label_end = title.index(')')\n label_text = title[label_start:label_end]\n except ValueError, e:\n print \"Warning: This tile has no embeded label. {0}\".format(e)\n if label_text:\n try:\n label = [repo.get_label(label_text)]\n if verbose:\n print \"Found label: {0}\".format(label)\n except UnknownObjectException, e:\n print \"Error: The label '{0}' does not exist on \" \\\n \"Github. {1}\".format(label_text, e)\n return label","def disp_notfound():\n from x84.bbs import getsession, getterminal, echo, getch\n term = getterminal()\n echo(u''.join((u'\\r\\n\\r\\n',\n term.bold(u'bAd REQUESt'),\n term.bold_red(' -/- '),\n term.bold('NOt fOUNd.',),)))\n if not getsession().user.get('expert', False):\n getch(1.7)","def label(cls) -> str:\n return \"!lobotomy.error\"","def label(self, name):\r\n return labels.IssueLabel(self, name)","def getissue(s,refconvdf,issuetaglist):\r\n #check if empty\r\n taglist=gettotaltags(s,refconvdf)\r\n issuename=list(set(issuetaglist.name.values).intersection(taglist)) \r\n if not issuename:\r\n return 'None'\r\n else:\r\n return issuename","def test_issue_clear_labels(self):\n pass","def get_fatal_alerts(self, path):","def pull_labels(self, org):\n pass","def label_messages(self, org, messages, label):\n pass","def auto_label_text_chunk(text, labels):\n\n for label in labels:\n regex_str = \"|\".join(labels[label])\n r = re.compile(regex_str, flags=re.I)\n if re.search(r, text) is not None:\n return label\n\n return \"None\"","def milestone_labels(argv=None):\n argv = argv or sys.argv[1:]\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument('color', help='color to make the labels')\n\n args = parser.parse_args(argv)\n\n session = GithubSession()\n\n labels = session.get_labels()\n\n labels_by_name = dict([(label['name'], label) for label in labels])\n\n for milestone in session.get_milestones():\n label_name = f'epic:{milestone[\"title\"]}'\n\n if label_name in labels_by_name:\n continue\n\n labels_by_name[label_name] = session.create_label(label_name, args.color)\n\n return labels_by_name","def _search_issues(self, summary):\n try:\n issues = self.jira.get_issues_by_summary(summary)\n except Exception as e:\n logging.error(\"Failed searching issues: \"+ str(e))\n return []\n return issues","def missing_entry_error(entry, name, yml):\n\n yml = symlink_target(yml)\n output_1 = path(yml) + '\\n'\n output_2 = colored(' - Error: Missing ', 'red')\n output_3 = colored(str(entry), attrs=['bold'])\n output_4 = colored(' in ', 'red')\n output_5 = colored(str(name), attrs=['bold'])\n return output_1 + output_2 + output_3 + output_4 + output_5","def label(cls) -> str:\n return \"!lobotomy.unknown\"","def test_label_returns_not_found_without_query(client):\n response = client.get(\n reverse('name:label', args=['']))\n assert 404 == response.status_code\n assert b'No matching term found' not in response.content","def display_for_triage(bugs):\n # bug title is like:\n # '\n # Bug #1724025 in openstack-ansible:\n # invalid regular expression...\"\n # '\n for bug in bugs:\n bug_name = u\"\".join(bug.title.split(\":\")[1:])\n print(u\"#link {link}\\n\\t{name}\".format(link=bug.web_link, name=bug_name))","def test_issue_delete_label(self):\n pass","def test_label_returns_not_found_with_query(client):\n response = client.get(\n reverse('name:label', args=['&&&&&&&&']))\n assert 404 == response.status_code\n assert b'No matching term found' in response.content","def get_label(issue_id):\n auth = subprocess.check_output(\n \"gsutil cat\"\n \" gs://bazel-trusted-encrypted-secrets/github-trusted-token.enc |\"\n \" gcloud kms decrypt --project bazel-public --location global\"\n \" --keyring buildkite --key github-trusted-token --ciphertext-file\"\n \" - --plaintext-file -\", shell=True\n ).decode(\"utf-8\").strip().split(\"\\n\")[0]\n headers = {\n \"Authorization\": \"Bearer \" + auth,\n \"Accept\": \"application/vnd.github+json\",\n }\n response = requests.get(\n \"https://api.github.com/repos/bazelbuild/bazel/issues/\"\n + issue_id + \"/labels\", headers=headers,\n )\n for item in response.json():\n for key, value in item.items():\n if key == \"name\" and \"team-\" in value:\n return value.strip()\n return None","def test_issue_list_issues(self):\n pass","def get_label(self, name):\n label_list = self.wls_board.get_labels()\n for label in label_list:\n if name in label.name: \n return label","def mistake_not_found():\n win6 = Toplevel(root)\n win6.title(\"Ошибка\")\n win6.geometry('270x40')\n lbl13 = Label(win6, text=\"Значение не найдено в базе данных!\", width=45, height=2)\n lbl13.place(x=-30, y=0)","def command_show(problem):\r\n print problem.get_html()","def verify_homeScreen():\r\n msg = \"\"\r\n try:\r\n 'Getting home page label for IOS - Open Manage Essentials'\r\n if g.platform == 'ios':\r\n text_view = ui_controls.text_view(get_obj_identifier('home_OME_lbl'), value=True)\r\n text_toverify = g.ios_homeScreen_label\r\n else:\r\n sleep(3)\r\n text_view = ui_controls.text_view(get_obj_identifier('home_OME_lbl'))\r\n text_toverify = g.android_homeScreen_label\r\n\r\n 'Verifying whether home screen label is Open Manage Essentials for IOS'\r\n if text_view.strip() == text_toverify :\r\n print \"Home Screen label is verified successfully\"\r\n else:\r\n print \"Home Screen label is not verified successfully\"\r\n return False, msg\r\n except Exception as excp:\r\n traceback.print_exc()\r\n msg += str(excp)\r\n return True, msg","def check_feedback(self, step):\r\n world.wait_for_visible(self.active_problem_selector('.tag-status.correct'))\r\n assert_equals(len(world.css_find(self.active_problem_selector('.tag-status.correct'))), 1)\r\n assert_equals(len(world.css_find(self.active_problem_selector('.show'))), 1)","def errors_fatal(self) -> List[Error]:","def check_happiness(content):\n baseline = 100\n happiness = [\"Smile\"]\n label = 5\n # print content\n emotion_time = content[0][1]\n # print 'emotion_time',emotion_time\n for c in content:\n for h in happiness:\n # print h\n if c[0] == h and c[1] >= baseline:\n print 'emotion & label',emotion_time, label\n return emotion_time, label","def verify_aboutScreen():\r\n msg = \"\"\r\n try:\r\n 'Getting about page label for IOS - About'\r\n if g.platform == 'ios':\r\n text_view = ui_controls.text_view(get_obj_identifier('mainMenu_about_lbl'), value=True)\r\n else:\r\n text_view = ui_controls.text_view(get_obj_identifier('mainMenu_about_lbl'))\r\n\r\n 'Verifying whether about screen label is About for IOS'\r\n if text_view.strip() == g.aboutScreen_label :\r\n print \"About Screen label is verified successfully\"\r\n else:\r\n print \"About Screen label is not verified successfully\"\r\n return False, msg\r\n except Exception as excp:\r\n traceback.print_exc()\r\n msg += str(excp)\r\n return True, msg","def welcome_users(identities):\n welcome_message = 'Hello'\n is_recognized_file = open('varThread\\\\recognized.check', 'w')\n is_recognized_file.write('1')\n is_recognized_file.close()\n if len(identities) == 1:\n welcome_message += '%s!' % identities[0]\n print(welcome_message)\n else:\n for identity_id in range(len(identities)-1):\n welcome_message += '%s, ' % identities[identity_id]\n welcome_message += 'and %s, ' % identities[-1]\n welcome_message += '!'\n print(welcome_message)","def check_happiness(content):\n baseline = 50\n happiness = [\"Smile\"]\n label = 5\n # print content\n emotion_time = content[0][1]\n # print 'emotion_time',emotion_time\n for c in content:\n for h in happiness:\n # print h\n if c[0] == h and c[1] >= baseline:\n print 'emotion & label',emotion_time, label\n return emotion_time, label","def find_mistake(self):\n if self.solve_stats[-1][\"comment\"] == \"\":\n for stat in reversed(self.solve_stats):\n if stat[\"comment\"] != \"\":\n self.solve_stats[stat[\"count\"]][\"comment\"] += \"mistake from here\"\n break","def test_warning_in_log():\n\n log_file_path = \"mobile_testkit_tests/test_data/mock_panic_log.txt\"\n\n with pytest.raises(AssertionError) as e:\n scan_logs.scan_for_errors(['warning'], log_file_path)\n\n error_message = str(e.value)\n assert error_message.startswith(\"warning found!!\")","def test_navigates_to_index_page_about_page(w_driver):\n #Index Page\n w_driver.get('localhost:8000')\n results=w_driver.page_source\n text_found1=re.search(r'Welcome to the Kasner Micro Search Engine', results)\n\n #About Page\n w_driver.get('localhost:8000/about')\n results=w_driver.page_source\n text_found2=re.search(r'About the Kasner Search Engine',results)\n \n assert(text_found1 != None and text_found2 != None)","def test_text_found_in_single_slide(collected_seg_motif):\n slide_not_found, error_records_future, error_records_seg, skip_test = collected_seg_motif\n if skip_test:\n pytest.skip(\"File does not have nti_data\")\n #print(\"error_records_future : \", error_records_future)\n print(\"error_records_seg : \", error_records_seg)\n if len(error_records_seg) > 0:\n print(\"error count : \", len(error_records_seg))\n print(\"error slides : \", (error_records_seg))\n pytest.fail(\n \"errors found. Count of missing rem slides in segments %s , count of remediation slides in nore than 1 slide %s\"\n % (slide_not_found, error_records_seg))","def projects_label(args):\n session = GithubSession()\n\n label_datas = list(session.get_labels())\n\n team = args.team\n team_label_data = None\n if team:\n team_label = utils.get_label(team, prefix=\"team\")\n team_label_data = [x for x in label_datas if x[\"name\"] == team_label][0]\n\n # get the project label\n project_label = utils.get_label(args.name, prefix=\"project\")\n project_label_data = [x for x in label_datas if x[\"name\"] == project_label][0]\n\n print(f\"label cards in project {args.name} column {args.column}\")\n\n project_board = session.get_project(args.name)\n project_backlog_grooming = session.get_column(project_board, \"backlog grooming\")\n\n cards = list(session.get_cards(project_backlog_grooming))\n for card_data in cards:\n issue_number = utils.get_issue_number_from_card_data(card_data)\n\n print(issue_number)\n\n # add the project label\n session.add_label(project_label_data, number=issue_number)\n\n if team_label_data:\n session.add_label(team_label_data, number=issue_number)","def search_for_warnings(self):\n log = str()\n print(self.default_starter_args + self.arguments)\n if not self.log_file.exists():\n print(str(self.log_file) + \" not there. Skipping search\")\n return\n print(str(self.log_file))\n with self.log_file.open(errors=\"backslashreplace\") as log_f:\n for line in log_f.readline():\n if \"WARN\" in line or \"ERROR\" in line:\n print(line.rstrip())\n log += line.rstrip()\n attach(log, \"WARN or ERROR lines from starter log\")","def test_labels_warning(self):\n with pytest.warns(\n YellowbrickWarning, match=\"both classes and encoder specified\"\n ):\n oz = ClassificationScoreVisualizer(\n GaussianNB(),\n classes=[\"a\", \"b\", \"c\"],\n encoder={0: \"foo\", 1: \"bar\", 2: \"zap\"},\n )\n labels = oz._labels()\n npt.assert_array_equal(labels, [\"foo\", \"bar\", \"zap\"])","def test_welcome_exploration(self):\n player = self.ExplorationPlayer(self.testapp, self.assertEqual, '0')\n\n response = player.get_initial_response()\n self.assertIn(\n 'do you know where the name \\'Oppia\\'', response['oppia_html'])\n self.assertEqual(response['title'], 'Welcome to Oppia!')\n\n response = player.submit_answer('0')\n self.assertIn(\n 'In fact, the word Oppia means \\'learn\\'.', response['oppia_html'])","async def issues(self, ctx):\n await ctx.message.delete()\n await ctx.send(\"Issue tracker: https://github.com/TheSuperGamer20578/Sudan-bot/issues\")","def details_not_matching():\n print(\"login details don't match.\")","def main():\n homepage_to_prefixes = defaultdict(set)\n for prefix in bioregistry.read_registry():\n if bioregistry.is_deprecated(prefix):\n continue\n homepage = bioregistry.get_homepage(prefix)\n if homepage is None:\n continue\n homepage_to_prefixes[homepage].add(prefix)\n\n rv = thread_map(_process, list(homepage_to_prefixes.items()), desc=\"Checking homepages\")\n\n failed = sum(failed for _, _, failed, _ in rv)\n click.secho(\n f\"{failed}/{len(rv)} ({failed / len(rv):.2%}) homepages failed to load\", fg=\"red\", bold=True\n )\n\n df = pd.DataFrame(\n columns=[\"prefix\", \"homepage\", \"message\"],\n data=[\n (prefix, homepage, msg)\n for homepage, prefixes, failed, msg in rv\n if failed\n for prefix in prefixes\n ],\n )\n click.echo(df.to_markdown())\n sys.exit(1 if 0 < failed else 0)","def get_known_words(label_matches):\r\n\tknown_words = set()\r\n\tfor key in label_matches.keys():\r\n\t\tif key[0] not in known_words:\r\n\t\t\tknown_words.add(key[0])\r\n\treturn known_words","def verify_diagnostics_and_usage_screen_name():\r\n msg = \"\"\r\n try:\r\n sleep(10)\r\n if g.platform == 'android':\r\n\r\n 'Verify screen name of diagnostics and usage page'\r\n sleep(3)\r\n \r\n flag1,msg = element_textvalidation('Diagnostics_usage_lbl','Diagnostics and Usage')\r\n \r\n \r\n flag = False if not (flag1) else True\r\n \r\n else:\r\n\r\n 'Verify screen name of diagnostics and usage label'\r\n \r\n flag1,msg = label_textvalidation('Diagnostics_usage_lbl','Diagnostics and Usage')\r\n \r\n\r\n flag = False if not (flag1) else True\r\n if not flag:\r\n return False, msg\r\n else:\r\n print \"License agreement screen name is displayed properly\"\r\n except Exception as excp:\r\n traceback.print_exc()\r\n msg += str(excp)\r\n return False, msg\r\n return True, msg","def ping_leads_of_idle_issues():\n for repo_id in github_api.get_all_repos():\n issues = github_api.get_issues_with_label(repo_id, \"idle\")\n if not issues:\n continue\n\n idle_issue_numbers = [(i[\"number\"], i[\"html_url\"]) for i in issues]\n\n message = ('The following issue(s) in <{repo_url}|{repo}> are '\n 'idle: {issue_links}. (cc: {leads})').format(\n repo_url=\"https://github.com/{}/{}\".format(repo_id.owner, repo_id.name),\n repo=\"{}/{}\".format(repo_id.owner, repo_id.name),\n issue_links=\", \".join(\n \"<{link}|#{number}>\".format(link=link, number=number)\n for (number, link) in idle_issue_numbers),\n leads=\", \".join(github_api.get_leads_for_repo(repo_id)))\n \n slack_api.send_message(message)","def test_headlines_predefined(self) -> None:\n for headline in self.report.headlines:\n if not self.rules.get_headline_rules(headline.name):\n headlines = [headline.name for headline in self.rules.headlines]\n suggestion, _ = process.extractOne(\n headline.name, headlines, scorer=fuzz.partial_ratio\n )\n self.add_error(\n f\"{headline.name} är inte en valid rubrik. \"\n f\"Rättningsförlsag: {suggestion}.\",\n headline=headline,\n )\n elif re.search(\"\\\\W{1,}\", headline.name, re.I):\n self.add_error(\n f\"Rubriken {headline.name} innehåller tecken som inte är \"\n \"alfanumeriska vilket inte är tillåtet för en rubrik.\",\n headline=headline,\n )","def checkForIndexPage(r):\n soup = BeautifulSoup(r.text, 'lxml')\n head = soup.find('h1')\n if head != None and head.string != None and (\"Index of \" in head.string):\n return \"Shows 'Index Of' page ✘\" \n else:\n return \"Displays properly ✓\"","def slurm_check(path='.'):\n for file in shrunner.get_files(path, '.out'):\n with open(file, 'r') as f:\n for line in f.read().splitlines():\n if 'issue' in line.lower():\n print(file)\n print(line)\n print('-' * 40)","def slack_channel_lookup_error(err):\n current_app.logger.exception(err)\n return err.message, 404","def report(issues, show_urls=False):\r\n # titles may have unicode in them, so we must encode everything below\r\n if show_urls:\r\n for i in issues:\r\n role = 'ghpull' if 'merged' in i else 'ghissue'\r\n print('* :%s:`%d`: %s' % (role, i['number'],\r\n i['title'].encode('utf-8')))\r\n else:\r\n for i in issues:\r\n print('* %d: %s' % (i['number'], i['title'].encode('utf-8')))","def test_index_layout(testapp):\n response = testapp.get('/', status=200)\n html = response.html\n assert 'SPACE' in html.find(\"h1\").text","def was_label_added(issue, label):\n if not label:\n return False\n\n # Optimization that does not require pulling in issue's actions.\n if any(label.lower() == l.lower() for l in issue.labels):\n return True\n\n for action in issue.actions:\n for added in action.labels.added:\n if label.lower() == added.lower():\n return True\n\n return False","def change_issues_label(self, msg, old_label, new_label):\n self._asset_bind(msg)\n yield (\"Processing....\")\n trans = self._translation_util(msg)\n client = self._github_operator(msg)\n cmd = \"repo:{} label:{} is:open type:issue\".format(\n task_repository_name(), old_label)\n issue_list = client.search_issue(cmd, 10)\n for issue in issue_list:\n trans.wait_for_limit(MAX_RESULT, MAX_RESULT)\n issue.remove_from_labels(old_label)\n issue.add_to_labels(new_label)\n yield \"{} issues has been changed label from {} to {}\".format(len(issue_list), old_label, new_label)","def test_missing_help_key(self):\n output = tags.madcap_flare_help({})\n self.assertEqual(output, 'http://example.com/Default.htm')","def test_cap_warning_in_log():\n\n log_file_path = \"mobile_testkit_tests/test_data/mock_panic_log.txt\"\n\n with pytest.raises(AssertionError) as e:\n scan_logs.scan_for_errors(['warning'], log_file_path)\n\n error_message = str(e.value)\n assert error_message.startswith(\"warning found!!\")","def disp_found(num):\n from x84.bbs import getterminal, echo\n term = getterminal()\n echo(u''.join((u'\\r',\n term.bold_white(u'%d' % (num,)),\n term.yellow(u' lOCAtiON%s diSCOVEREd ' %\n (u's' if num > 1 else u'')),\n term.bold_black(u'...'),)))","def target_found( self ):\n print( \"Solution: \" + self.path );","def check_valid_title(title):\n title_issues = TitleIssues(title_contains_nsfw=title_contains_nsfw(title))\n return title_issues","def not_found(error):\n pass","def main() -> None:\n\n gh_annotations: List[str] = []\n\n for line in sys.stdin.readlines():\n print(line, end=\"\")\n\n if \":\" not in line:\n continue\n\n try:\n file, line_no, col, error = line.strip().split(\":\", 3)\n gh_annotations.append(f\"::error file={file},line={line_no},col={col}::{error}\")\n except ValueError:\n pass\n\n for annotation in gh_annotations:\n print(annotation)\n\n sys.exit(len(gh_annotations) > 0)","def get_error_contexts(exp_tree, unknown_summary, soln_id, debug=False):\n \n if debug:\n print('get_error_contexts')\n messages = []\n \n # error_contexts: List of subgraphs limited to either neighboring\n # leaf nodes, or to immediately avaialable connecting unknowns/equations.\n error_contexts = list(nx.connected_components(\n exp_tree.subgraph([_ for _ in exp_tree if not 'mapped' in exp_tree.nodes[_]])\n ))\n \n for context_g in error_contexts:\n if debug:\n print(context_g)\n if len(context_g) == 1:\n # it's a leaf\n if is_symbol(exp_tree, list(context_g)[0]):\n # it's a var,unknown,param\n error_object = tree_report(\n nx.subgraph(exp_tree, context_g), unknown_summary, debug=debug\n )\n eq_context = get_eqbox_html(\n list(context_g)[0],\n exp_tree\n )\n messages.append(f\"{error_object} in {eq_context}\")\n else:\n #it's a constant, get root and process it\n # Get the subgraph rooted at its predecessor\n if message_text[soln_id]['status'] == False:\n if debug:\n print(\"It's a constant\")\n # Breakdown and report errors in constants\n # only when something really went wrong\n # otherwise don't bother\n messages.append(\n tree_report(\n get_rooted_subgraph(\n exp_tree,\n list(exp_tree[list(context_g)[0]])[0] #root\n ), unknown_summary, debug=debug\n )\n )\n else:\n if debug:\n print(\"It's a constant but the answer is correct\")\n continue\n \n elif len(context_g) == 2:\n # it's an edge, same logic basically after you find the leaf node\n node, root = tuple(context_g)\n if is_symbol(exp_tree, root):\n node,root = root,node\n \n if is_symbol(exp_tree, node):\n error_object = tree_report(\n nx.subgraph(exp_tree, [node]), unknown_summary, debug=debug\n )\n eq_context = get_eqbox_html(\n node,\n exp_tree,\n )\n messages.append(f\"{error_object} in {eq_context}\")\n \n else:\n messages.append(\n tree_report(\n nx.subgraph(exp_tree, context_g), unknown_summary, debug=debug\n )\n )\n \n return messages","def detect_labels_uri(uri):\n client = vision.ImageAnnotatorClient()\n image = types.Image()\n image.source.image_uri = uri\n macy_keywords = []\n #print(dir(client))\n response_label = client.label_detection(image=image)\n response_web = client.web_detection(image=image)\n labels = response_label.label_annotations\n web_annotations = response_web.web_detection\n\n #print(dir(response_web))\n #print((dir(labels)))\n print('Labels:')\n\n for label in labels:\n print (label.description)\n if label.description in clw:\n print(label)\n macy_keywords.append(label.description)\n #for annotation in web_annotations():\n #print (annotation)\n\n for web_entity in web_annotations.web_entities:\n print (web_entity.description)\n if any(word in web_entity.description.lower() for word in clw):\n print(web_entity)\n macy_keywords.append(web_entity.description.lower())\n\n print set(macy_keywords)\n get_macy_links(set(macy_keywords))","def test_navigates_to_about_page_then_index_page_then_about_page_success(w_driver):\n #1.) Navigate to about page, click link for index page\n w_driver.get('localhost:8000/about')\n\n element=w_driver.find_element_by_link_text('back to Kasner').click()\n #find index page info\n results=w_driver.page_source\n text_found1=re.search(r'Welcome to the Kasner Micro Search Engine',results)\n\n #2.) Click link for index page\n element=w_driver.find_element_by_link_text('About our team').click()\n #find about page info\n results=w_driver.page_source\n text_found2=re.search(r'About the Kasner Search Engine',results)\n\n #3.) Verify info we found is not None\n assert(text_found1 != None)\n assert(text_found2 != None)","def warnings_active(self) -> List[Error]:","async def new_label(event, gh, *args, **kwargs):\n if event.data[\"label\"][\"name\"] == TRIVIAL_LABEL:\n issue_number_found = ISSUE_RE.search(\n event.data[\"pull_request\"][\"title\"])\n if issue_number_found:\n status = create_success_status(issue_number_found)\n else:\n status = TRIVIAL_STATUS\n await _post_status(event, gh, status)","def test_about_layout(testapp):\n response = testapp.get('/about', status=200)\n html = response.html\n assert 'Chaitanya' in html.find(\"h1\").text","def warnings(self) -> List[Error]:","def test_github_repo_search_with_invalid_string(self):\n driver = self.driver\n driver.get(self.base_url)\n search_box = driver.find_element_by_name(\"q\")\n search_box.send_keys(\"**************\")\n search_box.send_keys(Keys.RETURN)\n time.sleep(2)\n assert \"search help section\" in driver.page_source","def findLabel(self, label):\n return self.root._findLabel(label)","def _find_duplicate(stacktrace: str) -> Optional[Issue]:\n for issue in TAGBOT_ISSUES_REPO.get_issues(state=\"all\"):\n m = re.search(\"(?s)```py\\n(.*)\\n```\", issue.body)\n if not m:\n continue\n if _is_duplicate(stacktrace, m[1]):\n return issue\n return None","def list_unique_problems(arn=None, nextToken=None):\n pass","def get_cm_problems(cm:np.ndarray, labels: List[str]) -> None:\n\n n = len(cm)\n\n # Find classes which are not present in the dataset\n for i in range(n):\n if sum(cm[i]) == 0:\n logger.info(\"The class '%s' was not in the dataset.\" % labels[i])\n\n # Find classes which are never predicted\n cm = cm.transpose()\n never_predicted = []\n for i in range(n):\n if sum(cm[i]) == 0:\n never_predicted.append(labels[i])\n \n if len(never_predicted) > 0:\n logger.info(tabulate(list(map(lambda x:[x], never_predicted)),\n tablefmt='psql', \n headers=[\"WARNING - The following classes were never predicted:\"], \n showindex=False)\n )","def test_severity_list_ok(self):\n test_name = sys._getframe().f_code.co_name\n rv, output = self._execute('severity list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)","def detect_fatal_errors(self):\n for instance in self.all_instances:\n instance.detect_fatal_errors()","def search_exceptions(self):\n templates = 'cfgov/jinja2/v1'\n return [\n templates + '/_defaults',\n templates + '/_lib',\n templates + '/_queries',\n templates + '/_settings',\n 'test',\n 'config'\n ]","def label_rule_for_title(text: str):\n return re.findall(LABEL_SPECIFICATION[\"RE_TITLE\"], text)","def test_label_not_in_config(self):\n with self.assertRaisesRegex(\n ValueError, 'The config \\'Label\\' field should contain the positive'\n ' class label.'):\n self.ci.run_with_metadata(\n indexed_inputs=self.dataset.indexed_examples,\n model=self.model,\n dataset=self.dataset,\n )","def problem_list(self):\r\n return self.q(css='a.problem-button').text","def __get_word_window_one_word_help(self, pattern, tokens, constraints):\n textsnippets = []\n textlength = len(tokens)\n for ind, token in enumerate(tokens):\n if check_pattern(pattern, token):\n if constraints is not None:\n self.__check_constraints(constraints, (ind, ind), ind, pattern, None, None, textsnippets, tokens)\n else:\n self.__get_word_window_help((ind, ind), textsnippets, textlength, tokens)\n return textsnippets","def verify_labeled(self, d_stmt, table):\n d_label = d_stmt.find_first(\"p_name\")\n if d_label:\n self.label = d_label.value\n table.check_table(d_stmt.linespan, Symbol(self.label, DanaType(\"label\")))"],"string":"[\n \"def test_issue_get_label(self):\\n pass\",\n \"def test_issue_get_labels(self):\\n pass\",\n \"def test_issue_list_labels(self):\\n pass\",\n \"def test_issue_add_label(self):\\n pass\",\n \"def test_issue_replace_labels(self):\\n pass\",\n \"def test_issue_create_label(self):\\n pass\",\n \"def _find_errors_in_page(self, response):\\n if response.status_code == 403:\\n return \\\"Could not check for errors, as response was a 403 response\\\\\\n forbidden. User asking for this url did not have permission.\\\"\\n \\n \\n errors = re.search('<ul class=\\\"errorlist\\\">(.*)</ul>', \\n response.content, \\n re.IGNORECASE)\\n\\n if errors: \\n #show a little around the actual error to scan for variables that\\n # might have caused it\\n span = errors.span()\\n wide_start = max(span[0]-200,0)\\n wide_end = min(span[1]+200,len(response.content)) \\n wide_error = response.content[wide_start:wide_end]\\n return wide_error\\n \\n return \\\"\\\"\",\n \"def label_milestone_issues():\\n session = GithubSession()\\n\\n labels = list(session.get_labels())\\n labels_by_name = dict([(x['name'], x) for x in labels])\\n\\n milestones = list(session.get_milestones())\\n\\n for milestone in milestones:\\n label_data = labels_by_name[f'epic:{milestone[\\\"title\\\"].strip()}']\\n\\n for issue in session.get_issues(milestone=milestone[\\\"number\\\"], state='all'):\\n session.add_label(label_data, number=issue['number'])\",\n \"def test_issue_remove_label(self):\\n pass\",\n \"def test_labels(self):\\n self.compliance_tester.test_labels(self.oi)\",\n \"def test_label_indices_unknown():\\n with pytest.raises(ValueError):\\n util.get_label_indices(3, \\\"random_str\\\")\",\n \"def test_issue_edit_label(self):\\n pass\",\n \"def get_labels():\\n return if_found(dao.get_labels())\",\n \"def _FindLabels(self):\\n texs = \\\" \\\".join(glob.glob(\\\"*.tex\\\"))\\n cat_process = subprocess.Popen(shlex.split(\\\"cat %s\\\" % texs),\\n stdout=subprocess.PIPE)\\n grep_process = subprocess.Popen(shlex.split(r\\\"grep \\\\\\\\\\\\\\\\label\\\"),\\n stdin=cat_process.stdout,\\n stdout=subprocess.PIPE)\\n cat_process.stdout.close()\\n\\n lines = grep_process.communicate()[0]\\n\\n ret = []\\n for label in lines.split(\\\"\\\\n\\\"):\\n ret.append(responses.BuildCompletionData(\\n re.sub(r\\\".*\\\\label{(.*)}.*\\\", r\\\"\\\\1\\\", label)\\n )\\n )\\n\\n return ret\",\n \"def test_labels(ruler: SpaczzRuler) -> None:\\n assert all(\\n [label in ruler.labels for label in [\\\"GPE\\\", \\\"STREET\\\", \\\"DRUG\\\", \\\"NAME\\\", \\\"BAND\\\"]]\\n )\\n assert len(ruler.labels) == 5\",\n \"def labels_list(issue):\\n return [x['name'] for x in issue['labels']]\",\n \"def labels(self):\\r\\n return labels.IssueLabels(self)\",\n \"def labels_validation(ele,actultext):\\r\\n msg, status = \\\"\\\", True\\r\\n\\r\\n try:\\r\\n 'Here we will pass objectidentifier for required element'\\r\\n text_heading = ui_controls.text_view(get_obj_identifier(ele))\\r\\n print \\\"Current label returned by UI is==>\\\"+str(text_heading)\\r\\n\\r\\n 'To check if it is returning empty value here'\\r\\n if not text_heading:\\r\\n print str(actultext)+\\\" label does not exist and it is returning empty value.\\\"\\r\\n return False, msg\\r\\n\\r\\n 'Comparing text retrieved from UI with validation text'\\r\\n if actultext.strip() == text_heading.strip():\\r\\n print (str(actultext)+\\\" label has been found!!!\\\")\\r\\n else:\\r\\n print(\\\"Sorry!!!lable has been mismatched,it should be \\\"+str(actultext))\\r\\n print (\\\"label shown at UI is==>\\\"+str(text_heading))\\r\\n return False, msg \\r\\n\\r\\n except Exception as excp:\\r\\n traceback.print_exc()\\r\\n msg += str(excp)\\r\\n status = False\\r\\n return status, msg\",\n \"def test_issue_search_issues(self):\\n pass\",\n \"def user_labels_find(*args):\\n return _ida_hexrays.user_labels_find(*args)\",\n \"def test_welcome_exploration(self):\\n self.init_player(\\n '0', 'Welcome to Oppia!', 'do you know where the name \\\\'Oppia\\\\'')\\n self.submit_and_compare(\\n '0', 'Yes!', 'In fact, the word Oppia means \\\\'learn\\\\'.')\\n self.submit_and_compare('Finish', 'Check your spelling!', '')\\n self.submit_and_compare(\\n 'Finnish', 'Yes! Oppia is the Finnish word for learn.',\\n 'What is the value of')\",\n \"def labels(self):\\n raise WTFError(\\\"is this your first day?\\\")\",\n \"def get_label(repo, title, verbose=None):\\n if verbose:\\n print \\\"Checking for label...\\\"\\n label = None\\n label_text = None\\n try:\\n label_start = 1 + title.index('(')\\n label_end = title.index(')')\\n label_text = title[label_start:label_end]\\n except ValueError, e:\\n print \\\"Warning: This tile has no embeded label. {0}\\\".format(e)\\n if label_text:\\n try:\\n label = [repo.get_label(label_text)]\\n if verbose:\\n print \\\"Found label: {0}\\\".format(label)\\n except UnknownObjectException, e:\\n print \\\"Error: The label '{0}' does not exist on \\\" \\\\\\n \\\"Github. {1}\\\".format(label_text, e)\\n return label\",\n \"def disp_notfound():\\n from x84.bbs import getsession, getterminal, echo, getch\\n term = getterminal()\\n echo(u''.join((u'\\\\r\\\\n\\\\r\\\\n',\\n term.bold(u'bAd REQUESt'),\\n term.bold_red(' -/- '),\\n term.bold('NOt fOUNd.',),)))\\n if not getsession().user.get('expert', False):\\n getch(1.7)\",\n \"def label(cls) -> str:\\n return \\\"!lobotomy.error\\\"\",\n \"def label(self, name):\\r\\n return labels.IssueLabel(self, name)\",\n \"def getissue(s,refconvdf,issuetaglist):\\r\\n #check if empty\\r\\n taglist=gettotaltags(s,refconvdf)\\r\\n issuename=list(set(issuetaglist.name.values).intersection(taglist)) \\r\\n if not issuename:\\r\\n return 'None'\\r\\n else:\\r\\n return issuename\",\n \"def test_issue_clear_labels(self):\\n pass\",\n \"def get_fatal_alerts(self, path):\",\n \"def pull_labels(self, org):\\n pass\",\n \"def label_messages(self, org, messages, label):\\n pass\",\n \"def auto_label_text_chunk(text, labels):\\n\\n for label in labels:\\n regex_str = \\\"|\\\".join(labels[label])\\n r = re.compile(regex_str, flags=re.I)\\n if re.search(r, text) is not None:\\n return label\\n\\n return \\\"None\\\"\",\n \"def milestone_labels(argv=None):\\n argv = argv or sys.argv[1:]\\n\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument('color', help='color to make the labels')\\n\\n args = parser.parse_args(argv)\\n\\n session = GithubSession()\\n\\n labels = session.get_labels()\\n\\n labels_by_name = dict([(label['name'], label) for label in labels])\\n\\n for milestone in session.get_milestones():\\n label_name = f'epic:{milestone[\\\"title\\\"]}'\\n\\n if label_name in labels_by_name:\\n continue\\n\\n labels_by_name[label_name] = session.create_label(label_name, args.color)\\n\\n return labels_by_name\",\n \"def _search_issues(self, summary):\\n try:\\n issues = self.jira.get_issues_by_summary(summary)\\n except Exception as e:\\n logging.error(\\\"Failed searching issues: \\\"+ str(e))\\n return []\\n return issues\",\n \"def missing_entry_error(entry, name, yml):\\n\\n yml = symlink_target(yml)\\n output_1 = path(yml) + '\\\\n'\\n output_2 = colored(' - Error: Missing ', 'red')\\n output_3 = colored(str(entry), attrs=['bold'])\\n output_4 = colored(' in ', 'red')\\n output_5 = colored(str(name), attrs=['bold'])\\n return output_1 + output_2 + output_3 + output_4 + output_5\",\n \"def label(cls) -> str:\\n return \\\"!lobotomy.unknown\\\"\",\n \"def test_label_returns_not_found_without_query(client):\\n response = client.get(\\n reverse('name:label', args=['']))\\n assert 404 == response.status_code\\n assert b'No matching term found' not in response.content\",\n \"def display_for_triage(bugs):\\n # bug title is like:\\n # '\\n # Bug #1724025 in openstack-ansible:\\n # invalid regular expression...\\\"\\n # '\\n for bug in bugs:\\n bug_name = u\\\"\\\".join(bug.title.split(\\\":\\\")[1:])\\n print(u\\\"#link {link}\\\\n\\\\t{name}\\\".format(link=bug.web_link, name=bug_name))\",\n \"def test_issue_delete_label(self):\\n pass\",\n \"def test_label_returns_not_found_with_query(client):\\n response = client.get(\\n reverse('name:label', args=['&&&&&&&&']))\\n assert 404 == response.status_code\\n assert b'No matching term found' in response.content\",\n \"def get_label(issue_id):\\n auth = subprocess.check_output(\\n \\\"gsutil cat\\\"\\n \\\" gs://bazel-trusted-encrypted-secrets/github-trusted-token.enc |\\\"\\n \\\" gcloud kms decrypt --project bazel-public --location global\\\"\\n \\\" --keyring buildkite --key github-trusted-token --ciphertext-file\\\"\\n \\\" - --plaintext-file -\\\", shell=True\\n ).decode(\\\"utf-8\\\").strip().split(\\\"\\\\n\\\")[0]\\n headers = {\\n \\\"Authorization\\\": \\\"Bearer \\\" + auth,\\n \\\"Accept\\\": \\\"application/vnd.github+json\\\",\\n }\\n response = requests.get(\\n \\\"https://api.github.com/repos/bazelbuild/bazel/issues/\\\"\\n + issue_id + \\\"/labels\\\", headers=headers,\\n )\\n for item in response.json():\\n for key, value in item.items():\\n if key == \\\"name\\\" and \\\"team-\\\" in value:\\n return value.strip()\\n return None\",\n \"def test_issue_list_issues(self):\\n pass\",\n \"def get_label(self, name):\\n label_list = self.wls_board.get_labels()\\n for label in label_list:\\n if name in label.name: \\n return label\",\n \"def mistake_not_found():\\n win6 = Toplevel(root)\\n win6.title(\\\"Ошибка\\\")\\n win6.geometry('270x40')\\n lbl13 = Label(win6, text=\\\"Значение не найдено в базе данных!\\\", width=45, height=2)\\n lbl13.place(x=-30, y=0)\",\n \"def command_show(problem):\\r\\n print problem.get_html()\",\n \"def verify_homeScreen():\\r\\n msg = \\\"\\\"\\r\\n try:\\r\\n 'Getting home page label for IOS - Open Manage Essentials'\\r\\n if g.platform == 'ios':\\r\\n text_view = ui_controls.text_view(get_obj_identifier('home_OME_lbl'), value=True)\\r\\n text_toverify = g.ios_homeScreen_label\\r\\n else:\\r\\n sleep(3)\\r\\n text_view = ui_controls.text_view(get_obj_identifier('home_OME_lbl'))\\r\\n text_toverify = g.android_homeScreen_label\\r\\n\\r\\n 'Verifying whether home screen label is Open Manage Essentials for IOS'\\r\\n if text_view.strip() == text_toverify :\\r\\n print \\\"Home Screen label is verified successfully\\\"\\r\\n else:\\r\\n print \\\"Home Screen label is not verified successfully\\\"\\r\\n return False, msg\\r\\n except Exception as excp:\\r\\n traceback.print_exc()\\r\\n msg += str(excp)\\r\\n return True, msg\",\n \"def check_feedback(self, step):\\r\\n world.wait_for_visible(self.active_problem_selector('.tag-status.correct'))\\r\\n assert_equals(len(world.css_find(self.active_problem_selector('.tag-status.correct'))), 1)\\r\\n assert_equals(len(world.css_find(self.active_problem_selector('.show'))), 1)\",\n \"def errors_fatal(self) -> List[Error]:\",\n \"def check_happiness(content):\\n baseline = 100\\n happiness = [\\\"Smile\\\"]\\n label = 5\\n # print content\\n emotion_time = content[0][1]\\n # print 'emotion_time',emotion_time\\n for c in content:\\n for h in happiness:\\n # print h\\n if c[0] == h and c[1] >= baseline:\\n print 'emotion & label',emotion_time, label\\n return emotion_time, label\",\n \"def verify_aboutScreen():\\r\\n msg = \\\"\\\"\\r\\n try:\\r\\n 'Getting about page label for IOS - About'\\r\\n if g.platform == 'ios':\\r\\n text_view = ui_controls.text_view(get_obj_identifier('mainMenu_about_lbl'), value=True)\\r\\n else:\\r\\n text_view = ui_controls.text_view(get_obj_identifier('mainMenu_about_lbl'))\\r\\n\\r\\n 'Verifying whether about screen label is About for IOS'\\r\\n if text_view.strip() == g.aboutScreen_label :\\r\\n print \\\"About Screen label is verified successfully\\\"\\r\\n else:\\r\\n print \\\"About Screen label is not verified successfully\\\"\\r\\n return False, msg\\r\\n except Exception as excp:\\r\\n traceback.print_exc()\\r\\n msg += str(excp)\\r\\n return True, msg\",\n \"def welcome_users(identities):\\n welcome_message = 'Hello'\\n is_recognized_file = open('varThread\\\\\\\\recognized.check', 'w')\\n is_recognized_file.write('1')\\n is_recognized_file.close()\\n if len(identities) == 1:\\n welcome_message += '%s!' % identities[0]\\n print(welcome_message)\\n else:\\n for identity_id in range(len(identities)-1):\\n welcome_message += '%s, ' % identities[identity_id]\\n welcome_message += 'and %s, ' % identities[-1]\\n welcome_message += '!'\\n print(welcome_message)\",\n \"def check_happiness(content):\\n baseline = 50\\n happiness = [\\\"Smile\\\"]\\n label = 5\\n # print content\\n emotion_time = content[0][1]\\n # print 'emotion_time',emotion_time\\n for c in content:\\n for h in happiness:\\n # print h\\n if c[0] == h and c[1] >= baseline:\\n print 'emotion & label',emotion_time, label\\n return emotion_time, label\",\n \"def find_mistake(self):\\n if self.solve_stats[-1][\\\"comment\\\"] == \\\"\\\":\\n for stat in reversed(self.solve_stats):\\n if stat[\\\"comment\\\"] != \\\"\\\":\\n self.solve_stats[stat[\\\"count\\\"]][\\\"comment\\\"] += \\\"mistake from here\\\"\\n break\",\n \"def test_warning_in_log():\\n\\n log_file_path = \\\"mobile_testkit_tests/test_data/mock_panic_log.txt\\\"\\n\\n with pytest.raises(AssertionError) as e:\\n scan_logs.scan_for_errors(['warning'], log_file_path)\\n\\n error_message = str(e.value)\\n assert error_message.startswith(\\\"warning found!!\\\")\",\n \"def test_navigates_to_index_page_about_page(w_driver):\\n #Index Page\\n w_driver.get('localhost:8000')\\n results=w_driver.page_source\\n text_found1=re.search(r'Welcome to the Kasner Micro Search Engine', results)\\n\\n #About Page\\n w_driver.get('localhost:8000/about')\\n results=w_driver.page_source\\n text_found2=re.search(r'About the Kasner Search Engine',results)\\n \\n assert(text_found1 != None and text_found2 != None)\",\n \"def test_text_found_in_single_slide(collected_seg_motif):\\n slide_not_found, error_records_future, error_records_seg, skip_test = collected_seg_motif\\n if skip_test:\\n pytest.skip(\\\"File does not have nti_data\\\")\\n #print(\\\"error_records_future : \\\", error_records_future)\\n print(\\\"error_records_seg : \\\", error_records_seg)\\n if len(error_records_seg) > 0:\\n print(\\\"error count : \\\", len(error_records_seg))\\n print(\\\"error slides : \\\", (error_records_seg))\\n pytest.fail(\\n \\\"errors found. Count of missing rem slides in segments %s , count of remediation slides in nore than 1 slide %s\\\"\\n % (slide_not_found, error_records_seg))\",\n \"def projects_label(args):\\n session = GithubSession()\\n\\n label_datas = list(session.get_labels())\\n\\n team = args.team\\n team_label_data = None\\n if team:\\n team_label = utils.get_label(team, prefix=\\\"team\\\")\\n team_label_data = [x for x in label_datas if x[\\\"name\\\"] == team_label][0]\\n\\n # get the project label\\n project_label = utils.get_label(args.name, prefix=\\\"project\\\")\\n project_label_data = [x for x in label_datas if x[\\\"name\\\"] == project_label][0]\\n\\n print(f\\\"label cards in project {args.name} column {args.column}\\\")\\n\\n project_board = session.get_project(args.name)\\n project_backlog_grooming = session.get_column(project_board, \\\"backlog grooming\\\")\\n\\n cards = list(session.get_cards(project_backlog_grooming))\\n for card_data in cards:\\n issue_number = utils.get_issue_number_from_card_data(card_data)\\n\\n print(issue_number)\\n\\n # add the project label\\n session.add_label(project_label_data, number=issue_number)\\n\\n if team_label_data:\\n session.add_label(team_label_data, number=issue_number)\",\n \"def search_for_warnings(self):\\n log = str()\\n print(self.default_starter_args + self.arguments)\\n if not self.log_file.exists():\\n print(str(self.log_file) + \\\" not there. Skipping search\\\")\\n return\\n print(str(self.log_file))\\n with self.log_file.open(errors=\\\"backslashreplace\\\") as log_f:\\n for line in log_f.readline():\\n if \\\"WARN\\\" in line or \\\"ERROR\\\" in line:\\n print(line.rstrip())\\n log += line.rstrip()\\n attach(log, \\\"WARN or ERROR lines from starter log\\\")\",\n \"def test_labels_warning(self):\\n with pytest.warns(\\n YellowbrickWarning, match=\\\"both classes and encoder specified\\\"\\n ):\\n oz = ClassificationScoreVisualizer(\\n GaussianNB(),\\n classes=[\\\"a\\\", \\\"b\\\", \\\"c\\\"],\\n encoder={0: \\\"foo\\\", 1: \\\"bar\\\", 2: \\\"zap\\\"},\\n )\\n labels = oz._labels()\\n npt.assert_array_equal(labels, [\\\"foo\\\", \\\"bar\\\", \\\"zap\\\"])\",\n \"def test_welcome_exploration(self):\\n player = self.ExplorationPlayer(self.testapp, self.assertEqual, '0')\\n\\n response = player.get_initial_response()\\n self.assertIn(\\n 'do you know where the name \\\\'Oppia\\\\'', response['oppia_html'])\\n self.assertEqual(response['title'], 'Welcome to Oppia!')\\n\\n response = player.submit_answer('0')\\n self.assertIn(\\n 'In fact, the word Oppia means \\\\'learn\\\\'.', response['oppia_html'])\",\n \"async def issues(self, ctx):\\n await ctx.message.delete()\\n await ctx.send(\\\"Issue tracker: https://github.com/TheSuperGamer20578/Sudan-bot/issues\\\")\",\n \"def details_not_matching():\\n print(\\\"login details don't match.\\\")\",\n \"def main():\\n homepage_to_prefixes = defaultdict(set)\\n for prefix in bioregistry.read_registry():\\n if bioregistry.is_deprecated(prefix):\\n continue\\n homepage = bioregistry.get_homepage(prefix)\\n if homepage is None:\\n continue\\n homepage_to_prefixes[homepage].add(prefix)\\n\\n rv = thread_map(_process, list(homepage_to_prefixes.items()), desc=\\\"Checking homepages\\\")\\n\\n failed = sum(failed for _, _, failed, _ in rv)\\n click.secho(\\n f\\\"{failed}/{len(rv)} ({failed / len(rv):.2%}) homepages failed to load\\\", fg=\\\"red\\\", bold=True\\n )\\n\\n df = pd.DataFrame(\\n columns=[\\\"prefix\\\", \\\"homepage\\\", \\\"message\\\"],\\n data=[\\n (prefix, homepage, msg)\\n for homepage, prefixes, failed, msg in rv\\n if failed\\n for prefix in prefixes\\n ],\\n )\\n click.echo(df.to_markdown())\\n sys.exit(1 if 0 < failed else 0)\",\n \"def get_known_words(label_matches):\\r\\n\\tknown_words = set()\\r\\n\\tfor key in label_matches.keys():\\r\\n\\t\\tif key[0] not in known_words:\\r\\n\\t\\t\\tknown_words.add(key[0])\\r\\n\\treturn known_words\",\n \"def verify_diagnostics_and_usage_screen_name():\\r\\n msg = \\\"\\\"\\r\\n try:\\r\\n sleep(10)\\r\\n if g.platform == 'android':\\r\\n\\r\\n 'Verify screen name of diagnostics and usage page'\\r\\n sleep(3)\\r\\n \\r\\n flag1,msg = element_textvalidation('Diagnostics_usage_lbl','Diagnostics and Usage')\\r\\n \\r\\n \\r\\n flag = False if not (flag1) else True\\r\\n \\r\\n else:\\r\\n\\r\\n 'Verify screen name of diagnostics and usage label'\\r\\n \\r\\n flag1,msg = label_textvalidation('Diagnostics_usage_lbl','Diagnostics and Usage')\\r\\n \\r\\n\\r\\n flag = False if not (flag1) else True\\r\\n if not flag:\\r\\n return False, msg\\r\\n else:\\r\\n print \\\"License agreement screen name is displayed properly\\\"\\r\\n except Exception as excp:\\r\\n traceback.print_exc()\\r\\n msg += str(excp)\\r\\n return False, msg\\r\\n return True, msg\",\n \"def ping_leads_of_idle_issues():\\n for repo_id in github_api.get_all_repos():\\n issues = github_api.get_issues_with_label(repo_id, \\\"idle\\\")\\n if not issues:\\n continue\\n\\n idle_issue_numbers = [(i[\\\"number\\\"], i[\\\"html_url\\\"]) for i in issues]\\n\\n message = ('The following issue(s) in <{repo_url}|{repo}> are '\\n 'idle: {issue_links}. (cc: {leads})').format(\\n repo_url=\\\"https://github.com/{}/{}\\\".format(repo_id.owner, repo_id.name),\\n repo=\\\"{}/{}\\\".format(repo_id.owner, repo_id.name),\\n issue_links=\\\", \\\".join(\\n \\\"<{link}|#{number}>\\\".format(link=link, number=number)\\n for (number, link) in idle_issue_numbers),\\n leads=\\\", \\\".join(github_api.get_leads_for_repo(repo_id)))\\n \\n slack_api.send_message(message)\",\n \"def test_headlines_predefined(self) -> None:\\n for headline in self.report.headlines:\\n if not self.rules.get_headline_rules(headline.name):\\n headlines = [headline.name for headline in self.rules.headlines]\\n suggestion, _ = process.extractOne(\\n headline.name, headlines, scorer=fuzz.partial_ratio\\n )\\n self.add_error(\\n f\\\"{headline.name} är inte en valid rubrik. \\\"\\n f\\\"Rättningsförlsag: {suggestion}.\\\",\\n headline=headline,\\n )\\n elif re.search(\\\"\\\\\\\\W{1,}\\\", headline.name, re.I):\\n self.add_error(\\n f\\\"Rubriken {headline.name} innehåller tecken som inte är \\\"\\n \\\"alfanumeriska vilket inte är tillåtet för en rubrik.\\\",\\n headline=headline,\\n )\",\n \"def checkForIndexPage(r):\\n soup = BeautifulSoup(r.text, 'lxml')\\n head = soup.find('h1')\\n if head != None and head.string != None and (\\\"Index of \\\" in head.string):\\n return \\\"Shows 'Index Of' page ✘\\\" \\n else:\\n return \\\"Displays properly ✓\\\"\",\n \"def slurm_check(path='.'):\\n for file in shrunner.get_files(path, '.out'):\\n with open(file, 'r') as f:\\n for line in f.read().splitlines():\\n if 'issue' in line.lower():\\n print(file)\\n print(line)\\n print('-' * 40)\",\n \"def slack_channel_lookup_error(err):\\n current_app.logger.exception(err)\\n return err.message, 404\",\n \"def report(issues, show_urls=False):\\r\\n # titles may have unicode in them, so we must encode everything below\\r\\n if show_urls:\\r\\n for i in issues:\\r\\n role = 'ghpull' if 'merged' in i else 'ghissue'\\r\\n print('* :%s:`%d`: %s' % (role, i['number'],\\r\\n i['title'].encode('utf-8')))\\r\\n else:\\r\\n for i in issues:\\r\\n print('* %d: %s' % (i['number'], i['title'].encode('utf-8')))\",\n \"def test_index_layout(testapp):\\n response = testapp.get('/', status=200)\\n html = response.html\\n assert 'SPACE' in html.find(\\\"h1\\\").text\",\n \"def was_label_added(issue, label):\\n if not label:\\n return False\\n\\n # Optimization that does not require pulling in issue's actions.\\n if any(label.lower() == l.lower() for l in issue.labels):\\n return True\\n\\n for action in issue.actions:\\n for added in action.labels.added:\\n if label.lower() == added.lower():\\n return True\\n\\n return False\",\n \"def change_issues_label(self, msg, old_label, new_label):\\n self._asset_bind(msg)\\n yield (\\\"Processing....\\\")\\n trans = self._translation_util(msg)\\n client = self._github_operator(msg)\\n cmd = \\\"repo:{} label:{} is:open type:issue\\\".format(\\n task_repository_name(), old_label)\\n issue_list = client.search_issue(cmd, 10)\\n for issue in issue_list:\\n trans.wait_for_limit(MAX_RESULT, MAX_RESULT)\\n issue.remove_from_labels(old_label)\\n issue.add_to_labels(new_label)\\n yield \\\"{} issues has been changed label from {} to {}\\\".format(len(issue_list), old_label, new_label)\",\n \"def test_missing_help_key(self):\\n output = tags.madcap_flare_help({})\\n self.assertEqual(output, 'http://example.com/Default.htm')\",\n \"def test_cap_warning_in_log():\\n\\n log_file_path = \\\"mobile_testkit_tests/test_data/mock_panic_log.txt\\\"\\n\\n with pytest.raises(AssertionError) as e:\\n scan_logs.scan_for_errors(['warning'], log_file_path)\\n\\n error_message = str(e.value)\\n assert error_message.startswith(\\\"warning found!!\\\")\",\n \"def disp_found(num):\\n from x84.bbs import getterminal, echo\\n term = getterminal()\\n echo(u''.join((u'\\\\r',\\n term.bold_white(u'%d' % (num,)),\\n term.yellow(u' lOCAtiON%s diSCOVEREd ' %\\n (u's' if num > 1 else u'')),\\n term.bold_black(u'...'),)))\",\n \"def target_found( self ):\\n print( \\\"Solution: \\\" + self.path );\",\n \"def check_valid_title(title):\\n title_issues = TitleIssues(title_contains_nsfw=title_contains_nsfw(title))\\n return title_issues\",\n \"def not_found(error):\\n pass\",\n \"def main() -> None:\\n\\n gh_annotations: List[str] = []\\n\\n for line in sys.stdin.readlines():\\n print(line, end=\\\"\\\")\\n\\n if \\\":\\\" not in line:\\n continue\\n\\n try:\\n file, line_no, col, error = line.strip().split(\\\":\\\", 3)\\n gh_annotations.append(f\\\"::error file={file},line={line_no},col={col}::{error}\\\")\\n except ValueError:\\n pass\\n\\n for annotation in gh_annotations:\\n print(annotation)\\n\\n sys.exit(len(gh_annotations) > 0)\",\n \"def get_error_contexts(exp_tree, unknown_summary, soln_id, debug=False):\\n \\n if debug:\\n print('get_error_contexts')\\n messages = []\\n \\n # error_contexts: List of subgraphs limited to either neighboring\\n # leaf nodes, or to immediately avaialable connecting unknowns/equations.\\n error_contexts = list(nx.connected_components(\\n exp_tree.subgraph([_ for _ in exp_tree if not 'mapped' in exp_tree.nodes[_]])\\n ))\\n \\n for context_g in error_contexts:\\n if debug:\\n print(context_g)\\n if len(context_g) == 1:\\n # it's a leaf\\n if is_symbol(exp_tree, list(context_g)[0]):\\n # it's a var,unknown,param\\n error_object = tree_report(\\n nx.subgraph(exp_tree, context_g), unknown_summary, debug=debug\\n )\\n eq_context = get_eqbox_html(\\n list(context_g)[0],\\n exp_tree\\n )\\n messages.append(f\\\"{error_object} in {eq_context}\\\")\\n else:\\n #it's a constant, get root and process it\\n # Get the subgraph rooted at its predecessor\\n if message_text[soln_id]['status'] == False:\\n if debug:\\n print(\\\"It's a constant\\\")\\n # Breakdown and report errors in constants\\n # only when something really went wrong\\n # otherwise don't bother\\n messages.append(\\n tree_report(\\n get_rooted_subgraph(\\n exp_tree,\\n list(exp_tree[list(context_g)[0]])[0] #root\\n ), unknown_summary, debug=debug\\n )\\n )\\n else:\\n if debug:\\n print(\\\"It's a constant but the answer is correct\\\")\\n continue\\n \\n elif len(context_g) == 2:\\n # it's an edge, same logic basically after you find the leaf node\\n node, root = tuple(context_g)\\n if is_symbol(exp_tree, root):\\n node,root = root,node\\n \\n if is_symbol(exp_tree, node):\\n error_object = tree_report(\\n nx.subgraph(exp_tree, [node]), unknown_summary, debug=debug\\n )\\n eq_context = get_eqbox_html(\\n node,\\n exp_tree,\\n )\\n messages.append(f\\\"{error_object} in {eq_context}\\\")\\n \\n else:\\n messages.append(\\n tree_report(\\n nx.subgraph(exp_tree, context_g), unknown_summary, debug=debug\\n )\\n )\\n \\n return messages\",\n \"def detect_labels_uri(uri):\\n client = vision.ImageAnnotatorClient()\\n image = types.Image()\\n image.source.image_uri = uri\\n macy_keywords = []\\n #print(dir(client))\\n response_label = client.label_detection(image=image)\\n response_web = client.web_detection(image=image)\\n labels = response_label.label_annotations\\n web_annotations = response_web.web_detection\\n\\n #print(dir(response_web))\\n #print((dir(labels)))\\n print('Labels:')\\n\\n for label in labels:\\n print (label.description)\\n if label.description in clw:\\n print(label)\\n macy_keywords.append(label.description)\\n #for annotation in web_annotations():\\n #print (annotation)\\n\\n for web_entity in web_annotations.web_entities:\\n print (web_entity.description)\\n if any(word in web_entity.description.lower() for word in clw):\\n print(web_entity)\\n macy_keywords.append(web_entity.description.lower())\\n\\n print set(macy_keywords)\\n get_macy_links(set(macy_keywords))\",\n \"def test_navigates_to_about_page_then_index_page_then_about_page_success(w_driver):\\n #1.) Navigate to about page, click link for index page\\n w_driver.get('localhost:8000/about')\\n\\n element=w_driver.find_element_by_link_text('back to Kasner').click()\\n #find index page info\\n results=w_driver.page_source\\n text_found1=re.search(r'Welcome to the Kasner Micro Search Engine',results)\\n\\n #2.) Click link for index page\\n element=w_driver.find_element_by_link_text('About our team').click()\\n #find about page info\\n results=w_driver.page_source\\n text_found2=re.search(r'About the Kasner Search Engine',results)\\n\\n #3.) Verify info we found is not None\\n assert(text_found1 != None)\\n assert(text_found2 != None)\",\n \"def warnings_active(self) -> List[Error]:\",\n \"async def new_label(event, gh, *args, **kwargs):\\n if event.data[\\\"label\\\"][\\\"name\\\"] == TRIVIAL_LABEL:\\n issue_number_found = ISSUE_RE.search(\\n event.data[\\\"pull_request\\\"][\\\"title\\\"])\\n if issue_number_found:\\n status = create_success_status(issue_number_found)\\n else:\\n status = TRIVIAL_STATUS\\n await _post_status(event, gh, status)\",\n \"def test_about_layout(testapp):\\n response = testapp.get('/about', status=200)\\n html = response.html\\n assert 'Chaitanya' in html.find(\\\"h1\\\").text\",\n \"def warnings(self) -> List[Error]:\",\n \"def test_github_repo_search_with_invalid_string(self):\\n driver = self.driver\\n driver.get(self.base_url)\\n search_box = driver.find_element_by_name(\\\"q\\\")\\n search_box.send_keys(\\\"**************\\\")\\n search_box.send_keys(Keys.RETURN)\\n time.sleep(2)\\n assert \\\"search help section\\\" in driver.page_source\",\n \"def findLabel(self, label):\\n return self.root._findLabel(label)\",\n \"def _find_duplicate(stacktrace: str) -> Optional[Issue]:\\n for issue in TAGBOT_ISSUES_REPO.get_issues(state=\\\"all\\\"):\\n m = re.search(\\\"(?s)```py\\\\n(.*)\\\\n```\\\", issue.body)\\n if not m:\\n continue\\n if _is_duplicate(stacktrace, m[1]):\\n return issue\\n return None\",\n \"def list_unique_problems(arn=None, nextToken=None):\\n pass\",\n \"def get_cm_problems(cm:np.ndarray, labels: List[str]) -> None:\\n\\n n = len(cm)\\n\\n # Find classes which are not present in the dataset\\n for i in range(n):\\n if sum(cm[i]) == 0:\\n logger.info(\\\"The class '%s' was not in the dataset.\\\" % labels[i])\\n\\n # Find classes which are never predicted\\n cm = cm.transpose()\\n never_predicted = []\\n for i in range(n):\\n if sum(cm[i]) == 0:\\n never_predicted.append(labels[i])\\n \\n if len(never_predicted) > 0:\\n logger.info(tabulate(list(map(lambda x:[x], never_predicted)),\\n tablefmt='psql', \\n headers=[\\\"WARNING - The following classes were never predicted:\\\"], \\n showindex=False)\\n )\",\n \"def test_severity_list_ok(self):\\n test_name = sys._getframe().f_code.co_name\\n rv, output = self._execute('severity list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def detect_fatal_errors(self):\\n for instance in self.all_instances:\\n instance.detect_fatal_errors()\",\n \"def search_exceptions(self):\\n templates = 'cfgov/jinja2/v1'\\n return [\\n templates + '/_defaults',\\n templates + '/_lib',\\n templates + '/_queries',\\n templates + '/_settings',\\n 'test',\\n 'config'\\n ]\",\n \"def label_rule_for_title(text: str):\\n return re.findall(LABEL_SPECIFICATION[\\\"RE_TITLE\\\"], text)\",\n \"def test_label_not_in_config(self):\\n with self.assertRaisesRegex(\\n ValueError, 'The config \\\\'Label\\\\' field should contain the positive'\\n ' class label.'):\\n self.ci.run_with_metadata(\\n indexed_inputs=self.dataset.indexed_examples,\\n model=self.model,\\n dataset=self.dataset,\\n )\",\n \"def problem_list(self):\\r\\n return self.q(css='a.problem-button').text\",\n \"def __get_word_window_one_word_help(self, pattern, tokens, constraints):\\n textsnippets = []\\n textlength = len(tokens)\\n for ind, token in enumerate(tokens):\\n if check_pattern(pattern, token):\\n if constraints is not None:\\n self.__check_constraints(constraints, (ind, ind), ind, pattern, None, None, textsnippets, tokens)\\n else:\\n self.__get_word_window_help((ind, ind), textsnippets, textlength, tokens)\\n return textsnippets\",\n \"def verify_labeled(self, d_stmt, table):\\n d_label = d_stmt.find_first(\\\"p_name\\\")\\n if d_label:\\n self.label = d_label.value\\n table.check_table(d_stmt.linespan, Symbol(self.label, DanaType(\\\"label\\\")))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62451565","0.6179279","0.59356964","0.5735456","0.5484781","0.54358155","0.5402358","0.5395643","0.53929466","0.5289219","0.528681","0.5269851","0.52506924","0.5225221","0.5182275","0.5177378","0.5172387","0.51599","0.5157391","0.5143768","0.5141218","0.51402473","0.5134565","0.5111425","0.50957716","0.5093779","0.5063387","0.5059618","0.5028367","0.49245852","0.49210757","0.49210745","0.4917196","0.49014753","0.49004313","0.4870671","0.4867994","0.4857314","0.4855764","0.48557052","0.48541197","0.48498937","0.48334137","0.482648","0.47994325","0.4775862","0.47746894","0.47639984","0.4755888","0.47539997","0.4746253","0.4736856","0.47225454","0.47152764","0.471088","0.4704755","0.47027832","0.46998557","0.46974987","0.46909788","0.46893173","0.46849573","0.46844605","0.46764854","0.4675523","0.46693885","0.4669159","0.46650594","0.46519798","0.46474457","0.46471465","0.4644433","0.46429482","0.46361423","0.46346894","0.46338508","0.4630118","0.46299526","0.46228427","0.46214008","0.461337","0.45982784","0.45963264","0.45894802","0.45758206","0.4572078","0.45711485","0.45645577","0.4564476","0.45636246","0.45502767","0.45489153","0.45470953","0.454293","0.4541496","0.45411712","0.45411396","0.4534451","0.45344028","0.45303473","0.4525724"],"string":"[\n \"0.62451565\",\n \"0.6179279\",\n \"0.59356964\",\n \"0.5735456\",\n \"0.5484781\",\n \"0.54358155\",\n \"0.5402358\",\n \"0.5395643\",\n \"0.53929466\",\n \"0.5289219\",\n \"0.528681\",\n \"0.5269851\",\n \"0.52506924\",\n \"0.5225221\",\n \"0.5182275\",\n \"0.5177378\",\n \"0.5172387\",\n \"0.51599\",\n \"0.5157391\",\n \"0.5143768\",\n \"0.5141218\",\n \"0.51402473\",\n \"0.5134565\",\n \"0.5111425\",\n \"0.50957716\",\n \"0.5093779\",\n \"0.5063387\",\n \"0.5059618\",\n \"0.5028367\",\n \"0.49245852\",\n \"0.49210757\",\n \"0.49210745\",\n \"0.4917196\",\n \"0.49014753\",\n \"0.49004313\",\n \"0.4870671\",\n \"0.4867994\",\n \"0.4857314\",\n \"0.4855764\",\n \"0.48557052\",\n \"0.48541197\",\n \"0.48498937\",\n \"0.48334137\",\n \"0.482648\",\n \"0.47994325\",\n \"0.4775862\",\n \"0.47746894\",\n \"0.47639984\",\n \"0.4755888\",\n \"0.47539997\",\n \"0.4746253\",\n \"0.4736856\",\n \"0.47225454\",\n \"0.47152764\",\n \"0.471088\",\n \"0.4704755\",\n \"0.47027832\",\n \"0.46998557\",\n \"0.46974987\",\n \"0.46909788\",\n \"0.46893173\",\n \"0.46849573\",\n \"0.46844605\",\n \"0.46764854\",\n \"0.4675523\",\n \"0.46693885\",\n \"0.4669159\",\n \"0.46650594\",\n \"0.46519798\",\n \"0.46474457\",\n \"0.46471465\",\n \"0.4644433\",\n \"0.46429482\",\n \"0.46361423\",\n \"0.46346894\",\n \"0.46338508\",\n \"0.4630118\",\n \"0.46299526\",\n \"0.46228427\",\n \"0.46214008\",\n \"0.461337\",\n \"0.45982784\",\n \"0.45963264\",\n \"0.45894802\",\n \"0.45758206\",\n \"0.4572078\",\n \"0.45711485\",\n \"0.45645577\",\n \"0.4564476\",\n \"0.45636246\",\n \"0.45502767\",\n \"0.45489153\",\n \"0.45470953\",\n \"0.454293\",\n \"0.4541496\",\n \"0.45411712\",\n \"0.45411396\",\n \"0.4534451\",\n \"0.45344028\",\n \"0.45303473\",\n \"0.4525724\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":74588,"cells":{"query":{"kind":"string","value":"Find issues with the label \"welcome\" then replace with \"pending\" label"},"document":{"kind":"string","value":"def confirm_recent_new_issues(self, msg, hour):\n self._asset_bind(msg)\n yield (\"Processing....\")\n trans = self._translation_util(msg)\n client = self._github_operator(msg)\n cmd = \"repo:{} is:open type:issue\".format(\n task_repository_name())\n issue_list = client.search_issue(cmd, 10)\n end_time = datetime.datetime.now()\n cnt = 0\n for issue in issue_list:\n start_time = issue.created_at\n if end_time - start_time <= datetime.timedelta(hours=hour):\n trans.wait_for_limit(MAX_RESULT, MAX_RESULT)\n confirmed = False\n for label in issue.get_labels():\n if label.name == \"welcome\":\n issue.remove_from_labels(\"welcome\")\n else:\n if label.name in [\"pending\", \"translating\", \"pushed\", \"finished\"]:\n confirmed = True\n if not confirmed:\n issue.add_to_labels(\"pending\")\n cnt += 1\n yield \"{} issues confirmed.\".format(cnt)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_issue_replace_labels(self):\n pass","def change_issues_label(self, msg, old_label, new_label):\n self._asset_bind(msg)\n yield (\"Processing....\")\n trans = self._translation_util(msg)\n client = self._github_operator(msg)\n cmd = \"repo:{} label:{} is:open type:issue\".format(\n task_repository_name(), old_label)\n issue_list = client.search_issue(cmd, 10)\n for issue in issue_list:\n trans.wait_for_limit(MAX_RESULT, MAX_RESULT)\n issue.remove_from_labels(old_label)\n issue.add_to_labels(new_label)\n yield \"{} issues has been changed label from {} to {}\".format(len(issue_list), old_label, new_label)","def test_issue_remove_label(self):\n pass","async def new_label(event, gh, *args, **kwargs):\n if event.data[\"label\"][\"name\"] == TRIVIAL_LABEL:\n issue_number_found = ISSUE_RE.search(\n event.data[\"pull_request\"][\"title\"])\n if issue_number_found:\n status = create_success_status(issue_number_found)\n else:\n status = TRIVIAL_STATUS\n await _post_status(event, gh, status)","def test_issue_add_label(self):\n pass","def patch_issue_labels(repo, project, issue_number):\n add_label_ids = request.json.get('addLabelIds') or []\n remove_label_ids = request.json.get('removeLabelIds') or []\n\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\n\n current_label_ids = dao.get_issue_label_ids(issue_path)\n\n revised_label_ids = [label_id for label_id in current_label_ids + add_label_ids\n if label_id not in remove_label_ids]\n\n dao.set_issue_label_ids(issue_path, revised_label_ids)\n\n return if_found(dao.get_issue_labels(issue_path))","def test_issue_edit_label(self):\n pass","def post_issue_labels(repo, project, issue_number):\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\n\n new_label_ids = request.json\n\n current_label_ids = dao.get_issue_label_ids(issue_path)\n\n revised_label_ids = current_label_ids + new_label_ids\n\n dao.set_issue_label_ids(issue_path, revised_label_ids)\n\n return if_found(dao.get_issue_labels(issue_path))","def test_issue_get_label(self):\n pass","def test_issue_list_labels(self):\n pass","def add_labels_to_issue(self, installation_id, repo_owner, repo_name,\n issue_num, predictions):\n # take an action if the prediction is confident enough\n if predictions['labels']:\n label_names, label_probabilities = self.filter_specified_labels(repo_owner,\n repo_name,\n predictions)\n else:\n label_names = []\n\n # get the isssue handle\n issue = get_issue_handle(installation_id, repo_owner, repo_name, issue_num)\n\n if label_names:\n # create message\n message = \"\"\"Issue-Label Bot is automatically applying the labels `{labels}` to this issue, with the confidence of {confidence}.\n Please mark this comment with :thumbsup: or :thumbsdown: to give our bot feedback!\n Links: [app homepage](https://github.com/marketplace/issue-label-bot), [dashboard]({app_url}data/{repo_owner}/{repo_name}) and [code](https://github.com/hamelsmu/MLapp) for this bot.\n \"\"\".format(labels=\"`, `\".join(label_names),\n confidence=\", \".join([\"{:.2f}\".format(p) for p in label_probabilities]),\n app_url=self.app_url,\n repo_owner=repo_owner,\n repo_name=repo_name)\n # label the issue using the GitHub api\n issue.add_labels(*label_names)\n logging.info(f'Add `{\"`, `\".join(label_names)}` to the issue # {issue_num}')\n else:\n message = \"\"\"Issue Label Bot is not confident enough to auto-label this issue.\n See [dashboard]({app_url}data/{repo_owner}/{repo_name}) for more details.\n \"\"\".format(app_url=self.app_url,\n repo_owner=repo_owner,\n repo_name=repo_name)\n logging.warning(f'Not confident enough to label this issue: # {issue_num}')\n\n # make a comment using the GitHub api\n comment = issue.create_comment(message)","def label(self, name):\r\n return labels.IssueLabel(self, name)","def test_issue_get_labels(self):\n pass","def test_issue_create_label(self):\n pass","def test_issue_clear_labels(self):\n pass","def label_milestone_issues():\n session = GithubSession()\n\n labels = list(session.get_labels())\n labels_by_name = dict([(x['name'], x) for x in labels])\n\n milestones = list(session.get_milestones())\n\n for milestone in milestones:\n label_data = labels_by_name[f'epic:{milestone[\"title\"].strip()}']\n\n for issue in session.get_issues(milestone=milestone[\"number\"], state='all'):\n session.add_label(label_data, number=issue['number'])","def test_labels_change(self):\n label_new = factories.LabelFactory(name=\"test_label\",\n object_type='Assessment')\n response = self.api.put(self.assessment, {'labels': [{\n \"name\": label_new.name,\n \"id\": label_new.id\n }]})\n self.assert200(response)\n notifs, notif_data = common.get_daily_notifications()\n updated = notif_data[\"user@example.com\"][\"assessment_updated\"]\n self.assertEqual(len(notifs), 1)\n self.assertEqual(\n updated[self.assessment.id][\"updated_data\"][\"LABELS\"],\n (\"test_label\", \"\")\n )","def projects_label(args):\n session = GithubSession()\n\n label_datas = list(session.get_labels())\n\n team = args.team\n team_label_data = None\n if team:\n team_label = utils.get_label(team, prefix=\"team\")\n team_label_data = [x for x in label_datas if x[\"name\"] == team_label][0]\n\n # get the project label\n project_label = utils.get_label(args.name, prefix=\"project\")\n project_label_data = [x for x in label_datas if x[\"name\"] == project_label][0]\n\n print(f\"label cards in project {args.name} column {args.column}\")\n\n project_board = session.get_project(args.name)\n project_backlog_grooming = session.get_column(project_board, \"backlog grooming\")\n\n cards = list(session.get_cards(project_backlog_grooming))\n for card_data in cards:\n issue_number = utils.get_issue_number_from_card_data(card_data)\n\n print(issue_number)\n\n # add the project label\n session.add_label(project_label_data, number=issue_number)\n\n if team_label_data:\n session.add_label(team_label_data, number=issue_number)","def test_issue_delete_label(self):\n pass","async def set_status(event, gh, *args, **kwargs):\n issue_number_found = ISSUE_RE.search(event.data[\"pull_request\"][\"title\"])\n if not issue_number_found:\n issue_url = event.data[\"pull_request\"][\"issue_url\"]\n data = await gh.getitem(issue_url)\n for label in data[\"labels\"]:\n if label[\"name\"] == TRIVIAL_LABEL:\n status = TRIVIAL_STATUS\n break\n else:\n status = FAILURE_STATUS\n else:\n status = create_success_status(issue_number_found)\n await _post_status(event, gh, status)","def auto_label_text_chunk(text, labels):\n\n for label in labels:\n regex_str = \"|\".join(labels[label])\n r = re.compile(regex_str, flags=re.I)\n if re.search(r, text) is not None:\n return label\n\n return \"None\"","def was_label_added(issue, label):\n if not label:\n return False\n\n # Optimization that does not require pulling in issue's actions.\n if any(label.lower() == l.lower() for l in issue.labels):\n return True\n\n for action in issue.actions:\n for added in action.labels.added:\n if label.lower() == added.lower():\n return True\n\n return False","def rename_issue(self, msg, chat):\n msg, text = self.split_message(msg)\n\n if text == '':\n self.url_handler.send_message(\"\"\"\n You want to modify the issue {},\n but you didn't provide any new text\n \"\"\".format(msg), chat)\n return\n\n payload = \"{\\n \\\"title\\\": \\\"\"+text+\"\\\",\\n \\\"labels\\\": [\\n \\\"telegram\\\"\\n ]\\n}\"\n print(payload)\n headers = {\n 'Content-Type': \"application/json\",\n 'Authorization': \"Basic WWVzdGVyZGF5WW91U2FpZFRvbW9ycm93Qm90Olllc3RlcmRheVlvdVNhaWRUb21vcnJvdw==\",\n 'Cache-Control': \"no-cache\",\n 'Postman-Token': \"49817fa1-698d-496d-b6e3-252e81bc792f\"\n }\n result = requests.request(\"GET\", constants.URL_GITHUB + '/' + msg)\n result_json = result.json()\n print(result_json)\n try:\n result_json['state']\n except:\n return self.url_handler.send_message(\"Issue does not exist\", chat)\n\n requests.request(\"POST\", constants.URL_GITHUB+'/'+msg, data=payload, headers=headers)\n return self.url_handler.send_message(\"Issue renamed {}\".format(text), chat)","def set_labels(repo: Repository, labels: list[Label]):\n\n log.info(f\"Fetching existing labels from {repo.full_name}\")\n existing_labels = {label.name.casefold(): label for label in repo.get_labels()}\n log.info(f\"Found {len(existing_labels)} existing labels\")\n\n for label in labels:\n qualified_name = label.qualified_name\n folded_name = qualified_name.casefold()\n if folded_name not in existing_labels:\n log.info(f\"Creating label {qualified_name}\")\n repo.create_label(**label.api_arguments)\n elif label != existing_labels[folded_name]:\n log.info(f\"Updating label {qualified_name}\")\n existing_label = existing_labels[folded_name]\n existing_label.edit(**label.api_arguments)\n else:\n log.info(f\"Label {qualified_name} already exists\")","def testWorkflowLabels(self):\n\n text = \"This is the best\"\n\n results = self.client.post(\"workflow\", json={\"name\": \"labels\", \"elements\": [text]}).json()\n self.assertEqual(results[0][0], 0)\n\n results = self.client.post(\"workflow\", json={\"name\": \"multiaction\", \"elements\": [text]}).json()\n self.assertEqual(results[0], \"['positive']. This is the best\")","def sanitize_labels(df):\n df['labels'] = df['query'].apply(lambda x: x.replace(' ', '<br>')) # linebreaks\n df['ranking_label'] = df.ranking.replace({'top': f'Evergreens - updated {df.date[0]}',\n 'rising': f'Trending - updated {df.date[0]}'})\n return df","def updatelabel(task, label, eid):\n ServerManager.get()\n result = ServerManager.api.update_property(task, eid, prop='label', value=label)\n if result.response_type == 'success':\n click.echo(click.style(result.message, fg='green'))\n else:\n click.echo(click.style(result.message, fg='red'))","def _unused_label(self, label):\n original = label\n existing = self.column_labels\n i = 2\n while label in existing:\n label = '{}_{}'.format(original, i)\n i += 1\n return label","def milestone_labels(argv=None):\n argv = argv or sys.argv[1:]\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument('color', help='color to make the labels')\n\n args = parser.parse_args(argv)\n\n session = GithubSession()\n\n labels = session.get_labels()\n\n labels_by_name = dict([(label['name'], label) for label in labels])\n\n for milestone in session.get_milestones():\n label_name = f'epic:{milestone[\"title\"]}'\n\n if label_name in labels_by_name:\n continue\n\n labels_by_name[label_name] = session.create_label(label_name, args.color)\n\n return labels_by_name","def fix_label(label):\n\n replace_dict = {'_': ' ',\n 'degE': '$^{\\circ}$E',\n 'ms-1': '$m s^{-1}$',\n 'm.s-1': '$m s^{-1}$',\n 'Wm-2': '$W m^{-2}$',\n '1000000 m2.s-1': '$10^6$m$^2$s$^{-1}$'\n } \n\n for value, replacement in list(replace_dict.items()):\n label = label.replace(value, replacement)\n\n return label","def pull_labels(self, org):\n pass","def actualizePenaltylabel(self, data):\n if data.has_key(StatusMsg.label_penalty_rest):\n self.penaltylabel.setText(str(data.get(StatusMsg.label_penalty_rest)))","def generate_label(incidentes_viales_df):\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\n r\"(A) La unidad de atención a emergencias fue despachada, \"\n \"llegó al lugar de los hechos y confirmó la emergencia reportada\",\n 'A', inplace=True)\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\n r'(N) La unidad de atención a emergencias fue despachada, '\n 'llegó al lugar de los hechos, pero en el sitio del evento '\n 'nadie solicitó el apoyo de la unidad',\n 'N', inplace=True)\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\n r'(D) El incidente reportado se registró en dos o más '\n 'ocasiones procediendo a mantener un único reporte (afirmativo,'\n ' informativo, negativo o falso) como el identificador para el '\n 'incidente',\n 'D', inplace=True)\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\n r'(F) El operador/a o despachador/a identifican, antes de dar '\n 'respuesta a la emergencia, que ésta es falsa. O al ser '\n 'despachada una unidad de atención a emergencias en el lugar '\n 'de los hechos se percatan que el incidente no corresponde al '\n 'reportado inicialmente',\n 'F', inplace=True)\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\n r'(I) El incidente reportado es afirmativo y se añade '\n 'información adicional al evento',\n 'I', inplace=True)\n\n incidentes_viales_df['label'] = np.where(\n (incidentes_viales_df.codigo_cierre == 'F') | (incidentes_viales_df.codigo_cierre == 'N'), 1, 0)\n\n return incidentes_viales_df","def rdf_update_labels(rdf, node):\n final_list = []\n for i in node.get_labels():\n # print(i)\n final_list += rdf_get_branch(rdf, i)\n for i in final_list:\n node.add_label(i)","def label_messages(self, org, messages, label):\n pass","def ResetReviewLabels(host, change, label, value='0', revision='current',\n message=None, notify=None):\n # This is tricky when working on the \"current\" revision, because there's\n # always the risk that the \"current\" revision will change in between API\n # calls. So, the code dereferences the \"current\" revision down to a literal\n # sha1 at the beginning and uses it for all subsequent calls. As a sanity\n # check, the \"current\" revision is dereferenced again at the end, and if it\n # differs from the previous \"current\" revision, an exception is raised.\n current = (revision == 'current')\n jmsg = GetChangeDetail(\n host, change, o_params=['CURRENT_REVISION', 'CURRENT_COMMIT'])\n if current:\n revision = jmsg['current_revision']\n value = str(value)\n path = '%s/revisions/%s/review' % (_GetChangePath(change), revision)\n message = message or (\n '%s label set to %s programmatically by chromite.' % (label, value))\n for review in jmsg.get('labels', {}).get(label, {}).get('all', []):\n if str(review.get('value', value)) != value:\n body = {\n 'message': message,\n 'labels': {label: value},\n 'on_behalf_of': review['_account_id'],\n }\n if notify:\n body['notify'] = notify\n response = FetchUrlJson(host, path, reqtype='POST', body=body)\n if str(response['labels'][label]) != value:\n username = review.get('email', jmsg.get('name', ''))\n raise GOBError(200, 'Unable to set %s label for user \"%s\"'\n ' on change %s.' % (label, username, change))\n if current:\n new_revision = GetChangeCurrentRevision(host, change)\n if not new_revision:\n raise GOBError(\n 200, 'Could not get review information for change \"%s\"' % change)\n elif new_revision != revision:\n raise GOBError(200, 'While resetting labels on change \"%s\", '\n 'a new patchset was uploaded.' % change)","def labels_list(issue):\n return [x['name'] for x in issue['labels']]","def label_7m(predictor, zhibo7m):\n for item_ in zhibo7m.find():\n try:\n live_texts_ = item_[\"content\"][\"textFeed\"]\n except Exception as e:\n zhibo7m.delete_one({\"_id\": item_['_id']})\n print(\"delete error id: {}\".format(item_[\"_id\"]))\n print(e)\n for l_index_, l_item_ in enumerate(live_texts_):\n l_item_[\"p_label\"] = predictor.predict(l_item_[\"msg\"])[0]\n live_texts_[l_index_] = l_item_\n # print(l_item_)\n zhibo7m.update_one({\"_id\": item_['_id']}, {\"$set\": {\"textFeed\": live_texts_}})","def labels(self):\r\n return labels.IssueLabels(self)","def test_unchanged_status_text(self):\n scale = \"count\"\n metric1 = dict(type=\"metric_type\",\n name=\"Metric\",\n unit=\"units\",\n scale=scale,\n recent_measurements=[dict(count=dict(value=0,\n status=\"near_target_met\")),\n dict(count=dict(value=42,\n status=\"near_target_met\"))])\n metric2 = dict(type=\"metric_type\",\n name=\"Metric\",\n unit=\"units\",\n scale=scale,\n recent_measurements=[dict(count=dict(value=5,\n status=\"target_met\")),\n dict(count=dict(value=10,\n status=\"target_not_met\"))])\n metric_notification_data1 = MetricNotificationData(metric1, self.data_model, \"status_long_unchanged\")\n metric_notification_data2 = MetricNotificationData(metric2, self.data_model, \"status_long_unchanged\")\n notification = Notification(self.report, [metric_notification_data1,\n metric_notification_data2], \"destination_uuid\", {})\n text = build_notification_text(notification)\n self.assertEqual(\n \"[Report 1](https://report1) has 2 metrics that are notable:\\n\\n\"\n \"* Metric has been yellow (near target met) for three weeks. Value: 42 units.\\n\"\n \"* Metric has been red (target not met) for three weeks. Value: 10 units.\\n\",\n text)","def reduce_labels(line: str) -> str:\n labels = [\"none\", \"favor\", \"against\"]\n label = line.split(\":\")[1]\n return label if label in labels else \"none\"","def delete_issue_label(repo, project, issue_number, delete_label_id):\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\n\n current_label_ids = dao.get_issue_label_ids(issue_path)\n\n revised_label_ids = [label_id for label_id in current_label_ids\n if label_id != delete_label_id]\n\n dao.set_issue_label_ids(issue_path, revised_label_ids)\n\n return if_found(dao.get_issue_labels(issue_path))","async def removed_label(event, gh, *args, **kwargs):\n if event.data[\"label\"][\"name\"] == TRIVIAL_LABEL:\n await set_status(event, gh)","def get_label(issue_id):\n auth = subprocess.check_output(\n \"gsutil cat\"\n \" gs://bazel-trusted-encrypted-secrets/github-trusted-token.enc |\"\n \" gcloud kms decrypt --project bazel-public --location global\"\n \" --keyring buildkite --key github-trusted-token --ciphertext-file\"\n \" - --plaintext-file -\", shell=True\n ).decode(\"utf-8\").strip().split(\"\\n\")[0]\n headers = {\n \"Authorization\": \"Bearer \" + auth,\n \"Accept\": \"application/vnd.github+json\",\n }\n response = requests.get(\n \"https://api.github.com/repos/bazelbuild/bazel/issues/\"\n + issue_id + \"/labels\", headers=headers,\n )\n for item in response.json():\n for key, value in item.items():\n if key == \"name\" and \"team-\" in value:\n return value.strip()\n return None","def updateQuestionsSolved(self):\r\n self.questionsCompletedLabel.setText(\"Questions completed: {}\".format(save.getProblemsSolved()))","def update_completed_emoji():\n for emoji in Desire.uncompleted_emoji():\n if emoji in get_slack_emoji():\n Desire.complete_emoji_for_all_users(emoji)","def apply(labels: Dict[str, str], dry=False) -> None:\n for old, new in labels.items():\n log.rename_label(old, new, subroutine, dry=dry)","def label_experiment(self, exp_id):\n exp = experiment.experiment(new_experiment=False, ts=str(exp_id))\n label = request.form.get('label')\n exp.update_metadata(change_label=True, label=label)\n\n return \"OK\"","def pending(ui, repo, *pats, **opts):\n\tm = LoadAllCL(ui, repo, web=True)\n\tnames = m.keys()\n\tnames.sort()\n\tfor name in names:\n\t\tcl = m[name]\n\t\tui.write(cl.PendingText() + \"\\n\")\n\n\tfiles = DefaultFiles(ui, repo, [], opts)\n\tif len(files) > 0:\n\t\ts = \"Changed files not in any CL:\\n\"\n\t\tfor f in files:\n\t\t\ts += \"\\t\" + f + \"\\n\"\n\t\tui.write(s)","def _apply_label(self, label):\n data = {\n \"name\" : label.title,\n \"description\" : label.desc,\n \"color\" : label.color\n }\n resp = self._post(\n self._base + \"/labels\", data=self._format_data(data))","def modify_labels(self, body, update_labels):\n for label_modifier in self.schema.get(\"labels\", []):\n\n # handle the service selector lookup\n spec = label_modifier.get(\"service\")\n if spec is not None:\n try:\n # get the service we're after\n service = self.k8s_inventory.k8s_client.get_service(\n name=spec.get(\"name\"),\n namespace=spec.get(\"namespace\"),\n )\n # reset the existing labels\n update_labels()\n # update with the selectors from the service\n update_labels(**service.spec.selector)\n except:\n return False\n\n # handle the static label modifications\n spec = label_modifier.get(\"label\")\n if spec is not None:\n updates = dict()\n updates[spec.get(\"key\")] = spec.get(\"value\")\n update_labels(**updates)\n\n return body","def label_bfwin007(predictor, bfwin007):\n for item_ in bfwin007.find():\n live_texts_ = item_[\"live_texts\"]\n if 0 == len(live_texts_):\n bfwin007.delete_one({\"_id\": item_[\"_id\"]})\n continue\n for l_index_, l_item_ in enumerate(live_texts_):\n l_item_[\"p_label\"] = predictor.predict(l_item_[\"live_text\"])[0]\n live_texts_[l_index_] = l_item_\n bfwin007.update_one({\"_id\": item_['_id']}, {\"$set\": {\"live_text\": live_texts_}})","def cleanup_new_bugs_with_assignee(self):\n logger.info(\"Cleanup new bugs with an assignee\")\n\n message = (\"@%s:\\n\\nSince you are set as assignee, I switch the \"\n \"status to 'In Progress'.\")\n subject = \"Cleanup\"\n\n project = self.client.projects[self.project_name]\n bug_tasks = project.searchTasks(status=['New'],\n omit_duplicates=True)\n switched_bugs = []\n\n for t in bug_tasks:\n bug_id = t.bug.id\n if bug_id in self.ignoreable_bug_ids:\n logger.debug(\"Ignore bug '%s'. \", bug_id)\n continue\n logger.debug(\"Checking bug '%s'\", bug_id)\n assignee = t.assignee\n if assignee is None:\n continue\n t.status = 'In Progress'\n switched_bugs.append(bug_id)\n content = message % assignee.display_name\n if self.dryrun:\n logger.debug(\"DRYRUN: I would switch bug '%s'\", bug_id)\n continue\n logger.debug(\"Switching status of bug '%s'\", bug_id)\n t.lp_save()\n t.bug.newMessage(content=content, subject=subject)\n\n logger.info(\"Switched bugs: '%s'\", switched_bugs)","def _clean_labels(self, data, label_col='user_labels'):\n\n def clean_up(lbl, tag):\n if not lbl:\n if not tag:\n return 'Unidentified'\n else:\n return 'Non-Prorocentrum'\n elif 'False Prorocentrum' in lbl or \\\n 'Prorocentrum_false_positiveal' in lbl:\n return 'Non-Prorocentrum'\n elif lbl[0] in ['Prorocentrum', 'False Non-Prorocentrum']:\n return lbl[0]\n else:\n return 'Non-Prorocentrum'\n\n df = data.copy()\n df[label_col] = df.apply(lambda x: clean_up(x[label_col],\n x['tags']), axis=1)\n df['label'] = df[label_col].map(self.classes)\n return df","def add_label_to_existing(hostname, new_labels):\n\n logging.debug('going to add labels %s to existing host %s' % (new_labels, hostname))\n\n checkmk_api_url = config['checkmk_api_url']\n checkmk_api_username = config['checkmk_api_username']\n checkmk_api_secret = config['checkmk_api_secret']\n checkmk_puppetdb_label = config['checkmk_puppetdb_label']\n\n # Save the attributes, save the ~world~ existing labels\n req_params = { 'action': 'get_host',\n '_username': config['checkmk_api_username'],\n '_secret': config['checkmk_api_secret'],\n 'hostname': hostname,\n 'output_format': 'json' }\n r = requests.post(checkmk_api_url, req_params)\n\n existing_labels = {}\n try:\n existing_labels.update(r.json()['result']['attributes']['labels'])\n except:\n pass\n\n # add new labels to existing labels and ensure from_puppetdb label present\n existing_labels.update(new_labels)\n existing_labels.update({ 'from_puppetdb': checkmk_puppetdb_label })\n\n payload = {'request': json.dumps({\n 'hostname': hostname,\n 'attributes': {\n 'labels': existing_labels\n }\n })}\n\n logging.debug('-- adding labels %s to host %s' % (existing_labels, hostname))\n r = requests.post(\"%s?action=edit_host&_username=%s&_secret=%s\" % (checkmk_api_url, checkmk_api_username, checkmk_api_secret), data=payload)\n logging.debug('-- got resp code = %d' % r.status_code)\n logging.debug('-- got resp text = %s' % r.text)\n r_json = json.loads(r.text)\n\n # Successful edit_host gives response of {\"result\": null, \"result_code\": 0}\n if r_json['result_code'] == 0 and r_json['result'] is None:\n logging.info('added labels %s to %s successfully' % (existing_labels, hostname))\n else:\n logging.warn('failed to add labels %s to host %s' % (r_json['result'], hostname))","def answer_problem(self, step):\r\n world.css_fill(self.active_problem_selector('.comment'), 'Test Response')\r\n world.css_click(self.active_problem_selector('.tag[data-id=\"{}\"]'.format(self.active_problem)))\r\n world.css_click(self.active_problem_selector('.check'))","def test_end(token, issue_number, actor, commit, run_id, new_token):\n success_or_only_flakiness, log_summary = _get_summary_table(token, run_id)\n if success_or_only_flakiness and not log_summary:\n # succeeded (without flakiness)\n firebase_github.add_label(token, issue_number, _LABEL_SUCCEED)\n comment = (_COMMENT_TITLE_SUCCEED +\n _get_description(actor, commit, run_id) +\n _COMMENT_HIDDEN_DIVIDER)\n _update_comment(token, issue_number, comment)\n else:\n if success_or_only_flakiness:\n # all failures/errors are due to flakiness (succeeded after retry)\n title = _COMMENT_TITLE_FLAKY\n firebase_github.add_label(token, issue_number, _LABEL_SUCCEED)\n else:\n # failures/errors still exist after retry\n title = _COMMENT_TITLE_FAIL\n firebase_github.add_label(token, issue_number, _LABEL_FAILED)\n comment = (title +\n _get_description(actor, commit, run_id) +\n log_summary +\n _COMMENT_FLAKY_TRACKER +\n _COMMENT_HIDDEN_DIVIDER)\n _update_comment(token, issue_number, comment)\n\n firebase_github.delete_label(new_token, issue_number, _LABEL_PROGRESS)","def label_rule_for_others(text: str, label_type: str) -> str:\n match = re.search(LABEL_SPECIFICATION[f\"RE_{label_type.upper()}\"], text)\n if match:\n return match.group(\"label\").strip()\n return \"\"","def UpdateLabel(self) -> _n_6_t_0:","def create_success_status(found_issue):\n status = STATUS_TEMPLATE.copy()\n status[\"state\"] = \"success\"\n issue_number = found_issue.group(\"issue\")\n status[\"description\"] = f\"Issue number {issue_number} found.\"\n status[\"target_url\"] = f\"https://bugs.python.org/issue{issue_number}\"\n return status","def makeRegularFinishResultLabel(finishReason, teamResult):\n if finishReason == FINISH_REASON.EXTERMINATION:\n return i18n.makeString(_FINISH_REASON_LABEL.format(''.join((str(finishReason), str(teamResult)))))\n else:\n return i18n.makeString(_FINISH_REASON_LABEL.format(finishReason))","def test_labels(self):\n self.compliance_tester.test_labels(self.oi)","def update_tt_label(video: dict):\n # try to get all text tracks\n if tts := video.get('text_tracks'):\n # go through all tracks\n for track in tts:\n # change the setting\n track['label'] = track['srclang']\n\n # get the video ID\n video_id = video.get('id')\n # create the JSON body\n json_body = { 'text_tracks': tts }\n # make the PATCH call\n r = get_cms().UpdateVideo(video_id=video_id, json_body=json_body)\n # check if all went well\n if r.status_code in [200,202]:\n print(f'Updated track labels for video ID {video_id} with status {r.status_code}.')\n # otherwise report the error\n else:\n print(f'Error code {r.status_code} updating track labels for video ID {video_id}:')\n print(r.text)","def put_labels():\n dao.delete_all_labels()\n for label in request.json:\n if 'id' not in label or not label['id']:\n label['id'] = str(uuid.uuid4())\n dao.set_label(id=label['id'],\n name=label['name'],\n fields=label['fields'])\n return if_found(dao.get_labels())","def translate_label(label):\n return invalid_re.sub('_', label)","def candidate_regressions(self):\n failclass = ('not classified', 'fixed by commit')\n candidate_regressions = set()\n for label, summary in self.label_summaries.items():\n if summary.status == Status.PASS:\n continue\n\n if all(c not in failclass for c in summary.classifications):\n continue\n\n candidate_regressions.add(label)\n return candidate_regressions","def test_update_from_pending_change_with_rich_text_reset(self):\n review_request = ReviewRequest.objects.create(self.user,\n self.repository)\n draft = ReviewRequestDraft.create(review_request)\n\n draft.description_rich_text = True\n draft.testing_done_rich_text = True\n\n changeset = ChangeSet()\n changeset.changenum = 4\n changeset.summary = '* This is a summary'\n changeset.description = '* This is a description.'\n changeset.testing_done = '* This is some testing.'\n draft.update_from_pending_change(4, changeset)\n\n self.assertEqual(draft.summary, '* This is a summary')\n self.assertEqual(draft.description, '* This is a description.')\n self.assertFalse(draft.description_rich_text)\n self.assertEqual(draft.testing_done, '* This is some testing.')\n self.assertFalse(draft.testing_done_rich_text)","async def issues(self, ctx):\n await ctx.message.delete()\n await ctx.send(\"Issue tracker: https://github.com/TheSuperGamer20578/Sudan-bot/issues\")","def _UpdateLabels(self, args, migration_job, update_fields):\n add_labels = labels_util.GetUpdateLabelsDictFromArgs(args)\n remove_labels = labels_util.GetRemoveLabelsListFromArgs(args)\n value_type = self.messages.MigrationJob.LabelsValue\n update_result = labels_util.Diff(\n additions=add_labels,\n subtractions=remove_labels,\n clear=args.clear_labels\n ).Apply(value_type)\n if update_result.needs_update:\n migration_job.labels = update_result.labels\n update_fields.append('labels')","def _update_team_labels(self, host, team_name):\n # host = Host.get(identifier=identifier)\n # function to get labelFingerprint and Labels info from an instance of GCP\n instance = self.get_instance(host.name, host.zone)\n labelFingerprint = instance['labelFingerprint']\n labels = instance['labels']\n\n # function to get information from a team\n team = TeamClient(api_url=TEAM_API_URL, team_name=team_name)\n team = team.team\n\n # add info of new team\n labels['servico_de_negocio'] = team.get('servico-de-negocio')\n labels['cliente'] = team.get('cliente')\n labels['team_slug_name'] = team.get('slug')\n labels['team_id'] = team.get('id')\n\n # create the body of function with Label Fingerprint and Labels\n body = dict()\n body['labels'] = labels\n body['labelFingerprint'] = labelFingerprint\n\n update_label = self.client.instances().setLabels(project=self.credential.project,\n zone=host.zone,\n instance=host.name,\n body=body).execute()\n\n return self.wait_operation(operation=update_label.get('name'),\n zone=host.zone)","def organize_issues_id(issues_status):\n issues_formatted = []\n\n for issue_status in issues_status:\n current = {issue_status['#text']: {'status_id': Status.PASS.value}}\n\n if issue_status['@fail'] == '1':\n current[issue_status['#text']]['status_id'] = Status.FAIL.value\n\n issues_formatted.append(current)\n\n return issues_formatted","def find_mistake(self):\n if self.solve_stats[-1][\"comment\"] == \"\":\n for stat in reversed(self.solve_stats):\n if stat[\"comment\"] != \"\":\n self.solve_stats[stat[\"count\"]][\"comment\"] += \"mistake from here\"\n break","def new_label(self, context, payload):\n\n labels = GmailActions.labels(context)['labels']\n label_id = \"\"\n\n for label in labels:\n if label['name'] == payload['name']:\n label_id = label['id']\n break\n\n access_token = util.get_access_token(context['headers'])\n url = util.get_url(context) + f\"labels/{label_id}\"\n response = util.rest(\"GET\", url, access_token)\n\n if response.status_code > 400:\n raise Exception(\"Error \", response.text)\n\n return json.loads(response.text)","def remove_labels(number, labels):\n\n cmds = [github_cli, 'pr', 'edit', str(number)]\n for lab in labels:\n cmds += ['--remove-label', lab]\n\n with subprocess.Popen(cmds) as p:\n _, err = p.communicate()\n print(err)","def get_label(repo, title, verbose=None):\n if verbose:\n print \"Checking for label...\"\n label = None\n label_text = None\n try:\n label_start = 1 + title.index('(')\n label_end = title.index(')')\n label_text = title[label_start:label_end]\n except ValueError, e:\n print \"Warning: This tile has no embeded label. {0}\".format(e)\n if label_text:\n try:\n label = [repo.get_label(label_text)]\n if verbose:\n print \"Found label: {0}\".format(label)\n except UnknownObjectException, e:\n print \"Error: The label '{0}' does not exist on \" \\\n \"Github. {1}\".format(label_text, e)\n return label","def bb_label(hit):\n try:\n labelid = hit.group(1)\n label = Label.objects.get(id=labelid)\n T = loader.get_template('webview/t/label.html')\n C = Context({ 'L' : label })\n return T.render(C)\n except:\n # Usually thrown if the ID is invalid or doesn't exist\n return '[label]%s[/label]' % (labelid)","def _update_label(self, outer_pos, inner_pos, new_label):\n r, c = outer_pos\n ir, ic = inner_pos\n self.inner_boards[r][c][ir][ic][\"text\"] = new_label","def _add_new_labels(running_lbls, current_lbls):\n if running_lbls is None:\n return current_lbls[:] # copy!\n elif running_lbls != current_lbls:\n for lbl in current_lbls:\n if lbl not in running_lbls:\n running_lbls.append(lbl)\n return running_lbls","def the_following_message_there_are_unapplied_network_changes_apply_them_now_unapplied_changes_will_be_lost_should_appear(driver):\n wait_on_element(driver, 0.5, 30, '//button[contains(.,\"TEST CHANGES\")]')\n driver.find_element_by_xpath('//button[contains(.,\"TEST CHANGES\")]').click()","def test_issue_search_issues(self):\n pass","def fix_label_names():\n\n assert trace.cpu.trace_done\n binary_addr = memorymanager.BinaryAddr(0)\n while binary_addr < len(classifications):\n c = classifications[binary_addr]\n if c is not None:\n dummy = [str(x) for x in c.as_string_list(binary_addr, None)]\n binary_addr += c.length()\n else:\n binary_addr += 1","def propagate_labels(image,labels,conflict=0):\n rlabels,_ = label(image)\n cors = correspondences(rlabels,labels,False)\n outputs = zeros(amax(rlabels)+1,'i')\n oops = -(1<<30)\n for o,i in cors.T:\n if outputs[o]!=0: outputs[o] = oops\n else: outputs[o] = i\n outputs[outputs==oops] = conflict\n outputs[0] = 0\n return outputs[rlabels]","def backlog_milestone():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"project\", help=\"name of the project\")\n parser.add_argument(\"milestone\", help=\"name of the milestone\")\n\n args = parser.parse_args()\n\n session = GithubSession()\n\n project_data = session.get_project(args.project)\n\n milestone_data = session.get_milestone(args.milestone)\n milestone_title = milestone_data[\"title\"]\n\n backlog_data = session.get_column(project_data, \"backlog\")\n icebox_data = session.get_column(project_data, \"icebox\")\n\n results = session.search(f'repo:openslate/openslate milestone:\"{milestone_title}\"')\n for search_data in results[\"items\"]:\n issue_data = get_issue(search_data[\"number\"]).issue\n issue_card = session.get_card(project_data, issue_data)\n\n if issue_card[\"column_url\"] == icebox_data[\"url\"]:\n session.move_card(issue_card, backlog_data)\n\n print(\".\", end=\"\")","def replaceLabels(self, old, new):\n for c in self.iterCurves():\n c.update({'label': c.getAttribute('label').replace(old, new)})","def process_not_mtn_bug(self,\n bug,\n project_name,\n old_milestone_name,\n new_milestone):\n errors = self.add_target_to_bug(bug, project_name, new_milestone)\n\n new_status = \"Won't Fix\"\n self.logging.debug(\"Update bug #%s status to '%s' for milestone: %s\",\n bug.bug.id, new_status, old_milestone_name)\n if not self.is_debug() and not errors:\n bug.status = new_status\n try:\n bug.lp_save()\n except Exception as exc: # pylint: disable=W0703\n errors = True\n self.logging.error(\n \"Can't update bug #%s status to '%s' for milestone: %s\",\n bug.bug.id,\n new_status,\n old_milestone_name\n )\n self.logging.exception(exc)\n\n if not errors:\n self.get_stats()[project_name][old_milestone_name]['migrated'] += 1\n self.increase_proccessed_issues()\n else:\n self.logging.error(\"Can't reassign the bug #%s.\", bug.bug.id)","def set_action_notification_text(apps, schema_editor):\n Action = apps.get_model(\"goals\", \"Action\")\n for action in Action.objects.all():\n text = action.notification_text\n if text and text[0].isupper() and not text[1].isupper():\n text = \"{}{}\".format(text[0:1].lower(), text[1:])\n action.notification_text = \"You can {}\".format(text)\n action.save()","def parse_status_file(jira, filename, issues):\n # Regexp to match Jira issue on a single line, i.e:\n # [SWG-28]\n # [LITE-32]\n # ...\n regex = r\"^\\[([A-Z]+-[0-9]+).*\\]\\n$\"\n\n # Regexp to match a tag that indicates we should stop processing, ex:\n # [STOP]\n # [JIPDATE-STOP]\n # [OTHER]\n # ...\n regex_stop = r\"^\\[.*\\]\\n$\"\n\n # Regexp to mach a tag that indicates to stop processing completely:\n # [FIN]\n regex_fin = r\"^\\[FIN\\]\\n$\"\n\n # Regexp to match for a status update, this will remove 'Status' from the\n # match:\n regex_status = r\"(?:^Status:) *(.+)\\n$\"\n\n # Contains the status text, it could be a file or a status email\n status = \"\"\n\n # Regexp to match for a time spent update, this will remove 'Time spent:'\n # from the match:\n regex_timespent = r\"(^Time spent:) \\d+\\w\\n$\"\n\n # List of resolutions (when doing a transition to Resolved). Query once globally.\n resolution_map = dict([(t.name.title(), t.id) for t in jira.resolutions()])\n\n with open(filename) as f:\n status = f.readlines()\n\n myissue = \"\"\n mycomment = \"\"\n\n # build list of {issue,comment} tuples found in status\n issue_comments = []\n for line in status:\n # New issue?\n match = re.search(regex, line)\n\n # Evaluate and save the transition regex for later. We have to do this\n # here, since we cannot assign and save the variable in the if\n # construction as you can do in C for example.\n transition = re.search(regex_status, line)\n\n if match:\n myissue = match.group(1)\n validissue = True\n\n # if we ran a query, we might already have fetched the issue\n # let's try to find the issue there first, otherwise ask Jira\n try:\n issue = [x for x in issues if str(x) == myissue][0]\n issue_comments.append((issue, \"\", \"\", None))\n\n # IndexError: we had fetched already, but issue is not found\n # TypeError: issues is None, we haven't queried Jira yet, at all\n except (IndexError, TypeError) as e:\n try:\n issue = jira.issue(myissue)\n issue_comments.append((issue, \"\", \"\", None))\n except Exception as e:\n if \"Issue Does Not Exist\" in e.text:\n print(\"[{}] : {}\".format(myissue, e.text))\n validissue = False\n\n # Stop parsing entirely. This needs to be placed before regex_stop\n # or the .* will match and [FIN] won't be processed\n elif re.search(regex_fin, line):\n break\n # If we have non-JIRA issue tags, stop parsing until we find a valid tag\n elif re.search(regex_stop, line):\n validissue = False\n elif transition and validissue:\n # If we have a match, then the new status should be first in the\n # group. Jira always expect the name of the state transitions to be\n # word capitalized, hence the call to the title() function. This\n # means that it doesn't matter if the user enter all lower case,\n # mixed or all upper case. All of them will work.\n new_status = transition.groups()[0].title()\n (i, c, _, ts) = issue_comments[-1]\n issue_comments[-1] = (i, c, new_status, ts)\n elif re.search(regex_timespent, line, re.IGNORECASE):\n timespent = line.split(\":\")[1].strip()\n (i, c, t, _) = issue_comments[-1]\n issue_comments[-1] = (i, c, t, timespent)\n else:\n # Don't add lines with comments\n if line[0] != \"#\" and issue_comments and validissue:\n (i, c, t, ts) = issue_comments[-1]\n issue_comments[-1] = (i, c + line, t, ts)\n\n issue_upload = []\n print(\"These JIRA cards will be updated as follows:\\n\")\n for idx, t in enumerate(issue_comments):\n (issue, comment, transition, timespent) = issue_comments[idx]\n\n # Strip beginning and trailing blank lines\n comment = comment.strip(\"\\n\")\n\n # initialize here to avoid unassigned variables and useless code complexity\n resolution_id = transition_id = None\n resolution = transition_summary = \"\"\n\n if transition != \"\" and transition != str(issue.fields.status):\n # An optional 'resolution' attribute can be set when doing a transition\n # to Resolved, using the following pattern: Resolved / <resolution>\n if (\n transition.startswith(\"Resolved\") or transition.startswith(\"Closed\")\n ) and \"/\" in transition:\n (transition, resolution) = map(str.strip, transition.split(\"/\"))\n if not resolution in resolution_map:\n print(\n 'Invalid resolution \"{}\" for issue {}'.format(resolution, issue)\n )\n print(\"Possible resolution: {}\".format([t for t in resolution_map]))\n sys.exit(1)\n resolution_id = resolution_map[resolution]\n\n transition_map = dict(\n [(t[\"name\"].title(), t[\"id\"]) for t in jira.transitions(issue)]\n )\n if not transition in transition_map:\n print('Invalid transition \"{}\" for issue {}'.format(transition, issue))\n print(\"Possible transitions: {}\".format([t for t in transition_map]))\n sys.exit(1)\n\n transition_id = transition_map[transition]\n if resolution:\n transition_summary = \" %s => %s (%s)\" % (\n issue.fields.status,\n transition,\n resolution,\n )\n else:\n transition_summary = \" %s => %s\" % (issue.fields.status, transition)\n\n if comment == \"\" and not transition_id:\n log.debug(\n \"Issue [%s] has no comment or transitions, not updating the issue\"\n % (issue)\n )\n continue\n\n issue_upload.append(\n (\n issue,\n comment,\n {\"transition\": transition_id, \"resolution\": resolution_id},\n timespent,\n )\n )\n print(\n \"[%s]%s\\n %s\"\n % (issue, transition_summary, \"\\n \".join(comment.splitlines()))\n )\n if timespent:\n print(\" Time spent: %s\" % timespent)\n print(\"\")\n\n issue_comments = issue_upload\n if issue_comments == [] or cfg.args.dry_run or should_update() == \"n\":\n if issue_comments == []:\n print(\"No change, Jira was not updated!\\n\")\n else:\n print(\"Comments will not be written to Jira!\\n\")\n if not cfg.args.s:\n print_status(status)\n sys.exit()\n\n # if we found something, let's update jira\n for issue, comment, transition, timespent in issue_comments:\n update_jira(jira, issue, comment, transition, timespent)\n\n print(\"Successfully updated your Jira tickets!\\n\")\n if not cfg.args.s:\n print_status(status)","def process_text(text):\n fix_dict = {'fig.': 'fig', 'fig .': 'fig ', 'Fig.': 'Fig', 'Fig .': 'Fig ',\n 'figure.': 'figure', 'figure .': 'figure ', 'Figure.': 'Fig', 'Figure .': 'Fig ',\n 'et al.': 'et al', 'III': '3', 'II': '2', 'I': '1'}\n\n for old_pattern in fix_dict.keys():\n text = text.replace(old_pattern, fix_dict[old_pattern])\n return text","def process_bug_links(text):\n text = LP_RE.sub(r\"[\\1](https://bugs.launchpad.net/bugs/\\2)\", text)\n text = BDO_RE.sub(r\"[\\1](https://bugs.debian.org/\\2)\", text)\n text = ISSUE_RE.sub(r\"[\\1](https://github.com/gammu/gammu/\\2)\", text)\n return BUG_RE.sub(r\"[\\1](https://bugs.cihar.com/\\2)\", text)","def play_level(correct_words, blanks, sentence):\n print sentence\n for index in range(4):\n check_word(correct_words[index], blanks[index])\n sentence = sentence.replace(blanks[index], correct_words[index])\n print sentence","def test_correct_labels(self):\n timeseries = self.build_timeseries()\n\n metric_labels_list = main.get_labels(timeseries[\"metric\"],\"labels\") \n expected_metric_labels_list = self.build_metric_labels()\n self.assertEqual(sorted(metric_labels_list), sorted(expected_metric_labels_list))\n\n resource_labels_list = main.get_labels(timeseries[\"resource\"],\"labels\")\n expected_resource_labels_list = self.build_resource_labels()\n self.assertEqual(sorted(resource_labels_list), sorted(expected_resource_labels_list))\n\n user_labels_list = main.get_labels(self.build_user_labels_request(),\"userLabels\")\n expected_user_labels_list = self.build_expected_user_labels_response()\n self.assertEqual(sorted(user_labels_list), sorted(expected_user_labels_list))\n\n system_labels_list = main.get_system_labels(self.build_user_labels_request(),\"systemLabels\")\n expected_system_labels_list = self.build_expected_system_labels_response()\n self.assertEqual(sorted(system_labels_list), sorted(expected_system_labels_list))","def handle_labels(ls):\r\n\r\n # assign each line a number\r\n line_num = {}\r\n counter = 0\r\n for i in ls:\r\n if not i.startswith('('):\r\n line_num[i] = counter\r\n counter += 1\r\n else:\r\n sb = i[1:-1]\r\n line_num[sb] = counter\r\n\r\n # replace @XXX with number\r\n var_address = 16\r\n mem = {}\r\n for i in range(len(ls)):\r\n if ls[i].startswith('@'):\r\n # if @XXX is already in numeral form, do nothing\r\n if ls[i][1:].isdigit():\r\n pass\r\n\r\n # replace with pre-defined symbols if found\r\n elif pre_defined_sb.get(ls[i][1:]) is not None:\r\n ls[i] = '@' + pre_defined_sb[ls[i][1:]]\r\n\r\n # replace by (XXX) line number if search failed\r\n elif line_num.get(ls[i][1:]) is not None:\r\n ls[i] = '@' + str(line_num[ls[i][1:]])\r\n\r\n # else must be user defined variable\r\n # assign same address for same variable\r\n else:\r\n if ls[i] not in mem:\r\n mem[ls[i]] = '@' + str(var_address)\r\n ls[i] = '@' + str(var_address)\r\n var_address += 1\r\n else:\r\n ls[i] = mem[ls[i]]\r\n\r\n # remove (XXX)'s\r\n ls = list(filter(lambda x: not x.startswith('('), ls))\r\n\r\n return ls","def update_label(self, repository, name, color, old_name=None, **kwargs):\n data = {'name': name, 'color': color}\n response = self.session.patch(\n '{}/repos/{}/labels/{}'.format(\n self.GH_API_ENDPOINT, repository, old_name or name\n ),\n json=data\n )\n if response.status_code != 200:\n raise GitHubError(response)","def check_indicator_files(tasks):\n\n for task in tasks:\n if task[\"status\"]==\"unknown\":\n if os.path.exists(task[\"result\"]):\n task[\"status\"]=\"previously completed\"\n else:\n task[\"status\"]=\"to do\"\n return","def label(cmd):\r\n cmd = cmd.replace('make][.DP', 'make1][.NP')\r\n cmd = cmd.replace('make][.SC', 'make2][.SC')\r\n cmd = re.sub('(draw.*)one','\\\\1one1',cmd)\r\n cmd = re.sub('(make1.*)one','\\\\1one1',cmd)\r\n cmd = re.sub('(make2.*)one','\\\\1one2',cmd)\r\n cmd = re.sub('(move.*)one','\\\\1one2',cmd)\r\n cmd = re.sub('(hide.*)one','\\\\1one2',cmd)\r\n cmd = '[result ' + cmd + ']' #dummy function for plop\r\n return cmd","def label(cmd):\n cmd = cmd.replace('make][.DP', 'make1][.NP')\n cmd = cmd.replace('make][.SC', 'make2][.SC')\n cmd = re.sub('(draw.*)one','\\\\1one1',cmd)\n cmd = re.sub('(make1.*)one','\\\\1one1',cmd)\n cmd = re.sub('(make2.*)one','\\\\1one2',cmd)\n cmd = re.sub('(move.*)one','\\\\1one2',cmd)\n cmd = re.sub('(hide.*)one','\\\\1one2',cmd)\n cmd = '[result ' + cmd + ']' #dummy function for plop\n return cmd","def confirm_replace( scr ):\n d = ConfirmReplaceDialog(scr)\n value = d.main()\n if \"yes\" in value:\n if value[\"yes\"]:\n return 1\n elif value[\"no\"]:\n return 2\n elif value[\"all\"]:\n return 3\n elif value[\"cancel\"]:\n return 4\n else:\n return 4","def tag(issue):\n # assert check_tags_exist(issue, _REQUIRED_TAGS)\n print(\"Tagging %s...\" %issue.filename)\n\n # Labels rows.\n issue = copy.deepcopy(issue)\n for index, row in issue.tags_df.iterrows():\n issue.tags_df.loc[index, \"jump\"] = '0'\n\n # If text is not null then search for JUMP.\n if not pd.isnull(row.text):\n text = row.text.strip()\n jump = _has_page_jump(text)\n if jump:\n issue.tags_df.loc[index, \"jump\"] = str(jump)\n\n # If 'function' column is not null set based on number of words.\n if pd.isnull(row.function):\n is_ME = len(text.split()) < 5\n issue.tags_df.loc[index, \"function\"] = \"ME\" if is_ME else \"TXT\"\n return issue","def _MaybeAddLabel(label_name):\n if label_name.lower() in labels_already_seen:\n return\n labels_already_seen.add(label_name.lower())\n if '-' in label_name:\n col, _value = label_name.split('-', 1)\n _MaybeAddCol(col)","def test_fallback_status_emoji(self):\n self.assertEqual(get_emoji_for_status(None), \"\")"],"string":"[\n \"def test_issue_replace_labels(self):\\n pass\",\n \"def change_issues_label(self, msg, old_label, new_label):\\n self._asset_bind(msg)\\n yield (\\\"Processing....\\\")\\n trans = self._translation_util(msg)\\n client = self._github_operator(msg)\\n cmd = \\\"repo:{} label:{} is:open type:issue\\\".format(\\n task_repository_name(), old_label)\\n issue_list = client.search_issue(cmd, 10)\\n for issue in issue_list:\\n trans.wait_for_limit(MAX_RESULT, MAX_RESULT)\\n issue.remove_from_labels(old_label)\\n issue.add_to_labels(new_label)\\n yield \\\"{} issues has been changed label from {} to {}\\\".format(len(issue_list), old_label, new_label)\",\n \"def test_issue_remove_label(self):\\n pass\",\n \"async def new_label(event, gh, *args, **kwargs):\\n if event.data[\\\"label\\\"][\\\"name\\\"] == TRIVIAL_LABEL:\\n issue_number_found = ISSUE_RE.search(\\n event.data[\\\"pull_request\\\"][\\\"title\\\"])\\n if issue_number_found:\\n status = create_success_status(issue_number_found)\\n else:\\n status = TRIVIAL_STATUS\\n await _post_status(event, gh, status)\",\n \"def test_issue_add_label(self):\\n pass\",\n \"def patch_issue_labels(repo, project, issue_number):\\n add_label_ids = request.json.get('addLabelIds') or []\\n remove_label_ids = request.json.get('removeLabelIds') or []\\n\\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\\n\\n current_label_ids = dao.get_issue_label_ids(issue_path)\\n\\n revised_label_ids = [label_id for label_id in current_label_ids + add_label_ids\\n if label_id not in remove_label_ids]\\n\\n dao.set_issue_label_ids(issue_path, revised_label_ids)\\n\\n return if_found(dao.get_issue_labels(issue_path))\",\n \"def test_issue_edit_label(self):\\n pass\",\n \"def post_issue_labels(repo, project, issue_number):\\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\\n\\n new_label_ids = request.json\\n\\n current_label_ids = dao.get_issue_label_ids(issue_path)\\n\\n revised_label_ids = current_label_ids + new_label_ids\\n\\n dao.set_issue_label_ids(issue_path, revised_label_ids)\\n\\n return if_found(dao.get_issue_labels(issue_path))\",\n \"def test_issue_get_label(self):\\n pass\",\n \"def test_issue_list_labels(self):\\n pass\",\n \"def add_labels_to_issue(self, installation_id, repo_owner, repo_name,\\n issue_num, predictions):\\n # take an action if the prediction is confident enough\\n if predictions['labels']:\\n label_names, label_probabilities = self.filter_specified_labels(repo_owner,\\n repo_name,\\n predictions)\\n else:\\n label_names = []\\n\\n # get the isssue handle\\n issue = get_issue_handle(installation_id, repo_owner, repo_name, issue_num)\\n\\n if label_names:\\n # create message\\n message = \\\"\\\"\\\"Issue-Label Bot is automatically applying the labels `{labels}` to this issue, with the confidence of {confidence}.\\n Please mark this comment with :thumbsup: or :thumbsdown: to give our bot feedback!\\n Links: [app homepage](https://github.com/marketplace/issue-label-bot), [dashboard]({app_url}data/{repo_owner}/{repo_name}) and [code](https://github.com/hamelsmu/MLapp) for this bot.\\n \\\"\\\"\\\".format(labels=\\\"`, `\\\".join(label_names),\\n confidence=\\\", \\\".join([\\\"{:.2f}\\\".format(p) for p in label_probabilities]),\\n app_url=self.app_url,\\n repo_owner=repo_owner,\\n repo_name=repo_name)\\n # label the issue using the GitHub api\\n issue.add_labels(*label_names)\\n logging.info(f'Add `{\\\"`, `\\\".join(label_names)}` to the issue # {issue_num}')\\n else:\\n message = \\\"\\\"\\\"Issue Label Bot is not confident enough to auto-label this issue.\\n See [dashboard]({app_url}data/{repo_owner}/{repo_name}) for more details.\\n \\\"\\\"\\\".format(app_url=self.app_url,\\n repo_owner=repo_owner,\\n repo_name=repo_name)\\n logging.warning(f'Not confident enough to label this issue: # {issue_num}')\\n\\n # make a comment using the GitHub api\\n comment = issue.create_comment(message)\",\n \"def label(self, name):\\r\\n return labels.IssueLabel(self, name)\",\n \"def test_issue_get_labels(self):\\n pass\",\n \"def test_issue_create_label(self):\\n pass\",\n \"def test_issue_clear_labels(self):\\n pass\",\n \"def label_milestone_issues():\\n session = GithubSession()\\n\\n labels = list(session.get_labels())\\n labels_by_name = dict([(x['name'], x) for x in labels])\\n\\n milestones = list(session.get_milestones())\\n\\n for milestone in milestones:\\n label_data = labels_by_name[f'epic:{milestone[\\\"title\\\"].strip()}']\\n\\n for issue in session.get_issues(milestone=milestone[\\\"number\\\"], state='all'):\\n session.add_label(label_data, number=issue['number'])\",\n \"def test_labels_change(self):\\n label_new = factories.LabelFactory(name=\\\"test_label\\\",\\n object_type='Assessment')\\n response = self.api.put(self.assessment, {'labels': [{\\n \\\"name\\\": label_new.name,\\n \\\"id\\\": label_new.id\\n }]})\\n self.assert200(response)\\n notifs, notif_data = common.get_daily_notifications()\\n updated = notif_data[\\\"user@example.com\\\"][\\\"assessment_updated\\\"]\\n self.assertEqual(len(notifs), 1)\\n self.assertEqual(\\n updated[self.assessment.id][\\\"updated_data\\\"][\\\"LABELS\\\"],\\n (\\\"test_label\\\", \\\"\\\")\\n )\",\n \"def projects_label(args):\\n session = GithubSession()\\n\\n label_datas = list(session.get_labels())\\n\\n team = args.team\\n team_label_data = None\\n if team:\\n team_label = utils.get_label(team, prefix=\\\"team\\\")\\n team_label_data = [x for x in label_datas if x[\\\"name\\\"] == team_label][0]\\n\\n # get the project label\\n project_label = utils.get_label(args.name, prefix=\\\"project\\\")\\n project_label_data = [x for x in label_datas if x[\\\"name\\\"] == project_label][0]\\n\\n print(f\\\"label cards in project {args.name} column {args.column}\\\")\\n\\n project_board = session.get_project(args.name)\\n project_backlog_grooming = session.get_column(project_board, \\\"backlog grooming\\\")\\n\\n cards = list(session.get_cards(project_backlog_grooming))\\n for card_data in cards:\\n issue_number = utils.get_issue_number_from_card_data(card_data)\\n\\n print(issue_number)\\n\\n # add the project label\\n session.add_label(project_label_data, number=issue_number)\\n\\n if team_label_data:\\n session.add_label(team_label_data, number=issue_number)\",\n \"def test_issue_delete_label(self):\\n pass\",\n \"async def set_status(event, gh, *args, **kwargs):\\n issue_number_found = ISSUE_RE.search(event.data[\\\"pull_request\\\"][\\\"title\\\"])\\n if not issue_number_found:\\n issue_url = event.data[\\\"pull_request\\\"][\\\"issue_url\\\"]\\n data = await gh.getitem(issue_url)\\n for label in data[\\\"labels\\\"]:\\n if label[\\\"name\\\"] == TRIVIAL_LABEL:\\n status = TRIVIAL_STATUS\\n break\\n else:\\n status = FAILURE_STATUS\\n else:\\n status = create_success_status(issue_number_found)\\n await _post_status(event, gh, status)\",\n \"def auto_label_text_chunk(text, labels):\\n\\n for label in labels:\\n regex_str = \\\"|\\\".join(labels[label])\\n r = re.compile(regex_str, flags=re.I)\\n if re.search(r, text) is not None:\\n return label\\n\\n return \\\"None\\\"\",\n \"def was_label_added(issue, label):\\n if not label:\\n return False\\n\\n # Optimization that does not require pulling in issue's actions.\\n if any(label.lower() == l.lower() for l in issue.labels):\\n return True\\n\\n for action in issue.actions:\\n for added in action.labels.added:\\n if label.lower() == added.lower():\\n return True\\n\\n return False\",\n \"def rename_issue(self, msg, chat):\\n msg, text = self.split_message(msg)\\n\\n if text == '':\\n self.url_handler.send_message(\\\"\\\"\\\"\\n You want to modify the issue {},\\n but you didn't provide any new text\\n \\\"\\\"\\\".format(msg), chat)\\n return\\n\\n payload = \\\"{\\\\n \\\\\\\"title\\\\\\\": \\\\\\\"\\\"+text+\\\"\\\\\\\",\\\\n \\\\\\\"labels\\\\\\\": [\\\\n \\\\\\\"telegram\\\\\\\"\\\\n ]\\\\n}\\\"\\n print(payload)\\n headers = {\\n 'Content-Type': \\\"application/json\\\",\\n 'Authorization': \\\"Basic WWVzdGVyZGF5WW91U2FpZFRvbW9ycm93Qm90Olllc3RlcmRheVlvdVNhaWRUb21vcnJvdw==\\\",\\n 'Cache-Control': \\\"no-cache\\\",\\n 'Postman-Token': \\\"49817fa1-698d-496d-b6e3-252e81bc792f\\\"\\n }\\n result = requests.request(\\\"GET\\\", constants.URL_GITHUB + '/' + msg)\\n result_json = result.json()\\n print(result_json)\\n try:\\n result_json['state']\\n except:\\n return self.url_handler.send_message(\\\"Issue does not exist\\\", chat)\\n\\n requests.request(\\\"POST\\\", constants.URL_GITHUB+'/'+msg, data=payload, headers=headers)\\n return self.url_handler.send_message(\\\"Issue renamed {}\\\".format(text), chat)\",\n \"def set_labels(repo: Repository, labels: list[Label]):\\n\\n log.info(f\\\"Fetching existing labels from {repo.full_name}\\\")\\n existing_labels = {label.name.casefold(): label for label in repo.get_labels()}\\n log.info(f\\\"Found {len(existing_labels)} existing labels\\\")\\n\\n for label in labels:\\n qualified_name = label.qualified_name\\n folded_name = qualified_name.casefold()\\n if folded_name not in existing_labels:\\n log.info(f\\\"Creating label {qualified_name}\\\")\\n repo.create_label(**label.api_arguments)\\n elif label != existing_labels[folded_name]:\\n log.info(f\\\"Updating label {qualified_name}\\\")\\n existing_label = existing_labels[folded_name]\\n existing_label.edit(**label.api_arguments)\\n else:\\n log.info(f\\\"Label {qualified_name} already exists\\\")\",\n \"def testWorkflowLabels(self):\\n\\n text = \\\"This is the best\\\"\\n\\n results = self.client.post(\\\"workflow\\\", json={\\\"name\\\": \\\"labels\\\", \\\"elements\\\": [text]}).json()\\n self.assertEqual(results[0][0], 0)\\n\\n results = self.client.post(\\\"workflow\\\", json={\\\"name\\\": \\\"multiaction\\\", \\\"elements\\\": [text]}).json()\\n self.assertEqual(results[0], \\\"['positive']. This is the best\\\")\",\n \"def sanitize_labels(df):\\n df['labels'] = df['query'].apply(lambda x: x.replace(' ', '<br>')) # linebreaks\\n df['ranking_label'] = df.ranking.replace({'top': f'Evergreens - updated {df.date[0]}',\\n 'rising': f'Trending - updated {df.date[0]}'})\\n return df\",\n \"def updatelabel(task, label, eid):\\n ServerManager.get()\\n result = ServerManager.api.update_property(task, eid, prop='label', value=label)\\n if result.response_type == 'success':\\n click.echo(click.style(result.message, fg='green'))\\n else:\\n click.echo(click.style(result.message, fg='red'))\",\n \"def _unused_label(self, label):\\n original = label\\n existing = self.column_labels\\n i = 2\\n while label in existing:\\n label = '{}_{}'.format(original, i)\\n i += 1\\n return label\",\n \"def milestone_labels(argv=None):\\n argv = argv or sys.argv[1:]\\n\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument('color', help='color to make the labels')\\n\\n args = parser.parse_args(argv)\\n\\n session = GithubSession()\\n\\n labels = session.get_labels()\\n\\n labels_by_name = dict([(label['name'], label) for label in labels])\\n\\n for milestone in session.get_milestones():\\n label_name = f'epic:{milestone[\\\"title\\\"]}'\\n\\n if label_name in labels_by_name:\\n continue\\n\\n labels_by_name[label_name] = session.create_label(label_name, args.color)\\n\\n return labels_by_name\",\n \"def fix_label(label):\\n\\n replace_dict = {'_': ' ',\\n 'degE': '$^{\\\\circ}$E',\\n 'ms-1': '$m s^{-1}$',\\n 'm.s-1': '$m s^{-1}$',\\n 'Wm-2': '$W m^{-2}$',\\n '1000000 m2.s-1': '$10^6$m$^2$s$^{-1}$'\\n } \\n\\n for value, replacement in list(replace_dict.items()):\\n label = label.replace(value, replacement)\\n\\n return label\",\n \"def pull_labels(self, org):\\n pass\",\n \"def actualizePenaltylabel(self, data):\\n if data.has_key(StatusMsg.label_penalty_rest):\\n self.penaltylabel.setText(str(data.get(StatusMsg.label_penalty_rest)))\",\n \"def generate_label(incidentes_viales_df):\\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\\n r\\\"(A) La unidad de atención a emergencias fue despachada, \\\"\\n \\\"llegó al lugar de los hechos y confirmó la emergencia reportada\\\",\\n 'A', inplace=True)\\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\\n r'(N) La unidad de atención a emergencias fue despachada, '\\n 'llegó al lugar de los hechos, pero en el sitio del evento '\\n 'nadie solicitó el apoyo de la unidad',\\n 'N', inplace=True)\\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\\n r'(D) El incidente reportado se registró en dos o más '\\n 'ocasiones procediendo a mantener un único reporte (afirmativo,'\\n ' informativo, negativo o falso) como el identificador para el '\\n 'incidente',\\n 'D', inplace=True)\\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\\n r'(F) El operador/a o despachador/a identifican, antes de dar '\\n 'respuesta a la emergencia, que ésta es falsa. O al ser '\\n 'despachada una unidad de atención a emergencias en el lugar '\\n 'de los hechos se percatan que el incidente no corresponde al '\\n 'reportado inicialmente',\\n 'F', inplace=True)\\n incidentes_viales_df.codigo_cierre.mask(incidentes_viales_df.codigo_cierre ==\\n r'(I) El incidente reportado es afirmativo y se añade '\\n 'información adicional al evento',\\n 'I', inplace=True)\\n\\n incidentes_viales_df['label'] = np.where(\\n (incidentes_viales_df.codigo_cierre == 'F') | (incidentes_viales_df.codigo_cierre == 'N'), 1, 0)\\n\\n return incidentes_viales_df\",\n \"def rdf_update_labels(rdf, node):\\n final_list = []\\n for i in node.get_labels():\\n # print(i)\\n final_list += rdf_get_branch(rdf, i)\\n for i in final_list:\\n node.add_label(i)\",\n \"def label_messages(self, org, messages, label):\\n pass\",\n \"def ResetReviewLabels(host, change, label, value='0', revision='current',\\n message=None, notify=None):\\n # This is tricky when working on the \\\"current\\\" revision, because there's\\n # always the risk that the \\\"current\\\" revision will change in between API\\n # calls. So, the code dereferences the \\\"current\\\" revision down to a literal\\n # sha1 at the beginning and uses it for all subsequent calls. As a sanity\\n # check, the \\\"current\\\" revision is dereferenced again at the end, and if it\\n # differs from the previous \\\"current\\\" revision, an exception is raised.\\n current = (revision == 'current')\\n jmsg = GetChangeDetail(\\n host, change, o_params=['CURRENT_REVISION', 'CURRENT_COMMIT'])\\n if current:\\n revision = jmsg['current_revision']\\n value = str(value)\\n path = '%s/revisions/%s/review' % (_GetChangePath(change), revision)\\n message = message or (\\n '%s label set to %s programmatically by chromite.' % (label, value))\\n for review in jmsg.get('labels', {}).get(label, {}).get('all', []):\\n if str(review.get('value', value)) != value:\\n body = {\\n 'message': message,\\n 'labels': {label: value},\\n 'on_behalf_of': review['_account_id'],\\n }\\n if notify:\\n body['notify'] = notify\\n response = FetchUrlJson(host, path, reqtype='POST', body=body)\\n if str(response['labels'][label]) != value:\\n username = review.get('email', jmsg.get('name', ''))\\n raise GOBError(200, 'Unable to set %s label for user \\\"%s\\\"'\\n ' on change %s.' % (label, username, change))\\n if current:\\n new_revision = GetChangeCurrentRevision(host, change)\\n if not new_revision:\\n raise GOBError(\\n 200, 'Could not get review information for change \\\"%s\\\"' % change)\\n elif new_revision != revision:\\n raise GOBError(200, 'While resetting labels on change \\\"%s\\\", '\\n 'a new patchset was uploaded.' % change)\",\n \"def labels_list(issue):\\n return [x['name'] for x in issue['labels']]\",\n \"def label_7m(predictor, zhibo7m):\\n for item_ in zhibo7m.find():\\n try:\\n live_texts_ = item_[\\\"content\\\"][\\\"textFeed\\\"]\\n except Exception as e:\\n zhibo7m.delete_one({\\\"_id\\\": item_['_id']})\\n print(\\\"delete error id: {}\\\".format(item_[\\\"_id\\\"]))\\n print(e)\\n for l_index_, l_item_ in enumerate(live_texts_):\\n l_item_[\\\"p_label\\\"] = predictor.predict(l_item_[\\\"msg\\\"])[0]\\n live_texts_[l_index_] = l_item_\\n # print(l_item_)\\n zhibo7m.update_one({\\\"_id\\\": item_['_id']}, {\\\"$set\\\": {\\\"textFeed\\\": live_texts_}})\",\n \"def labels(self):\\r\\n return labels.IssueLabels(self)\",\n \"def test_unchanged_status_text(self):\\n scale = \\\"count\\\"\\n metric1 = dict(type=\\\"metric_type\\\",\\n name=\\\"Metric\\\",\\n unit=\\\"units\\\",\\n scale=scale,\\n recent_measurements=[dict(count=dict(value=0,\\n status=\\\"near_target_met\\\")),\\n dict(count=dict(value=42,\\n status=\\\"near_target_met\\\"))])\\n metric2 = dict(type=\\\"metric_type\\\",\\n name=\\\"Metric\\\",\\n unit=\\\"units\\\",\\n scale=scale,\\n recent_measurements=[dict(count=dict(value=5,\\n status=\\\"target_met\\\")),\\n dict(count=dict(value=10,\\n status=\\\"target_not_met\\\"))])\\n metric_notification_data1 = MetricNotificationData(metric1, self.data_model, \\\"status_long_unchanged\\\")\\n metric_notification_data2 = MetricNotificationData(metric2, self.data_model, \\\"status_long_unchanged\\\")\\n notification = Notification(self.report, [metric_notification_data1,\\n metric_notification_data2], \\\"destination_uuid\\\", {})\\n text = build_notification_text(notification)\\n self.assertEqual(\\n \\\"[Report 1](https://report1) has 2 metrics that are notable:\\\\n\\\\n\\\"\\n \\\"* Metric has been yellow (near target met) for three weeks. Value: 42 units.\\\\n\\\"\\n \\\"* Metric has been red (target not met) for three weeks. Value: 10 units.\\\\n\\\",\\n text)\",\n \"def reduce_labels(line: str) -> str:\\n labels = [\\\"none\\\", \\\"favor\\\", \\\"against\\\"]\\n label = line.split(\\\":\\\")[1]\\n return label if label in labels else \\\"none\\\"\",\n \"def delete_issue_label(repo, project, issue_number, delete_label_id):\\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\\n\\n current_label_ids = dao.get_issue_label_ids(issue_path)\\n\\n revised_label_ids = [label_id for label_id in current_label_ids\\n if label_id != delete_label_id]\\n\\n dao.set_issue_label_ids(issue_path, revised_label_ids)\\n\\n return if_found(dao.get_issue_labels(issue_path))\",\n \"async def removed_label(event, gh, *args, **kwargs):\\n if event.data[\\\"label\\\"][\\\"name\\\"] == TRIVIAL_LABEL:\\n await set_status(event, gh)\",\n \"def get_label(issue_id):\\n auth = subprocess.check_output(\\n \\\"gsutil cat\\\"\\n \\\" gs://bazel-trusted-encrypted-secrets/github-trusted-token.enc |\\\"\\n \\\" gcloud kms decrypt --project bazel-public --location global\\\"\\n \\\" --keyring buildkite --key github-trusted-token --ciphertext-file\\\"\\n \\\" - --plaintext-file -\\\", shell=True\\n ).decode(\\\"utf-8\\\").strip().split(\\\"\\\\n\\\")[0]\\n headers = {\\n \\\"Authorization\\\": \\\"Bearer \\\" + auth,\\n \\\"Accept\\\": \\\"application/vnd.github+json\\\",\\n }\\n response = requests.get(\\n \\\"https://api.github.com/repos/bazelbuild/bazel/issues/\\\"\\n + issue_id + \\\"/labels\\\", headers=headers,\\n )\\n for item in response.json():\\n for key, value in item.items():\\n if key == \\\"name\\\" and \\\"team-\\\" in value:\\n return value.strip()\\n return None\",\n \"def updateQuestionsSolved(self):\\r\\n self.questionsCompletedLabel.setText(\\\"Questions completed: {}\\\".format(save.getProblemsSolved()))\",\n \"def update_completed_emoji():\\n for emoji in Desire.uncompleted_emoji():\\n if emoji in get_slack_emoji():\\n Desire.complete_emoji_for_all_users(emoji)\",\n \"def apply(labels: Dict[str, str], dry=False) -> None:\\n for old, new in labels.items():\\n log.rename_label(old, new, subroutine, dry=dry)\",\n \"def label_experiment(self, exp_id):\\n exp = experiment.experiment(new_experiment=False, ts=str(exp_id))\\n label = request.form.get('label')\\n exp.update_metadata(change_label=True, label=label)\\n\\n return \\\"OK\\\"\",\n \"def pending(ui, repo, *pats, **opts):\\n\\tm = LoadAllCL(ui, repo, web=True)\\n\\tnames = m.keys()\\n\\tnames.sort()\\n\\tfor name in names:\\n\\t\\tcl = m[name]\\n\\t\\tui.write(cl.PendingText() + \\\"\\\\n\\\")\\n\\n\\tfiles = DefaultFiles(ui, repo, [], opts)\\n\\tif len(files) > 0:\\n\\t\\ts = \\\"Changed files not in any CL:\\\\n\\\"\\n\\t\\tfor f in files:\\n\\t\\t\\ts += \\\"\\\\t\\\" + f + \\\"\\\\n\\\"\\n\\t\\tui.write(s)\",\n \"def _apply_label(self, label):\\n data = {\\n \\\"name\\\" : label.title,\\n \\\"description\\\" : label.desc,\\n \\\"color\\\" : label.color\\n }\\n resp = self._post(\\n self._base + \\\"/labels\\\", data=self._format_data(data))\",\n \"def modify_labels(self, body, update_labels):\\n for label_modifier in self.schema.get(\\\"labels\\\", []):\\n\\n # handle the service selector lookup\\n spec = label_modifier.get(\\\"service\\\")\\n if spec is not None:\\n try:\\n # get the service we're after\\n service = self.k8s_inventory.k8s_client.get_service(\\n name=spec.get(\\\"name\\\"),\\n namespace=spec.get(\\\"namespace\\\"),\\n )\\n # reset the existing labels\\n update_labels()\\n # update with the selectors from the service\\n update_labels(**service.spec.selector)\\n except:\\n return False\\n\\n # handle the static label modifications\\n spec = label_modifier.get(\\\"label\\\")\\n if spec is not None:\\n updates = dict()\\n updates[spec.get(\\\"key\\\")] = spec.get(\\\"value\\\")\\n update_labels(**updates)\\n\\n return body\",\n \"def label_bfwin007(predictor, bfwin007):\\n for item_ in bfwin007.find():\\n live_texts_ = item_[\\\"live_texts\\\"]\\n if 0 == len(live_texts_):\\n bfwin007.delete_one({\\\"_id\\\": item_[\\\"_id\\\"]})\\n continue\\n for l_index_, l_item_ in enumerate(live_texts_):\\n l_item_[\\\"p_label\\\"] = predictor.predict(l_item_[\\\"live_text\\\"])[0]\\n live_texts_[l_index_] = l_item_\\n bfwin007.update_one({\\\"_id\\\": item_['_id']}, {\\\"$set\\\": {\\\"live_text\\\": live_texts_}})\",\n \"def cleanup_new_bugs_with_assignee(self):\\n logger.info(\\\"Cleanup new bugs with an assignee\\\")\\n\\n message = (\\\"@%s:\\\\n\\\\nSince you are set as assignee, I switch the \\\"\\n \\\"status to 'In Progress'.\\\")\\n subject = \\\"Cleanup\\\"\\n\\n project = self.client.projects[self.project_name]\\n bug_tasks = project.searchTasks(status=['New'],\\n omit_duplicates=True)\\n switched_bugs = []\\n\\n for t in bug_tasks:\\n bug_id = t.bug.id\\n if bug_id in self.ignoreable_bug_ids:\\n logger.debug(\\\"Ignore bug '%s'. \\\", bug_id)\\n continue\\n logger.debug(\\\"Checking bug '%s'\\\", bug_id)\\n assignee = t.assignee\\n if assignee is None:\\n continue\\n t.status = 'In Progress'\\n switched_bugs.append(bug_id)\\n content = message % assignee.display_name\\n if self.dryrun:\\n logger.debug(\\\"DRYRUN: I would switch bug '%s'\\\", bug_id)\\n continue\\n logger.debug(\\\"Switching status of bug '%s'\\\", bug_id)\\n t.lp_save()\\n t.bug.newMessage(content=content, subject=subject)\\n\\n logger.info(\\\"Switched bugs: '%s'\\\", switched_bugs)\",\n \"def _clean_labels(self, data, label_col='user_labels'):\\n\\n def clean_up(lbl, tag):\\n if not lbl:\\n if not tag:\\n return 'Unidentified'\\n else:\\n return 'Non-Prorocentrum'\\n elif 'False Prorocentrum' in lbl or \\\\\\n 'Prorocentrum_false_positiveal' in lbl:\\n return 'Non-Prorocentrum'\\n elif lbl[0] in ['Prorocentrum', 'False Non-Prorocentrum']:\\n return lbl[0]\\n else:\\n return 'Non-Prorocentrum'\\n\\n df = data.copy()\\n df[label_col] = df.apply(lambda x: clean_up(x[label_col],\\n x['tags']), axis=1)\\n df['label'] = df[label_col].map(self.classes)\\n return df\",\n \"def add_label_to_existing(hostname, new_labels):\\n\\n logging.debug('going to add labels %s to existing host %s' % (new_labels, hostname))\\n\\n checkmk_api_url = config['checkmk_api_url']\\n checkmk_api_username = config['checkmk_api_username']\\n checkmk_api_secret = config['checkmk_api_secret']\\n checkmk_puppetdb_label = config['checkmk_puppetdb_label']\\n\\n # Save the attributes, save the ~world~ existing labels\\n req_params = { 'action': 'get_host',\\n '_username': config['checkmk_api_username'],\\n '_secret': config['checkmk_api_secret'],\\n 'hostname': hostname,\\n 'output_format': 'json' }\\n r = requests.post(checkmk_api_url, req_params)\\n\\n existing_labels = {}\\n try:\\n existing_labels.update(r.json()['result']['attributes']['labels'])\\n except:\\n pass\\n\\n # add new labels to existing labels and ensure from_puppetdb label present\\n existing_labels.update(new_labels)\\n existing_labels.update({ 'from_puppetdb': checkmk_puppetdb_label })\\n\\n payload = {'request': json.dumps({\\n 'hostname': hostname,\\n 'attributes': {\\n 'labels': existing_labels\\n }\\n })}\\n\\n logging.debug('-- adding labels %s to host %s' % (existing_labels, hostname))\\n r = requests.post(\\\"%s?action=edit_host&_username=%s&_secret=%s\\\" % (checkmk_api_url, checkmk_api_username, checkmk_api_secret), data=payload)\\n logging.debug('-- got resp code = %d' % r.status_code)\\n logging.debug('-- got resp text = %s' % r.text)\\n r_json = json.loads(r.text)\\n\\n # Successful edit_host gives response of {\\\"result\\\": null, \\\"result_code\\\": 0}\\n if r_json['result_code'] == 0 and r_json['result'] is None:\\n logging.info('added labels %s to %s successfully' % (existing_labels, hostname))\\n else:\\n logging.warn('failed to add labels %s to host %s' % (r_json['result'], hostname))\",\n \"def answer_problem(self, step):\\r\\n world.css_fill(self.active_problem_selector('.comment'), 'Test Response')\\r\\n world.css_click(self.active_problem_selector('.tag[data-id=\\\"{}\\\"]'.format(self.active_problem)))\\r\\n world.css_click(self.active_problem_selector('.check'))\",\n \"def test_end(token, issue_number, actor, commit, run_id, new_token):\\n success_or_only_flakiness, log_summary = _get_summary_table(token, run_id)\\n if success_or_only_flakiness and not log_summary:\\n # succeeded (without flakiness)\\n firebase_github.add_label(token, issue_number, _LABEL_SUCCEED)\\n comment = (_COMMENT_TITLE_SUCCEED +\\n _get_description(actor, commit, run_id) +\\n _COMMENT_HIDDEN_DIVIDER)\\n _update_comment(token, issue_number, comment)\\n else:\\n if success_or_only_flakiness:\\n # all failures/errors are due to flakiness (succeeded after retry)\\n title = _COMMENT_TITLE_FLAKY\\n firebase_github.add_label(token, issue_number, _LABEL_SUCCEED)\\n else:\\n # failures/errors still exist after retry\\n title = _COMMENT_TITLE_FAIL\\n firebase_github.add_label(token, issue_number, _LABEL_FAILED)\\n comment = (title +\\n _get_description(actor, commit, run_id) +\\n log_summary +\\n _COMMENT_FLAKY_TRACKER +\\n _COMMENT_HIDDEN_DIVIDER)\\n _update_comment(token, issue_number, comment)\\n\\n firebase_github.delete_label(new_token, issue_number, _LABEL_PROGRESS)\",\n \"def label_rule_for_others(text: str, label_type: str) -> str:\\n match = re.search(LABEL_SPECIFICATION[f\\\"RE_{label_type.upper()}\\\"], text)\\n if match:\\n return match.group(\\\"label\\\").strip()\\n return \\\"\\\"\",\n \"def UpdateLabel(self) -> _n_6_t_0:\",\n \"def create_success_status(found_issue):\\n status = STATUS_TEMPLATE.copy()\\n status[\\\"state\\\"] = \\\"success\\\"\\n issue_number = found_issue.group(\\\"issue\\\")\\n status[\\\"description\\\"] = f\\\"Issue number {issue_number} found.\\\"\\n status[\\\"target_url\\\"] = f\\\"https://bugs.python.org/issue{issue_number}\\\"\\n return status\",\n \"def makeRegularFinishResultLabel(finishReason, teamResult):\\n if finishReason == FINISH_REASON.EXTERMINATION:\\n return i18n.makeString(_FINISH_REASON_LABEL.format(''.join((str(finishReason), str(teamResult)))))\\n else:\\n return i18n.makeString(_FINISH_REASON_LABEL.format(finishReason))\",\n \"def test_labels(self):\\n self.compliance_tester.test_labels(self.oi)\",\n \"def update_tt_label(video: dict):\\n # try to get all text tracks\\n if tts := video.get('text_tracks'):\\n # go through all tracks\\n for track in tts:\\n # change the setting\\n track['label'] = track['srclang']\\n\\n # get the video ID\\n video_id = video.get('id')\\n # create the JSON body\\n json_body = { 'text_tracks': tts }\\n # make the PATCH call\\n r = get_cms().UpdateVideo(video_id=video_id, json_body=json_body)\\n # check if all went well\\n if r.status_code in [200,202]:\\n print(f'Updated track labels for video ID {video_id} with status {r.status_code}.')\\n # otherwise report the error\\n else:\\n print(f'Error code {r.status_code} updating track labels for video ID {video_id}:')\\n print(r.text)\",\n \"def put_labels():\\n dao.delete_all_labels()\\n for label in request.json:\\n if 'id' not in label or not label['id']:\\n label['id'] = str(uuid.uuid4())\\n dao.set_label(id=label['id'],\\n name=label['name'],\\n fields=label['fields'])\\n return if_found(dao.get_labels())\",\n \"def translate_label(label):\\n return invalid_re.sub('_', label)\",\n \"def candidate_regressions(self):\\n failclass = ('not classified', 'fixed by commit')\\n candidate_regressions = set()\\n for label, summary in self.label_summaries.items():\\n if summary.status == Status.PASS:\\n continue\\n\\n if all(c not in failclass for c in summary.classifications):\\n continue\\n\\n candidate_regressions.add(label)\\n return candidate_regressions\",\n \"def test_update_from_pending_change_with_rich_text_reset(self):\\n review_request = ReviewRequest.objects.create(self.user,\\n self.repository)\\n draft = ReviewRequestDraft.create(review_request)\\n\\n draft.description_rich_text = True\\n draft.testing_done_rich_text = True\\n\\n changeset = ChangeSet()\\n changeset.changenum = 4\\n changeset.summary = '* This is a summary'\\n changeset.description = '* This is a description.'\\n changeset.testing_done = '* This is some testing.'\\n draft.update_from_pending_change(4, changeset)\\n\\n self.assertEqual(draft.summary, '* This is a summary')\\n self.assertEqual(draft.description, '* This is a description.')\\n self.assertFalse(draft.description_rich_text)\\n self.assertEqual(draft.testing_done, '* This is some testing.')\\n self.assertFalse(draft.testing_done_rich_text)\",\n \"async def issues(self, ctx):\\n await ctx.message.delete()\\n await ctx.send(\\\"Issue tracker: https://github.com/TheSuperGamer20578/Sudan-bot/issues\\\")\",\n \"def _UpdateLabels(self, args, migration_job, update_fields):\\n add_labels = labels_util.GetUpdateLabelsDictFromArgs(args)\\n remove_labels = labels_util.GetRemoveLabelsListFromArgs(args)\\n value_type = self.messages.MigrationJob.LabelsValue\\n update_result = labels_util.Diff(\\n additions=add_labels,\\n subtractions=remove_labels,\\n clear=args.clear_labels\\n ).Apply(value_type)\\n if update_result.needs_update:\\n migration_job.labels = update_result.labels\\n update_fields.append('labels')\",\n \"def _update_team_labels(self, host, team_name):\\n # host = Host.get(identifier=identifier)\\n # function to get labelFingerprint and Labels info from an instance of GCP\\n instance = self.get_instance(host.name, host.zone)\\n labelFingerprint = instance['labelFingerprint']\\n labels = instance['labels']\\n\\n # function to get information from a team\\n team = TeamClient(api_url=TEAM_API_URL, team_name=team_name)\\n team = team.team\\n\\n # add info of new team\\n labels['servico_de_negocio'] = team.get('servico-de-negocio')\\n labels['cliente'] = team.get('cliente')\\n labels['team_slug_name'] = team.get('slug')\\n labels['team_id'] = team.get('id')\\n\\n # create the body of function with Label Fingerprint and Labels\\n body = dict()\\n body['labels'] = labels\\n body['labelFingerprint'] = labelFingerprint\\n\\n update_label = self.client.instances().setLabels(project=self.credential.project,\\n zone=host.zone,\\n instance=host.name,\\n body=body).execute()\\n\\n return self.wait_operation(operation=update_label.get('name'),\\n zone=host.zone)\",\n \"def organize_issues_id(issues_status):\\n issues_formatted = []\\n\\n for issue_status in issues_status:\\n current = {issue_status['#text']: {'status_id': Status.PASS.value}}\\n\\n if issue_status['@fail'] == '1':\\n current[issue_status['#text']]['status_id'] = Status.FAIL.value\\n\\n issues_formatted.append(current)\\n\\n return issues_formatted\",\n \"def find_mistake(self):\\n if self.solve_stats[-1][\\\"comment\\\"] == \\\"\\\":\\n for stat in reversed(self.solve_stats):\\n if stat[\\\"comment\\\"] != \\\"\\\":\\n self.solve_stats[stat[\\\"count\\\"]][\\\"comment\\\"] += \\\"mistake from here\\\"\\n break\",\n \"def new_label(self, context, payload):\\n\\n labels = GmailActions.labels(context)['labels']\\n label_id = \\\"\\\"\\n\\n for label in labels:\\n if label['name'] == payload['name']:\\n label_id = label['id']\\n break\\n\\n access_token = util.get_access_token(context['headers'])\\n url = util.get_url(context) + f\\\"labels/{label_id}\\\"\\n response = util.rest(\\\"GET\\\", url, access_token)\\n\\n if response.status_code > 400:\\n raise Exception(\\\"Error \\\", response.text)\\n\\n return json.loads(response.text)\",\n \"def remove_labels(number, labels):\\n\\n cmds = [github_cli, 'pr', 'edit', str(number)]\\n for lab in labels:\\n cmds += ['--remove-label', lab]\\n\\n with subprocess.Popen(cmds) as p:\\n _, err = p.communicate()\\n print(err)\",\n \"def get_label(repo, title, verbose=None):\\n if verbose:\\n print \\\"Checking for label...\\\"\\n label = None\\n label_text = None\\n try:\\n label_start = 1 + title.index('(')\\n label_end = title.index(')')\\n label_text = title[label_start:label_end]\\n except ValueError, e:\\n print \\\"Warning: This tile has no embeded label. {0}\\\".format(e)\\n if label_text:\\n try:\\n label = [repo.get_label(label_text)]\\n if verbose:\\n print \\\"Found label: {0}\\\".format(label)\\n except UnknownObjectException, e:\\n print \\\"Error: The label '{0}' does not exist on \\\" \\\\\\n \\\"Github. {1}\\\".format(label_text, e)\\n return label\",\n \"def bb_label(hit):\\n try:\\n labelid = hit.group(1)\\n label = Label.objects.get(id=labelid)\\n T = loader.get_template('webview/t/label.html')\\n C = Context({ 'L' : label })\\n return T.render(C)\\n except:\\n # Usually thrown if the ID is invalid or doesn't exist\\n return '[label]%s[/label]' % (labelid)\",\n \"def _update_label(self, outer_pos, inner_pos, new_label):\\n r, c = outer_pos\\n ir, ic = inner_pos\\n self.inner_boards[r][c][ir][ic][\\\"text\\\"] = new_label\",\n \"def _add_new_labels(running_lbls, current_lbls):\\n if running_lbls is None:\\n return current_lbls[:] # copy!\\n elif running_lbls != current_lbls:\\n for lbl in current_lbls:\\n if lbl not in running_lbls:\\n running_lbls.append(lbl)\\n return running_lbls\",\n \"def the_following_message_there_are_unapplied_network_changes_apply_them_now_unapplied_changes_will_be_lost_should_appear(driver):\\n wait_on_element(driver, 0.5, 30, '//button[contains(.,\\\"TEST CHANGES\\\")]')\\n driver.find_element_by_xpath('//button[contains(.,\\\"TEST CHANGES\\\")]').click()\",\n \"def test_issue_search_issues(self):\\n pass\",\n \"def fix_label_names():\\n\\n assert trace.cpu.trace_done\\n binary_addr = memorymanager.BinaryAddr(0)\\n while binary_addr < len(classifications):\\n c = classifications[binary_addr]\\n if c is not None:\\n dummy = [str(x) for x in c.as_string_list(binary_addr, None)]\\n binary_addr += c.length()\\n else:\\n binary_addr += 1\",\n \"def propagate_labels(image,labels,conflict=0):\\n rlabels,_ = label(image)\\n cors = correspondences(rlabels,labels,False)\\n outputs = zeros(amax(rlabels)+1,'i')\\n oops = -(1<<30)\\n for o,i in cors.T:\\n if outputs[o]!=0: outputs[o] = oops\\n else: outputs[o] = i\\n outputs[outputs==oops] = conflict\\n outputs[0] = 0\\n return outputs[rlabels]\",\n \"def backlog_milestone():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"project\\\", help=\\\"name of the project\\\")\\n parser.add_argument(\\\"milestone\\\", help=\\\"name of the milestone\\\")\\n\\n args = parser.parse_args()\\n\\n session = GithubSession()\\n\\n project_data = session.get_project(args.project)\\n\\n milestone_data = session.get_milestone(args.milestone)\\n milestone_title = milestone_data[\\\"title\\\"]\\n\\n backlog_data = session.get_column(project_data, \\\"backlog\\\")\\n icebox_data = session.get_column(project_data, \\\"icebox\\\")\\n\\n results = session.search(f'repo:openslate/openslate milestone:\\\"{milestone_title}\\\"')\\n for search_data in results[\\\"items\\\"]:\\n issue_data = get_issue(search_data[\\\"number\\\"]).issue\\n issue_card = session.get_card(project_data, issue_data)\\n\\n if issue_card[\\\"column_url\\\"] == icebox_data[\\\"url\\\"]:\\n session.move_card(issue_card, backlog_data)\\n\\n print(\\\".\\\", end=\\\"\\\")\",\n \"def replaceLabels(self, old, new):\\n for c in self.iterCurves():\\n c.update({'label': c.getAttribute('label').replace(old, new)})\",\n \"def process_not_mtn_bug(self,\\n bug,\\n project_name,\\n old_milestone_name,\\n new_milestone):\\n errors = self.add_target_to_bug(bug, project_name, new_milestone)\\n\\n new_status = \\\"Won't Fix\\\"\\n self.logging.debug(\\\"Update bug #%s status to '%s' for milestone: %s\\\",\\n bug.bug.id, new_status, old_milestone_name)\\n if not self.is_debug() and not errors:\\n bug.status = new_status\\n try:\\n bug.lp_save()\\n except Exception as exc: # pylint: disable=W0703\\n errors = True\\n self.logging.error(\\n \\\"Can't update bug #%s status to '%s' for milestone: %s\\\",\\n bug.bug.id,\\n new_status,\\n old_milestone_name\\n )\\n self.logging.exception(exc)\\n\\n if not errors:\\n self.get_stats()[project_name][old_milestone_name]['migrated'] += 1\\n self.increase_proccessed_issues()\\n else:\\n self.logging.error(\\\"Can't reassign the bug #%s.\\\", bug.bug.id)\",\n \"def set_action_notification_text(apps, schema_editor):\\n Action = apps.get_model(\\\"goals\\\", \\\"Action\\\")\\n for action in Action.objects.all():\\n text = action.notification_text\\n if text and text[0].isupper() and not text[1].isupper():\\n text = \\\"{}{}\\\".format(text[0:1].lower(), text[1:])\\n action.notification_text = \\\"You can {}\\\".format(text)\\n action.save()\",\n \"def parse_status_file(jira, filename, issues):\\n # Regexp to match Jira issue on a single line, i.e:\\n # [SWG-28]\\n # [LITE-32]\\n # ...\\n regex = r\\\"^\\\\[([A-Z]+-[0-9]+).*\\\\]\\\\n$\\\"\\n\\n # Regexp to match a tag that indicates we should stop processing, ex:\\n # [STOP]\\n # [JIPDATE-STOP]\\n # [OTHER]\\n # ...\\n regex_stop = r\\\"^\\\\[.*\\\\]\\\\n$\\\"\\n\\n # Regexp to mach a tag that indicates to stop processing completely:\\n # [FIN]\\n regex_fin = r\\\"^\\\\[FIN\\\\]\\\\n$\\\"\\n\\n # Regexp to match for a status update, this will remove 'Status' from the\\n # match:\\n regex_status = r\\\"(?:^Status:) *(.+)\\\\n$\\\"\\n\\n # Contains the status text, it could be a file or a status email\\n status = \\\"\\\"\\n\\n # Regexp to match for a time spent update, this will remove 'Time spent:'\\n # from the match:\\n regex_timespent = r\\\"(^Time spent:) \\\\d+\\\\w\\\\n$\\\"\\n\\n # List of resolutions (when doing a transition to Resolved). Query once globally.\\n resolution_map = dict([(t.name.title(), t.id) for t in jira.resolutions()])\\n\\n with open(filename) as f:\\n status = f.readlines()\\n\\n myissue = \\\"\\\"\\n mycomment = \\\"\\\"\\n\\n # build list of {issue,comment} tuples found in status\\n issue_comments = []\\n for line in status:\\n # New issue?\\n match = re.search(regex, line)\\n\\n # Evaluate and save the transition regex for later. We have to do this\\n # here, since we cannot assign and save the variable in the if\\n # construction as you can do in C for example.\\n transition = re.search(regex_status, line)\\n\\n if match:\\n myissue = match.group(1)\\n validissue = True\\n\\n # if we ran a query, we might already have fetched the issue\\n # let's try to find the issue there first, otherwise ask Jira\\n try:\\n issue = [x for x in issues if str(x) == myissue][0]\\n issue_comments.append((issue, \\\"\\\", \\\"\\\", None))\\n\\n # IndexError: we had fetched already, but issue is not found\\n # TypeError: issues is None, we haven't queried Jira yet, at all\\n except (IndexError, TypeError) as e:\\n try:\\n issue = jira.issue(myissue)\\n issue_comments.append((issue, \\\"\\\", \\\"\\\", None))\\n except Exception as e:\\n if \\\"Issue Does Not Exist\\\" in e.text:\\n print(\\\"[{}] : {}\\\".format(myissue, e.text))\\n validissue = False\\n\\n # Stop parsing entirely. This needs to be placed before regex_stop\\n # or the .* will match and [FIN] won't be processed\\n elif re.search(regex_fin, line):\\n break\\n # If we have non-JIRA issue tags, stop parsing until we find a valid tag\\n elif re.search(regex_stop, line):\\n validissue = False\\n elif transition and validissue:\\n # If we have a match, then the new status should be first in the\\n # group. Jira always expect the name of the state transitions to be\\n # word capitalized, hence the call to the title() function. This\\n # means that it doesn't matter if the user enter all lower case,\\n # mixed or all upper case. All of them will work.\\n new_status = transition.groups()[0].title()\\n (i, c, _, ts) = issue_comments[-1]\\n issue_comments[-1] = (i, c, new_status, ts)\\n elif re.search(regex_timespent, line, re.IGNORECASE):\\n timespent = line.split(\\\":\\\")[1].strip()\\n (i, c, t, _) = issue_comments[-1]\\n issue_comments[-1] = (i, c, t, timespent)\\n else:\\n # Don't add lines with comments\\n if line[0] != \\\"#\\\" and issue_comments and validissue:\\n (i, c, t, ts) = issue_comments[-1]\\n issue_comments[-1] = (i, c + line, t, ts)\\n\\n issue_upload = []\\n print(\\\"These JIRA cards will be updated as follows:\\\\n\\\")\\n for idx, t in enumerate(issue_comments):\\n (issue, comment, transition, timespent) = issue_comments[idx]\\n\\n # Strip beginning and trailing blank lines\\n comment = comment.strip(\\\"\\\\n\\\")\\n\\n # initialize here to avoid unassigned variables and useless code complexity\\n resolution_id = transition_id = None\\n resolution = transition_summary = \\\"\\\"\\n\\n if transition != \\\"\\\" and transition != str(issue.fields.status):\\n # An optional 'resolution' attribute can be set when doing a transition\\n # to Resolved, using the following pattern: Resolved / <resolution>\\n if (\\n transition.startswith(\\\"Resolved\\\") or transition.startswith(\\\"Closed\\\")\\n ) and \\\"/\\\" in transition:\\n (transition, resolution) = map(str.strip, transition.split(\\\"/\\\"))\\n if not resolution in resolution_map:\\n print(\\n 'Invalid resolution \\\"{}\\\" for issue {}'.format(resolution, issue)\\n )\\n print(\\\"Possible resolution: {}\\\".format([t for t in resolution_map]))\\n sys.exit(1)\\n resolution_id = resolution_map[resolution]\\n\\n transition_map = dict(\\n [(t[\\\"name\\\"].title(), t[\\\"id\\\"]) for t in jira.transitions(issue)]\\n )\\n if not transition in transition_map:\\n print('Invalid transition \\\"{}\\\" for issue {}'.format(transition, issue))\\n print(\\\"Possible transitions: {}\\\".format([t for t in transition_map]))\\n sys.exit(1)\\n\\n transition_id = transition_map[transition]\\n if resolution:\\n transition_summary = \\\" %s => %s (%s)\\\" % (\\n issue.fields.status,\\n transition,\\n resolution,\\n )\\n else:\\n transition_summary = \\\" %s => %s\\\" % (issue.fields.status, transition)\\n\\n if comment == \\\"\\\" and not transition_id:\\n log.debug(\\n \\\"Issue [%s] has no comment or transitions, not updating the issue\\\"\\n % (issue)\\n )\\n continue\\n\\n issue_upload.append(\\n (\\n issue,\\n comment,\\n {\\\"transition\\\": transition_id, \\\"resolution\\\": resolution_id},\\n timespent,\\n )\\n )\\n print(\\n \\\"[%s]%s\\\\n %s\\\"\\n % (issue, transition_summary, \\\"\\\\n \\\".join(comment.splitlines()))\\n )\\n if timespent:\\n print(\\\" Time spent: %s\\\" % timespent)\\n print(\\\"\\\")\\n\\n issue_comments = issue_upload\\n if issue_comments == [] or cfg.args.dry_run or should_update() == \\\"n\\\":\\n if issue_comments == []:\\n print(\\\"No change, Jira was not updated!\\\\n\\\")\\n else:\\n print(\\\"Comments will not be written to Jira!\\\\n\\\")\\n if not cfg.args.s:\\n print_status(status)\\n sys.exit()\\n\\n # if we found something, let's update jira\\n for issue, comment, transition, timespent in issue_comments:\\n update_jira(jira, issue, comment, transition, timespent)\\n\\n print(\\\"Successfully updated your Jira tickets!\\\\n\\\")\\n if not cfg.args.s:\\n print_status(status)\",\n \"def process_text(text):\\n fix_dict = {'fig.': 'fig', 'fig .': 'fig ', 'Fig.': 'Fig', 'Fig .': 'Fig ',\\n 'figure.': 'figure', 'figure .': 'figure ', 'Figure.': 'Fig', 'Figure .': 'Fig ',\\n 'et al.': 'et al', 'III': '3', 'II': '2', 'I': '1'}\\n\\n for old_pattern in fix_dict.keys():\\n text = text.replace(old_pattern, fix_dict[old_pattern])\\n return text\",\n \"def process_bug_links(text):\\n text = LP_RE.sub(r\\\"[\\\\1](https://bugs.launchpad.net/bugs/\\\\2)\\\", text)\\n text = BDO_RE.sub(r\\\"[\\\\1](https://bugs.debian.org/\\\\2)\\\", text)\\n text = ISSUE_RE.sub(r\\\"[\\\\1](https://github.com/gammu/gammu/\\\\2)\\\", text)\\n return BUG_RE.sub(r\\\"[\\\\1](https://bugs.cihar.com/\\\\2)\\\", text)\",\n \"def play_level(correct_words, blanks, sentence):\\n print sentence\\n for index in range(4):\\n check_word(correct_words[index], blanks[index])\\n sentence = sentence.replace(blanks[index], correct_words[index])\\n print sentence\",\n \"def test_correct_labels(self):\\n timeseries = self.build_timeseries()\\n\\n metric_labels_list = main.get_labels(timeseries[\\\"metric\\\"],\\\"labels\\\") \\n expected_metric_labels_list = self.build_metric_labels()\\n self.assertEqual(sorted(metric_labels_list), sorted(expected_metric_labels_list))\\n\\n resource_labels_list = main.get_labels(timeseries[\\\"resource\\\"],\\\"labels\\\")\\n expected_resource_labels_list = self.build_resource_labels()\\n self.assertEqual(sorted(resource_labels_list), sorted(expected_resource_labels_list))\\n\\n user_labels_list = main.get_labels(self.build_user_labels_request(),\\\"userLabels\\\")\\n expected_user_labels_list = self.build_expected_user_labels_response()\\n self.assertEqual(sorted(user_labels_list), sorted(expected_user_labels_list))\\n\\n system_labels_list = main.get_system_labels(self.build_user_labels_request(),\\\"systemLabels\\\")\\n expected_system_labels_list = self.build_expected_system_labels_response()\\n self.assertEqual(sorted(system_labels_list), sorted(expected_system_labels_list))\",\n \"def handle_labels(ls):\\r\\n\\r\\n # assign each line a number\\r\\n line_num = {}\\r\\n counter = 0\\r\\n for i in ls:\\r\\n if not i.startswith('('):\\r\\n line_num[i] = counter\\r\\n counter += 1\\r\\n else:\\r\\n sb = i[1:-1]\\r\\n line_num[sb] = counter\\r\\n\\r\\n # replace @XXX with number\\r\\n var_address = 16\\r\\n mem = {}\\r\\n for i in range(len(ls)):\\r\\n if ls[i].startswith('@'):\\r\\n # if @XXX is already in numeral form, do nothing\\r\\n if ls[i][1:].isdigit():\\r\\n pass\\r\\n\\r\\n # replace with pre-defined symbols if found\\r\\n elif pre_defined_sb.get(ls[i][1:]) is not None:\\r\\n ls[i] = '@' + pre_defined_sb[ls[i][1:]]\\r\\n\\r\\n # replace by (XXX) line number if search failed\\r\\n elif line_num.get(ls[i][1:]) is not None:\\r\\n ls[i] = '@' + str(line_num[ls[i][1:]])\\r\\n\\r\\n # else must be user defined variable\\r\\n # assign same address for same variable\\r\\n else:\\r\\n if ls[i] not in mem:\\r\\n mem[ls[i]] = '@' + str(var_address)\\r\\n ls[i] = '@' + str(var_address)\\r\\n var_address += 1\\r\\n else:\\r\\n ls[i] = mem[ls[i]]\\r\\n\\r\\n # remove (XXX)'s\\r\\n ls = list(filter(lambda x: not x.startswith('('), ls))\\r\\n\\r\\n return ls\",\n \"def update_label(self, repository, name, color, old_name=None, **kwargs):\\n data = {'name': name, 'color': color}\\n response = self.session.patch(\\n '{}/repos/{}/labels/{}'.format(\\n self.GH_API_ENDPOINT, repository, old_name or name\\n ),\\n json=data\\n )\\n if response.status_code != 200:\\n raise GitHubError(response)\",\n \"def check_indicator_files(tasks):\\n\\n for task in tasks:\\n if task[\\\"status\\\"]==\\\"unknown\\\":\\n if os.path.exists(task[\\\"result\\\"]):\\n task[\\\"status\\\"]=\\\"previously completed\\\"\\n else:\\n task[\\\"status\\\"]=\\\"to do\\\"\\n return\",\n \"def label(cmd):\\r\\n cmd = cmd.replace('make][.DP', 'make1][.NP')\\r\\n cmd = cmd.replace('make][.SC', 'make2][.SC')\\r\\n cmd = re.sub('(draw.*)one','\\\\\\\\1one1',cmd)\\r\\n cmd = re.sub('(make1.*)one','\\\\\\\\1one1',cmd)\\r\\n cmd = re.sub('(make2.*)one','\\\\\\\\1one2',cmd)\\r\\n cmd = re.sub('(move.*)one','\\\\\\\\1one2',cmd)\\r\\n cmd = re.sub('(hide.*)one','\\\\\\\\1one2',cmd)\\r\\n cmd = '[result ' + cmd + ']' #dummy function for plop\\r\\n return cmd\",\n \"def label(cmd):\\n cmd = cmd.replace('make][.DP', 'make1][.NP')\\n cmd = cmd.replace('make][.SC', 'make2][.SC')\\n cmd = re.sub('(draw.*)one','\\\\\\\\1one1',cmd)\\n cmd = re.sub('(make1.*)one','\\\\\\\\1one1',cmd)\\n cmd = re.sub('(make2.*)one','\\\\\\\\1one2',cmd)\\n cmd = re.sub('(move.*)one','\\\\\\\\1one2',cmd)\\n cmd = re.sub('(hide.*)one','\\\\\\\\1one2',cmd)\\n cmd = '[result ' + cmd + ']' #dummy function for plop\\n return cmd\",\n \"def confirm_replace( scr ):\\n d = ConfirmReplaceDialog(scr)\\n value = d.main()\\n if \\\"yes\\\" in value:\\n if value[\\\"yes\\\"]:\\n return 1\\n elif value[\\\"no\\\"]:\\n return 2\\n elif value[\\\"all\\\"]:\\n return 3\\n elif value[\\\"cancel\\\"]:\\n return 4\\n else:\\n return 4\",\n \"def tag(issue):\\n # assert check_tags_exist(issue, _REQUIRED_TAGS)\\n print(\\\"Tagging %s...\\\" %issue.filename)\\n\\n # Labels rows.\\n issue = copy.deepcopy(issue)\\n for index, row in issue.tags_df.iterrows():\\n issue.tags_df.loc[index, \\\"jump\\\"] = '0'\\n\\n # If text is not null then search for JUMP.\\n if not pd.isnull(row.text):\\n text = row.text.strip()\\n jump = _has_page_jump(text)\\n if jump:\\n issue.tags_df.loc[index, \\\"jump\\\"] = str(jump)\\n\\n # If 'function' column is not null set based on number of words.\\n if pd.isnull(row.function):\\n is_ME = len(text.split()) < 5\\n issue.tags_df.loc[index, \\\"function\\\"] = \\\"ME\\\" if is_ME else \\\"TXT\\\"\\n return issue\",\n \"def _MaybeAddLabel(label_name):\\n if label_name.lower() in labels_already_seen:\\n return\\n labels_already_seen.add(label_name.lower())\\n if '-' in label_name:\\n col, _value = label_name.split('-', 1)\\n _MaybeAddCol(col)\",\n \"def test_fallback_status_emoji(self):\\n self.assertEqual(get_emoji_for_status(None), \\\"\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7193442","0.67526186","0.63840276","0.6223574","0.62127376","0.5960943","0.5946542","0.58994776","0.58699584","0.58600855","0.5739906","0.57178646","0.56967795","0.56701887","0.56290764","0.5619469","0.5574466","0.55743533","0.554854","0.54189855","0.53522754","0.52421373","0.5226149","0.5189689","0.5160639","0.5153668","0.51222146","0.51184464","0.5094399","0.5087239","0.5078208","0.5066869","0.50570315","0.5056278","0.50516576","0.5041236","0.5031564","0.5031224","0.5017236","0.500047","0.49944058","0.4989992","0.4989633","0.4986107","0.4952166","0.49488676","0.49395898","0.49363738","0.49303788","0.49129075","0.49124146","0.48977855","0.48871619","0.48673174","0.4860882","0.48532808","0.48480576","0.4841362","0.4827759","0.48269388","0.48131707","0.48075822","0.4805436","0.47804722","0.47639593","0.47543558","0.4741585","0.474001","0.47381648","0.47322577","0.47281808","0.47260958","0.47219867","0.4708501","0.46897352","0.46893722","0.46859387","0.467135","0.4671122","0.46631786","0.46605352","0.4657191","0.46465266","0.46308243","0.46218097","0.4618838","0.46049055","0.4603669","0.46011683","0.45968133","0.45911616","0.4584341","0.45732278","0.45712316","0.45690957","0.45660397","0.45645145","0.4564247","0.45610887","0.45610622"],"string":"[\n \"0.7193442\",\n \"0.67526186\",\n \"0.63840276\",\n \"0.6223574\",\n \"0.62127376\",\n \"0.5960943\",\n \"0.5946542\",\n \"0.58994776\",\n \"0.58699584\",\n \"0.58600855\",\n \"0.5739906\",\n \"0.57178646\",\n \"0.56967795\",\n \"0.56701887\",\n \"0.56290764\",\n \"0.5619469\",\n \"0.5574466\",\n \"0.55743533\",\n \"0.554854\",\n \"0.54189855\",\n \"0.53522754\",\n \"0.52421373\",\n \"0.5226149\",\n \"0.5189689\",\n \"0.5160639\",\n \"0.5153668\",\n \"0.51222146\",\n \"0.51184464\",\n \"0.5094399\",\n \"0.5087239\",\n \"0.5078208\",\n \"0.5066869\",\n \"0.50570315\",\n \"0.5056278\",\n \"0.50516576\",\n \"0.5041236\",\n \"0.5031564\",\n \"0.5031224\",\n \"0.5017236\",\n \"0.500047\",\n \"0.49944058\",\n \"0.4989992\",\n \"0.4989633\",\n \"0.4986107\",\n \"0.4952166\",\n \"0.49488676\",\n \"0.49395898\",\n \"0.49363738\",\n \"0.49303788\",\n \"0.49129075\",\n \"0.49124146\",\n \"0.48977855\",\n \"0.48871619\",\n \"0.48673174\",\n \"0.4860882\",\n \"0.48532808\",\n \"0.48480576\",\n \"0.4841362\",\n \"0.4827759\",\n \"0.48269388\",\n \"0.48131707\",\n \"0.48075822\",\n \"0.4805436\",\n \"0.47804722\",\n \"0.47639593\",\n \"0.47543558\",\n \"0.4741585\",\n \"0.474001\",\n \"0.47381648\",\n \"0.47322577\",\n \"0.47281808\",\n \"0.47260958\",\n \"0.47219867\",\n \"0.4708501\",\n \"0.46897352\",\n \"0.46893722\",\n \"0.46859387\",\n \"0.467135\",\n \"0.4671122\",\n \"0.46631786\",\n \"0.46605352\",\n \"0.4657191\",\n \"0.46465266\",\n \"0.46308243\",\n \"0.46218097\",\n \"0.4618838\",\n \"0.46049055\",\n \"0.4603669\",\n \"0.46011683\",\n \"0.45968133\",\n \"0.45911616\",\n \"0.4584341\",\n \"0.45732278\",\n \"0.45712316\",\n \"0.45690957\",\n \"0.45660397\",\n \"0.45645145\",\n \"0.4564247\",\n \"0.45610887\",\n \"0.45610622\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.46344647"},"document_rank":{"kind":"string","value":"83"}}},{"rowIdx":74589,"cells":{"query":{"kind":"string","value":"Bump the version label of issues from old_label to new_label"},"document":{"kind":"string","value":"def change_issues_label(self, msg, old_label, new_label):\n self._asset_bind(msg)\n yield (\"Processing....\")\n trans = self._translation_util(msg)\n client = self._github_operator(msg)\n cmd = \"repo:{} label:{} is:open type:issue\".format(\n task_repository_name(), old_label)\n issue_list = client.search_issue(cmd, 10)\n for issue in issue_list:\n trans.wait_for_limit(MAX_RESULT, MAX_RESULT)\n issue.remove_from_labels(old_label)\n issue.add_to_labels(new_label)\n yield \"{} issues has been changed label from {} to {}\".format(len(issue_list), old_label, new_label)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_issue_replace_labels(self):\n pass","def post_issue_labels(repo, project, issue_number):\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\n\n new_label_ids = request.json\n\n current_label_ids = dao.get_issue_label_ids(issue_path)\n\n revised_label_ids = current_label_ids + new_label_ids\n\n dao.set_issue_label_ids(issue_path, revised_label_ids)\n\n return if_found(dao.get_issue_labels(issue_path))","def patch_issue_labels(repo, project, issue_number):\n add_label_ids = request.json.get('addLabelIds') or []\n remove_label_ids = request.json.get('removeLabelIds') or []\n\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\n\n current_label_ids = dao.get_issue_label_ids(issue_path)\n\n revised_label_ids = [label_id for label_id in current_label_ids + add_label_ids\n if label_id not in remove_label_ids]\n\n dao.set_issue_label_ids(issue_path, revised_label_ids)\n\n return if_found(dao.get_issue_labels(issue_path))","def update_label(self, repository, name, color, old_name=None, **kwargs):\n data = {'name': name, 'color': color}\n response = self.session.patch(\n '{}/repos/{}/labels/{}'.format(\n self.GH_API_ENDPOINT, repository, old_name or name\n ),\n json=data\n )\n if response.status_code != 200:\n raise GitHubError(response)","def _UpdateLabels(self, args, migration_job, update_fields):\n add_labels = labels_util.GetUpdateLabelsDictFromArgs(args)\n remove_labels = labels_util.GetRemoveLabelsListFromArgs(args)\n value_type = self.messages.MigrationJob.LabelsValue\n update_result = labels_util.Diff(\n additions=add_labels,\n subtractions=remove_labels,\n clear=args.clear_labels\n ).Apply(value_type)\n if update_result.needs_update:\n migration_job.labels = update_result.labels\n update_fields.append('labels')","def UpdateLabel(self) -> _n_6_t_0:","def test_issue_add_label(self):\n pass","def test_issue_remove_label(self):\n pass","def test_issue_edit_label(self):\n pass","def label(self, name):\r\n return labels.IssueLabel(self, name)","def test_labels_change(self):\n label_new = factories.LabelFactory(name=\"test_label\",\n object_type='Assessment')\n response = self.api.put(self.assessment, {'labels': [{\n \"name\": label_new.name,\n \"id\": label_new.id\n }]})\n self.assert200(response)\n notifs, notif_data = common.get_daily_notifications()\n updated = notif_data[\"user@example.com\"][\"assessment_updated\"]\n self.assertEqual(len(notifs), 1)\n self.assertEqual(\n updated[self.assessment.id][\"updated_data\"][\"LABELS\"],\n (\"test_label\", \"\")\n )","def changeLabel(nuclideBase, newLabel):\n nuclideBase.label = newLabel\n byLabel[newLabel] = nuclideBase","async def new_label(event, gh, *args, **kwargs):\n if event.data[\"label\"][\"name\"] == TRIVIAL_LABEL:\n issue_number_found = ISSUE_RE.search(\n event.data[\"pull_request\"][\"title\"])\n if issue_number_found:\n status = create_success_status(issue_number_found)\n else:\n status = TRIVIAL_STATUS\n await _post_status(event, gh, status)","def test_issue_create_label(self):\n pass","def upgrade(self, old_version, new_version):\n pass","def test_issue_get_label(self):\n pass","def relabel(self, old: int, new: int) -> None:\n if not (isinstance(old, int) and isinstance(new, int)):\n try:\n old = int(old)\n new = int(new)\n except ValueError:\n msg = f'Expecting integer arguments, got {type(old)} and {type(new)}!'\n raise ValueError(msg)\n\n if new in set(self.infos.keys()):\n msg = f'New label < {new} > is in existing labels {set(self.infos.keys())}!'\n raise ValueError(msg)\n\n # modify corresponding SegmentInfo object\n seginfo = self.infos[old]\n seginfo.label_value = new\n # modify array data\n self.data = relabel(self.data, old, new)\n # propagate state changes\n self._update_state_from_infos()","def rename_label(self, *args):\n return _ida_hexrays.vdui_t_rename_label(self, *args)","def _update_label(self, change: Dict[str, Any]):\n dim = change['owner'].description\n coord = self.controls[dim]['coord'][dim, change['new']]\n self.controls[dim]['label'].value = coord_element_to_string(coord)","def test_issue_list_labels(self):\n pass","def test_unreleased_version_label_string(self):\n\n gitchangelog.file_put_contents(\n \".gitchangelog.rc\",\n \"unreleased_version_label = 'bar'\")\n changelog = w('$tprog \"HEAD^..HEAD\"')\n self.assertNoDiff(\n textwrap.dedent(\"\"\"\\\n bar\n ---\n\n New\n ~~~\n - Begin. [Bob]\n\n\n \"\"\"),\n changelog)","def update_labels(source_repo, service, source_service, destination):\n app = App()\n if source_repo:\n serv = app.get_service(source_service, repo=source_repo)\n else:\n serv = app.guess_service()\n repo_labels = serv.list_labels()\n if not repo_labels:\n print(\"No labels.\")\n return\n\n for repo_for_copy in destination:\n other_serv = app.get_service(service, repo=repo_for_copy)\n changes = other_serv.update_labels(labels=repo_labels)\n\n click.echo(\"{changes} labels of {labels_count} copied to {repo_name}\".format(\n changes=changes,\n labels_count=len(repo_labels),\n repo_name=repo_for_copy\n ))","def rdf_update_labels(rdf, node):\n final_list = []\n for i in node.get_labels():\n # print(i)\n final_list += rdf_get_branch(rdf, i)\n for i in final_list:\n node.add_label(i)","def _update_label(self, outer_pos, inner_pos, new_label):\n r, c = outer_pos\n ir, ic = inner_pos\n self.inner_boards[r][c][ir][ic][\"text\"] = new_label","def add_labels_to_issue(self, installation_id, repo_owner, repo_name,\n issue_num, predictions):\n # take an action if the prediction is confident enough\n if predictions['labels']:\n label_names, label_probabilities = self.filter_specified_labels(repo_owner,\n repo_name,\n predictions)\n else:\n label_names = []\n\n # get the isssue handle\n issue = get_issue_handle(installation_id, repo_owner, repo_name, issue_num)\n\n if label_names:\n # create message\n message = \"\"\"Issue-Label Bot is automatically applying the labels `{labels}` to this issue, with the confidence of {confidence}.\n Please mark this comment with :thumbsup: or :thumbsdown: to give our bot feedback!\n Links: [app homepage](https://github.com/marketplace/issue-label-bot), [dashboard]({app_url}data/{repo_owner}/{repo_name}) and [code](https://github.com/hamelsmu/MLapp) for this bot.\n \"\"\".format(labels=\"`, `\".join(label_names),\n confidence=\", \".join([\"{:.2f}\".format(p) for p in label_probabilities]),\n app_url=self.app_url,\n repo_owner=repo_owner,\n repo_name=repo_name)\n # label the issue using the GitHub api\n issue.add_labels(*label_names)\n logging.info(f'Add `{\"`, `\".join(label_names)}` to the issue # {issue_num}')\n else:\n message = \"\"\"Issue Label Bot is not confident enough to auto-label this issue.\n See [dashboard]({app_url}data/{repo_owner}/{repo_name}) for more details.\n \"\"\".format(app_url=self.app_url,\n repo_owner=repo_owner,\n repo_name=repo_name)\n logging.warning(f'Not confident enough to label this issue: # {issue_num}')\n\n # make a comment using the GitHub api\n comment = issue.create_comment(message)","def update_labels(self,label_dict):\n \t\tfor key in self.deps:\n \t\t\tfor dependent in self.deps[key]:\n \t\t\t\tlabel = dependent[1]\n \t\t\t\tlabel_dict[label] = label_dict.get(label,0) + 1\n \t\treturn label_dict","def ResetReviewLabels(host, change, label, value='0', revision='current',\n message=None, notify=None):\n # This is tricky when working on the \"current\" revision, because there's\n # always the risk that the \"current\" revision will change in between API\n # calls. So, the code dereferences the \"current\" revision down to a literal\n # sha1 at the beginning and uses it for all subsequent calls. As a sanity\n # check, the \"current\" revision is dereferenced again at the end, and if it\n # differs from the previous \"current\" revision, an exception is raised.\n current = (revision == 'current')\n jmsg = GetChangeDetail(\n host, change, o_params=['CURRENT_REVISION', 'CURRENT_COMMIT'])\n if current:\n revision = jmsg['current_revision']\n value = str(value)\n path = '%s/revisions/%s/review' % (_GetChangePath(change), revision)\n message = message or (\n '%s label set to %s programmatically by chromite.' % (label, value))\n for review in jmsg.get('labels', {}).get(label, {}).get('all', []):\n if str(review.get('value', value)) != value:\n body = {\n 'message': message,\n 'labels': {label: value},\n 'on_behalf_of': review['_account_id'],\n }\n if notify:\n body['notify'] = notify\n response = FetchUrlJson(host, path, reqtype='POST', body=body)\n if str(response['labels'][label]) != value:\n username = review.get('email', jmsg.get('name', ''))\n raise GOBError(200, 'Unable to set %s label for user \"%s\"'\n ' on change %s.' % (label, username, change))\n if current:\n new_revision = GetChangeCurrentRevision(host, change)\n if not new_revision:\n raise GOBError(\n 200, 'Could not get review information for change \"%s\"' % change)\n elif new_revision != revision:\n raise GOBError(200, 'While resetting labels on change \"%s\", '\n 'a new patchset was uploaded.' % change)","def test_issue_get_labels(self):\n pass","def update_label(label1, label2, idx):\n for i in range(0, len(idx)):\n label1[i] = label2[idx[i]]\n return label1","def replaceLabels(self, old, new):\n for c in self.iterCurves():\n c.update({'label': c.getAttribute('label').replace(old, new)})","def bump(self, *, rc: int = 0) -> str:\n try:\n current = Version(self.pkg_version)\n assert isinstance(current, Version)\n # bump the version\n self.pkg_version = str(bump_version(post_bump=True, current=current, rc=rc))\n except Exception as e: # pragma: no cover\n log.error(f\"Error: {e}\")\n return self.pkg_version","def change_labels(labels, cluster_name, idx_to_change, target_labels):\n assert(type(idx_to_change) == list)\n assert(type(target_labels) == list)\n assert(len(idx_to_change) == len(target_labels))\n\n sub_list = labels[labels == cluster_name]\n\n for idx, target in zip(idx_to_change, target_labels):\n sub_list[idx] = target\n\n labels[labels == cluster_name] = sub_list\n\n return labels","def updatelabel(task, label, eid):\n ServerManager.get()\n result = ServerManager.api.update_property(task, eid, prop='label', value=label)\n if result.response_type == 'success':\n click.echo(click.style(result.message, fg='green'))\n else:\n click.echo(click.style(result.message, fg='red'))","def test_issue_delete_label(self):\n pass","def rename(self, label_value: int, new_name: str) -> None:\n seginfo = self.infos[label_value]\n seginfo.name = new_name\n # propagate state changes\n self._update_state_from_infos()","def set_labels(repo: Repository, labels: list[Label]):\n\n log.info(f\"Fetching existing labels from {repo.full_name}\")\n existing_labels = {label.name.casefold(): label for label in repo.get_labels()}\n log.info(f\"Found {len(existing_labels)} existing labels\")\n\n for label in labels:\n qualified_name = label.qualified_name\n folded_name = qualified_name.casefold()\n if folded_name not in existing_labels:\n log.info(f\"Creating label {qualified_name}\")\n repo.create_label(**label.api_arguments)\n elif label != existing_labels[folded_name]:\n log.info(f\"Updating label {qualified_name}\")\n existing_label = existing_labels[folded_name]\n existing_label.edit(**label.api_arguments)\n else:\n log.info(f\"Label {qualified_name} already exists\")","def set_labels(self, new_labels=None):\n self.labels = new_labels","def projects_label(args):\n session = GithubSession()\n\n label_datas = list(session.get_labels())\n\n team = args.team\n team_label_data = None\n if team:\n team_label = utils.get_label(team, prefix=\"team\")\n team_label_data = [x for x in label_datas if x[\"name\"] == team_label][0]\n\n # get the project label\n project_label = utils.get_label(args.name, prefix=\"project\")\n project_label_data = [x for x in label_datas if x[\"name\"] == project_label][0]\n\n print(f\"label cards in project {args.name} column {args.column}\")\n\n project_board = session.get_project(args.name)\n project_backlog_grooming = session.get_column(project_board, \"backlog grooming\")\n\n cards = list(session.get_cards(project_backlog_grooming))\n for card_data in cards:\n issue_number = utils.get_issue_number_from_card_data(card_data)\n\n print(issue_number)\n\n # add the project label\n session.add_label(project_label_data, number=issue_number)\n\n if team_label_data:\n session.add_label(team_label_data, number=issue_number)","def test_unreleased_version_label_callable(self):\n\n gitchangelog.file_put_contents(\n \".gitchangelog.rc\",\n \"unreleased_version_label = lambda : 'foo'\")\n changelog = w('$tprog \"HEAD^..HEAD\"')\n self.assertNoDiff(\n textwrap.dedent(\"\"\"\\\n foo\n ---\n\n New\n ~~~\n - Begin. [Bob]\n\n\n \"\"\"),\n changelog)","def UpdatePipelineToolStatusLabel( gui, statusMessage ):\n gui.pipelineToolStatusLabel.setText( statusMessage )","def update_tips(self, refname, old, new):","def update_label(self, uuid, name):\n payload = self._build_params(uuid=uuid, name=name)\n return Label.deserialize(self._post('labels', None, payload))","def delete_issue_label(repo, project, issue_number, delete_label_id):\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\n\n current_label_ids = dao.get_issue_label_ids(issue_path)\n\n revised_label_ids = [label_id for label_id in current_label_ids\n if label_id != delete_label_id]\n\n dao.set_issue_label_ids(issue_path, revised_label_ids)\n\n return if_found(dao.get_issue_labels(issue_path))","def bump_version(ctx, branch, semantic):\n\n try:\n log.echo('Bumping version...', break_line=False)\n bump = ctx.obj.github.bump_version(branch=branch, semantic=semantic)\n log.checkmark()\n log.echo('Bumped version from {} to {}'.format(bump.prev_version, bump.next_version))\n except BaseException as _:\n log.xmark()\n raise","def rename_issue(self, msg, chat):\n msg, text = self.split_message(msg)\n\n if text == '':\n self.url_handler.send_message(\"\"\"\n You want to modify the issue {},\n but you didn't provide any new text\n \"\"\".format(msg), chat)\n return\n\n payload = \"{\\n \\\"title\\\": \\\"\"+text+\"\\\",\\n \\\"labels\\\": [\\n \\\"telegram\\\"\\n ]\\n}\"\n print(payload)\n headers = {\n 'Content-Type': \"application/json\",\n 'Authorization': \"Basic WWVzdGVyZGF5WW91U2FpZFRvbW9ycm93Qm90Olllc3RlcmRheVlvdVNhaWRUb21vcnJvdw==\",\n 'Cache-Control': \"no-cache\",\n 'Postman-Token': \"49817fa1-698d-496d-b6e3-252e81bc792f\"\n }\n result = requests.request(\"GET\", constants.URL_GITHUB + '/' + msg)\n result_json = result.json()\n print(result_json)\n try:\n result_json['state']\n except:\n return self.url_handler.send_message(\"Issue does not exist\", chat)\n\n requests.request(\"POST\", constants.URL_GITHUB+'/'+msg, data=payload, headers=headers)\n return self.url_handler.send_message(\"Issue renamed {}\".format(text), chat)","def on_change(self, attr, old, new):\n n = self.labels.index(new) + 1 # Select 0-indexed\n self.notify(set_figures(n))","def put(self, id):\n context = request.environ.get('context')\n resp = dbapi.netdevices_labels_update(context, id, request.json)\n response = {\"labels\": list(resp.labels)}\n return response, 200, None","def labels(self):\r\n return labels.IssueLabels(self)","def update_ref_label_by_prediction(\n ref_label: torch.Tensor,\n pre_label: torch.Tensor) -> torch.Tensor:\n ref_label = utils.update_ref_label_with_mask(\n ref_label, pre_label == 1, utils.REF_POSSIBLY_FOREGROUND)\n ref_label = utils.update_ref_label_with_mask(\n ref_label, pre_label == 0, utils.REF_POSSIBLY_BACKGROUND)\n return ref_label","def label_milestone_issues():\n session = GithubSession()\n\n labels = list(session.get_labels())\n labels_by_name = dict([(x['name'], x) for x in labels])\n\n milestones = list(session.get_milestones())\n\n for milestone in milestones:\n label_data = labels_by_name[f'epic:{milestone[\"title\"].strip()}']\n\n for issue in session.get_issues(milestone=milestone[\"number\"], state='all'):\n session.add_label(label_data, number=issue['number'])","def update_versions(new_py_version: str) -> None:\n current_py_version = get_current_py_version()\n current_py_version_without_suffix = replace_dev_suffix_with(current_py_version, \"\")\n\n # Python\n replace_occurrences(\n files=[Path(\"mlflow\", \"version.py\")],\n pattern=re.escape(current_py_version),\n repl=new_py_version,\n )\n # JS\n replace_occurrences(\n files=[\n Path(\n \"mlflow\",\n \"server\",\n \"js\",\n \"src\",\n \"common\",\n \"constants.tsx\",\n )\n ],\n pattern=re.escape(current_py_version),\n repl=new_py_version,\n )\n\n # Java\n for java_extension in [\"xml\", \"java\"]:\n replace_occurrences(\n files=Path(\"mlflow\", \"java\").rglob(f\"*.{java_extension}\"),\n pattern=rf\"{re.escape(current_py_version_without_suffix)}(-SNAPSHOT)?\",\n repl=replace_dev_suffix_with(new_py_version, \"-SNAPSHOT\"),\n )\n\n # R\n replace_occurrences(\n files=[Path(\"mlflow\", \"R\", \"mlflow\", \"DESCRIPTION\")],\n pattern=f\"Version: {re.escape(current_py_version_without_suffix)}\",\n repl=f\"Version: {replace_dev_suffix_with(new_py_version, '')}\",\n )","def set_base_image_labels(driver, user_disk, img_name, branch, target):\n\n dashes = [i for i, c in enumerate(img_name) if c=='-']\n cf_version = img_name[dashes[0]+1:dashes[3]]\n build_id = img_name[dashes[-1]+1:]\n\n driver.ex_set_volume_labels(user_disk,\n {'cf_version': cf_version, 'branch': branch,\n 'target': target, 'build_id': build_id})","def add_label_to_existing(hostname, new_labels):\n\n logging.debug('going to add labels %s to existing host %s' % (new_labels, hostname))\n\n checkmk_api_url = config['checkmk_api_url']\n checkmk_api_username = config['checkmk_api_username']\n checkmk_api_secret = config['checkmk_api_secret']\n checkmk_puppetdb_label = config['checkmk_puppetdb_label']\n\n # Save the attributes, save the ~world~ existing labels\n req_params = { 'action': 'get_host',\n '_username': config['checkmk_api_username'],\n '_secret': config['checkmk_api_secret'],\n 'hostname': hostname,\n 'output_format': 'json' }\n r = requests.post(checkmk_api_url, req_params)\n\n existing_labels = {}\n try:\n existing_labels.update(r.json()['result']['attributes']['labels'])\n except:\n pass\n\n # add new labels to existing labels and ensure from_puppetdb label present\n existing_labels.update(new_labels)\n existing_labels.update({ 'from_puppetdb': checkmk_puppetdb_label })\n\n payload = {'request': json.dumps({\n 'hostname': hostname,\n 'attributes': {\n 'labels': existing_labels\n }\n })}\n\n logging.debug('-- adding labels %s to host %s' % (existing_labels, hostname))\n r = requests.post(\"%s?action=edit_host&_username=%s&_secret=%s\" % (checkmk_api_url, checkmk_api_username, checkmk_api_secret), data=payload)\n logging.debug('-- got resp code = %d' % r.status_code)\n logging.debug('-- got resp text = %s' % r.text)\n r_json = json.loads(r.text)\n\n # Successful edit_host gives response of {\"result\": null, \"result_code\": 0}\n if r_json['result_code'] == 0 and r_json['result'] is None:\n logging.info('added labels %s to %s successfully' % (existing_labels, hostname))\n else:\n logging.warn('failed to add labels %s to host %s' % (r_json['result'], hostname))","def add_labels(number, labels):\n\n cmds = [github_cli, 'pr', 'edit', str(number)]\n for lab in labels:\n cmds += ['--add-label', lab]\n\n with subprocess.Popen(cmds) as p:\n _, err = p.communicate()\n print(err)","def update_version() -> str:\n cur_version = get_current_version(args.stage)\n\n if args.stage == \"prod\":\n prv_version = get_current_version(stage='staging')\n new_version = semver.finalize_version(prv_version)\n elif args.stage == \"staging\":\n prv_version = get_current_version(stage='integration')\n assert '-integration' in prv_version\n new_version = prv_version.replace('-integration', '-rc') # don't bump the version number\n else:\n new_version = getattr(semver, f'bump_{args.release}')(str(cur_version))\n new_version = new_version if semver.parse_version_info(new_version).prerelease \\\n else semver.bump_prerelease(new_version, token='integration')\n\n if cur_version == new_version:\n print(\"Nothing to promote\")\n exit(0)\n else:\n print(f\"Upgrading: {cur_version} -> {new_version}\")\n return new_version","def bump_oa_release_number(**kwargs):\n\n oa_folder = kwargs['workdir'] + '/openstack-ansible/'\n fpth, cver = get_oa_version(oa_folder)\n LOGGER.info(\"Current version {} in {}\".format(cver, fpth))\n\n if cver == \"master\":\n click.confirm(\"Master should only changed when necessary. Sure?\")\n\n if kwargs['version'] == \"auto\":\n LOGGER.info(\"Guessing next version\")\n cver_l = cver.split(\".\")\n try:\n cver_l[-1] = str(int(cver_l[-1]) + 1)\n except ValueError as vee:\n LOGGER.error(\"Cannot up the version: {}\".format(vee))\n nver = click.prompt(\"New version?\")\n else:\n nver = \".\".join(cver_l)\n else:\n nver = kwargs['version']\n\n for line in fileinput.input(\"{}/{}\".format(oa_folder, fpth),\n inplace=True):\n print(line.replace(\n \"openstack_release: {}\".format(cver),\n \"openstack_release: {}\".format(nver))),\n LOGGER.info(\"Updated the version in repo to {}\".format(nver))\n\n msg = (\"Here is a commit message you could use:\\n\"\n \"Update all SHAs for {new_version}\\n\\n\"\n \"This patch updates all the roles to the latest available stable \\n\"\n \"SHA's, copies the release notes from the updated roles into the \\n\"\n \"integrated repo, updates all the OpenStack Service SHA's, and \\n\"\n \"updates the appropriate python requirements pins. \\n\\n\"\n \"Depends-On: {release_changeid}\").format(\n new_version=os.environ.get('new_version', nver),\n release_changeid=os.environ.get('release_changeid', '<TODO>'))\n\n if kwargs['commit']:\n repo = Repo(oa_folder)\n repo.git.add('.')\n repo.index.commit(msg)\n click.echo(\"Commit done. Please verify before review.\")\n else:\n click.echo(msg)","def fix_label_names():\n\n assert trace.cpu.trace_done\n binary_addr = memorymanager.BinaryAddr(0)\n while binary_addr < len(classifications):\n c = classifications[binary_addr]\n if c is not None:\n dummy = [str(x) for x in c.as_string_list(binary_addr, None)]\n binary_addr += c.length()\n else:\n binary_addr += 1","def add_label(self, new_name, status):\n api_uri = self._uri_dict.get('addLabel')\n data = {\n 'newName': new_name,\n 'status': status\n }\n r_data = self._post(api_uri, data)\n return r_data","def label(self, name):\r\n return labels.RepoLabel(self, name)","def version_changed(\n self, entity: Union[str, dict], attribute: str, old: str, new: str, kwargs: dict\n ) -> None:\n self.log(old)\n self.log(new)\n if new != old:\n app = entity.split(\".\")[1].split(\"_\")[0]\n self.notification_app.notify(\n kind=\"single\",\n level=\"home\",\n title=f\"Neue Version für {app}!\",\n message=f\"Die Version {self.get_state(entity)} \"\n f\"für {app} ist verfügbar.\",\n targets=self.notifications[\"targets\"],\n )","def test_issue_clear_labels(self):\n pass","def update(self, src, labels): # real signature unknown; restored from __doc__\n pass","def swaplabel(self, label_a: int, label_b: int) -> None:\n if not (isinstance(label_a, int) and isinstance(label_b, int)):\n msg = f'Expecting integer arguments, got {type(label_a)} and {type(label_b)}!'\n raise ValueError(msg)\n\n self.infos[label_a].label_value = label_b\n self.infos[label_b].label_value = label_a\n labels = [label_a, label_b]\n # modify array data\n self.data = relabel(self.data, labels, reversed(labels))\n # propagate state changes\n self._update_state_from_infos()","def bump_version(version_number):\n print(f\"Bumping version to: {version_number}\")\n _create_new_changelog_release()\n for filename, replacer in get_files_to_change().items():\n print(\"Bumping version in %s\" % filename)\n with open(filename, 'r') as f:\n contents = f.read()\n if callable(replacer):\n new_contents = replacer(version_number, contents)\n else:\n new_contents = _regex_based_version_bump(\n version_number,\n replacer,\n contents)\n with open(filename, 'w') as f:\n f.write(new_contents)","def setLabel2(*args):","def update_tt_label(video: dict):\n # try to get all text tracks\n if tts := video.get('text_tracks'):\n # go through all tracks\n for track in tts:\n # change the setting\n track['label'] = track['srclang']\n\n # get the video ID\n video_id = video.get('id')\n # create the JSON body\n json_body = { 'text_tracks': tts }\n # make the PATCH call\n r = get_cms().UpdateVideo(video_id=video_id, json_body=json_body)\n # check if all went well\n if r.status_code in [200,202]:\n print(f'Updated track labels for video ID {video_id} with status {r.status_code}.')\n # otherwise report the error\n else:\n print(f'Error code {r.status_code} updating track labels for video ID {video_id}:')\n print(r.text)","def remove_labels(number, labels):\n\n cmds = [github_cli, 'pr', 'edit', str(number)]\n for lab in labels:\n cmds += ['--remove-label', lab]\n\n with subprocess.Popen(cmds) as p:\n _, err = p.communicate()\n print(err)","def propagate_labels(image,labels,conflict=0):\n rlabels,_ = label(image)\n cors = correspondences(rlabels,labels,False)\n outputs = zeros(amax(rlabels)+1,'i')\n oops = -(1<<30)\n for o,i in cors.T:\n if outputs[o]!=0: outputs[o] = oops\n else: outputs[o] = i\n outputs[outputs==oops] = conflict\n outputs[0] = 0\n return outputs[rlabels]","def _update_team_labels(self, host, team_name):\n # host = Host.get(identifier=identifier)\n # function to get labelFingerprint and Labels info from an instance of GCP\n instance = self.get_instance(host.name, host.zone)\n labelFingerprint = instance['labelFingerprint']\n labels = instance['labels']\n\n # function to get information from a team\n team = TeamClient(api_url=TEAM_API_URL, team_name=team_name)\n team = team.team\n\n # add info of new team\n labels['servico_de_negocio'] = team.get('servico-de-negocio')\n labels['cliente'] = team.get('cliente')\n labels['team_slug_name'] = team.get('slug')\n labels['team_id'] = team.get('id')\n\n # create the body of function with Label Fingerprint and Labels\n body = dict()\n body['labels'] = labels\n body['labelFingerprint'] = labelFingerprint\n\n update_label = self.client.instances().setLabels(project=self.credential.project,\n zone=host.zone,\n instance=host.name,\n body=body).execute()\n\n return self.wait_operation(operation=update_label.get('name'),\n zone=host.zone)","def rename(old, new):","def rename(old, new):","def rename(self, group, new_title):\n old_md = '[[{}]]'.format(self.title)\n new_md = '[[{}]]'.format(new_title)\n \n old_html = render_wiki_link(group, self.id, self.title)\n new_html = render_wiki_link(group, self.id, new_title)\n \n # `switch_db(WikiPage, group)` has already been done in `main.wiki_rename_page`.\n for p in self.__class__.objects(refs__contains=self.id).exclude('comments').all():\n p.md = p.md.replace(old_md, new_md)\n p.html = p.html.replace(old_html, new_html)\n p.save()\n \n with switch_db(WikiPageVersion, group) as _WikiPageVersion:\n for pv in _WikiPageVersion.objects.search_text(old_md).all():\n pv.diff = pv.diff.replace(old_md, new_md)\n pv.save()\n with switch_db(WikiCache, group) as _WikiCache:\n _WikiCache.objects(changes_id_title=[self.id, self.title]).\\\n update(set__changes_id_title__S=[self.id, new_title])\n _WikiCache.objects(keypages_id_title=[self.id, self.title]).\\\n update(set__keypages_id_title__S=[self.id, new_title])\n\n self.title = new_title\n self.save()","def relabel_topic(user, map_id, old_topic_text, new_topic_text):\n the_map = get_map(user, map_id)\n topic = the_map.subtopics.pop(old_topic_text)\n topic.text = new_topic_text\n the_map.subtopics[new_topic_text] = topic\n save_map(user, map_id, the_map)","def change_version(new_version):\n global __model\n __model = __ITU1511(new_version)\n utils.memory.clear()","def test_version_rename_error_bad_version(self):\n test_name = sys._getframe().f_code.co_name\n rv, output = self._execute('version rename bad_version changed_name')\n self.assertEqual(2, rv)\n self.assertEqual(self.expected_results[test_name], output)","def update(self):\n\n params = {\n \"title\": self.title,\n \"body\": self.body,\n \"state\": self.state,\n \"labels\": self.labels,\n \"assignees\": self.assignees,\n }\n\n if self.milestone:\n params[\"milestone\"] = self.milestone\n\n resp = yield self.client.request(\n \"{}/issues/{}\".format(self.repo.base_path, self.num),\n params=params, method=\"PATCH\")\n self.c = resp.data\n self.after_sync()\n raise gen.Return(self)","def modify_labels(self, body, update_labels):\n for label_modifier in self.schema.get(\"labels\", []):\n\n # handle the service selector lookup\n spec = label_modifier.get(\"service\")\n if spec is not None:\n try:\n # get the service we're after\n service = self.k8s_inventory.k8s_client.get_service(\n name=spec.get(\"name\"),\n namespace=spec.get(\"namespace\"),\n )\n # reset the existing labels\n update_labels()\n # update with the selectors from the service\n update_labels(**service.spec.selector)\n except:\n return False\n\n # handle the static label modifications\n spec = label_modifier.get(\"label\")\n if spec is not None:\n updates = dict()\n updates[spec.get(\"key\")] = spec.get(\"value\")\n update_labels(**updates)\n\n return body","def test_changeVersions(self):\n self._testVersionChanging(8, 2, 3)","def test_priority_change_error_bad_new_name(self):\n test_name = sys._getframe().f_code.co_name\n rv, output = self._execute('priority change major minor')\n self.assertEqual(2, rv)\n self.assertEqual(self.expected_results[test_name], output)","def rename_value(model: onnx.ModelProto, old_name: str, new_name: str):\n if old_name == new_name:\n return\n logger = get_root_logger()\n logger.info(f'rename {old_name} -> {new_name}')\n for n in model.graph.node:\n for i, output in enumerate(n.output):\n if output == old_name:\n n.output[i] = new_name\n for i, input in enumerate(n.input):\n if input == old_name:\n n.input[i] = new_name\n for v in model.graph.value_info:\n if v.name == old_name:\n v.name = new_name\n for i, input in enumerate(model.graph.input):\n if input.name == old_name:\n input.name = new_name\n for i, output in enumerate(model.graph.output):\n if output.name == old_name:\n output.name = new_name","def test_severity_change_error_bad_new_name(self):\n test_name = sys._getframe().f_code.co_name\n self._execute('severity add major')\n self._execute('severity add critical')\n rv, output = self._execute('severity change critical major')\n self.assertEqual(2, rv)\n self.assertEqual(self.expected_results[test_name], output)","def update_frame_label(self):\n count = len(self.main_frame_list)\n\n for idx in range(count): #Start, count) \n s1 = \"\"\n for i in range(16): #self.main_frame_nibble_list: # 16\n s = \"\"\n for j in range(4):\n s += str(self.main_button_bit_list[idx][i*4 + j].get_label())\n s = s[::-1]\n self.main_frame_nibble_list[idx][i].set_label(str(hex(int(s,2)))[2:].upper())\n s1 += str(self.main_frame_nibble_list[idx][i].get_label())\n s1 = s1[::-1]\n if DEBUG: print(s1[:8] + \" \" + s1[8:])\n self.main_frame_list[idx].set_label(s1[:8] + \" \" + s1[8:])","def increment_milestone_version(old_version, release_type):\n if release_type == \"milestone\":\n if \"b\" in old_version[-1]:\n # Not the first milestone\n new_version_parts = old_version[:-1]\n next_milestone = int(old_version[-1][2:]) + 1\n new_version_parts.append(\"0b{}\".format(next_milestone))\n else:\n new_version_parts = increment_version(old_version, (1, 0, 0))\n new_version_parts.append(\"0b1\")\n elif release_type == \"rc\":\n new_version_parts = old_version[:-1]\n if \"b\" in old_version[-1]:\n # First RC\n new_version_parts.append(\"0rc1\")\n else:\n next_rc = int(old_version[-1][3:]) + 1\n new_version_parts.append(\"0rc{}\".format(next_rc))\n else:\n raise ValueError(\"Unknown release type {!r}\".format(release_type))\n return new_version_parts","def rename(self, old_path, new_path):\n self.rename_file(old_path, new_path)\n self.checkpoints.rename_all_checkpoints(old_path, new_path)","def process_mtn_bug(self,\n bug,\n project_name,\n old_milestone_name,\n new_milestone):\n updates_milestone = self.get_updates_milestone_for(\n old_milestone_name,\n project_name\n )\n\n if not updates_milestone:\n return None\n\n errors = self.add_target_to_bug(bug, project_name, new_milestone)\n\n self.logging.debug(\"Set milestone %s\", updates_milestone.name, )\n if not self.is_debug() and not errors:\n bug.milestone = updates_milestone\n\n try:\n bug.lp_save()\n except Exception as exc: # pylint: disable=W0703\n errors = True\n self.logging.error(\n \"Can't target bug for maintenance '%s' for bug #%s : %s\",\n old_milestone_name + '-updates',\n bug.bug.id,\n exc\n )\n self.logging.exception(exc)\n\n if not errors:\n self.get_stats()[project_name][old_milestone_name]['migrated'] += 1\n self.increase_proccessed_issues()\n else:\n self.logging.error(\"Can't reassign the bug #%s.\", bug.bug.id)","def test_correct_labels(self):\n timeseries = self.build_timeseries()\n\n metric_labels_list = main.get_labels(timeseries[\"metric\"],\"labels\") \n expected_metric_labels_list = self.build_metric_labels()\n self.assertEqual(sorted(metric_labels_list), sorted(expected_metric_labels_list))\n\n resource_labels_list = main.get_labels(timeseries[\"resource\"],\"labels\")\n expected_resource_labels_list = self.build_resource_labels()\n self.assertEqual(sorted(resource_labels_list), sorted(expected_resource_labels_list))\n\n user_labels_list = main.get_labels(self.build_user_labels_request(),\"userLabels\")\n expected_user_labels_list = self.build_expected_user_labels_response()\n self.assertEqual(sorted(user_labels_list), sorted(expected_user_labels_list))\n\n system_labels_list = main.get_system_labels(self.build_user_labels_request(),\"systemLabels\")\n expected_system_labels_list = self.build_expected_system_labels_response()\n self.assertEqual(sorted(system_labels_list), sorted(expected_system_labels_list))","def update_label(UniqueLabel, Label):\n\n UniqueLabel['Confidence'] = ((UniqueLabel['Confidence'] * UniqueLabel['Count']) + Label['Label']['Confidence'])/(UniqueLabel['Count'] + 1)\n UniqueLabel['TimeStamps'].append(Label['Timestamp'])\n UniqueLabel['Count'] += 1\n\t\n return","def update_status(self, msg):\n self.status_lbl.config(text=msg)","def _update_head(self, index_entry, branch, new_id):\r\n index_entry['versions'][branch] = new_id\r\n self.db_connection.update_course_index(index_entry)","def label_experiment(self, exp_id):\n exp = experiment.experiment(new_experiment=False, ts=str(exp_id))\n label = request.form.get('label')\n exp.update_metadata(change_label=True, label=label)\n\n return \"OK\"","def update_sentence_and_label_fields(self, new_sentence: str, new_label: str):\n self.sentence = new_sentence\n self.label = new_label","def test_labels(self):\n self.compliance_tester.test_labels(self.oi)","def bump_tag(\n tag: Optional[semantic_version.Version],\n change_type: int) -> semantic_version.Version:\n if tag is None:\n return semantic_version.Version('0.0.1')\n\n if change_type == constants.MAJOR:\n return tag.next_major()\n\n if change_type == constants.MINOR:\n return tag.next_minor()\n\n return tag.next_patch()","def setLabel(*args):","def setLabel(*args):","def setLabel(*args):","def setLabel(*args):","def setLabel(*args):","def apply(labels: Dict[str, str], dry=False) -> None:\n for old, new in labels.items():\n log.rename_label(old, new, subroutine, dry=dry)","def test_milestone_rename_ok(self):\n test_name = sys._getframe().f_code.co_name\n self._execute('milestone rename milestone1 changed_milestone')\n rv, output = self._execute('milestone list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)"],"string":"[\n \"def test_issue_replace_labels(self):\\n pass\",\n \"def post_issue_labels(repo, project, issue_number):\\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\\n\\n new_label_ids = request.json\\n\\n current_label_ids = dao.get_issue_label_ids(issue_path)\\n\\n revised_label_ids = current_label_ids + new_label_ids\\n\\n dao.set_issue_label_ids(issue_path, revised_label_ids)\\n\\n return if_found(dao.get_issue_labels(issue_path))\",\n \"def patch_issue_labels(repo, project, issue_number):\\n add_label_ids = request.json.get('addLabelIds') or []\\n remove_label_ids = request.json.get('removeLabelIds') or []\\n\\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\\n\\n current_label_ids = dao.get_issue_label_ids(issue_path)\\n\\n revised_label_ids = [label_id for label_id in current_label_ids + add_label_ids\\n if label_id not in remove_label_ids]\\n\\n dao.set_issue_label_ids(issue_path, revised_label_ids)\\n\\n return if_found(dao.get_issue_labels(issue_path))\",\n \"def update_label(self, repository, name, color, old_name=None, **kwargs):\\n data = {'name': name, 'color': color}\\n response = self.session.patch(\\n '{}/repos/{}/labels/{}'.format(\\n self.GH_API_ENDPOINT, repository, old_name or name\\n ),\\n json=data\\n )\\n if response.status_code != 200:\\n raise GitHubError(response)\",\n \"def _UpdateLabels(self, args, migration_job, update_fields):\\n add_labels = labels_util.GetUpdateLabelsDictFromArgs(args)\\n remove_labels = labels_util.GetRemoveLabelsListFromArgs(args)\\n value_type = self.messages.MigrationJob.LabelsValue\\n update_result = labels_util.Diff(\\n additions=add_labels,\\n subtractions=remove_labels,\\n clear=args.clear_labels\\n ).Apply(value_type)\\n if update_result.needs_update:\\n migration_job.labels = update_result.labels\\n update_fields.append('labels')\",\n \"def UpdateLabel(self) -> _n_6_t_0:\",\n \"def test_issue_add_label(self):\\n pass\",\n \"def test_issue_remove_label(self):\\n pass\",\n \"def test_issue_edit_label(self):\\n pass\",\n \"def label(self, name):\\r\\n return labels.IssueLabel(self, name)\",\n \"def test_labels_change(self):\\n label_new = factories.LabelFactory(name=\\\"test_label\\\",\\n object_type='Assessment')\\n response = self.api.put(self.assessment, {'labels': [{\\n \\\"name\\\": label_new.name,\\n \\\"id\\\": label_new.id\\n }]})\\n self.assert200(response)\\n notifs, notif_data = common.get_daily_notifications()\\n updated = notif_data[\\\"user@example.com\\\"][\\\"assessment_updated\\\"]\\n self.assertEqual(len(notifs), 1)\\n self.assertEqual(\\n updated[self.assessment.id][\\\"updated_data\\\"][\\\"LABELS\\\"],\\n (\\\"test_label\\\", \\\"\\\")\\n )\",\n \"def changeLabel(nuclideBase, newLabel):\\n nuclideBase.label = newLabel\\n byLabel[newLabel] = nuclideBase\",\n \"async def new_label(event, gh, *args, **kwargs):\\n if event.data[\\\"label\\\"][\\\"name\\\"] == TRIVIAL_LABEL:\\n issue_number_found = ISSUE_RE.search(\\n event.data[\\\"pull_request\\\"][\\\"title\\\"])\\n if issue_number_found:\\n status = create_success_status(issue_number_found)\\n else:\\n status = TRIVIAL_STATUS\\n await _post_status(event, gh, status)\",\n \"def test_issue_create_label(self):\\n pass\",\n \"def upgrade(self, old_version, new_version):\\n pass\",\n \"def test_issue_get_label(self):\\n pass\",\n \"def relabel(self, old: int, new: int) -> None:\\n if not (isinstance(old, int) and isinstance(new, int)):\\n try:\\n old = int(old)\\n new = int(new)\\n except ValueError:\\n msg = f'Expecting integer arguments, got {type(old)} and {type(new)}!'\\n raise ValueError(msg)\\n\\n if new in set(self.infos.keys()):\\n msg = f'New label < {new} > is in existing labels {set(self.infos.keys())}!'\\n raise ValueError(msg)\\n\\n # modify corresponding SegmentInfo object\\n seginfo = self.infos[old]\\n seginfo.label_value = new\\n # modify array data\\n self.data = relabel(self.data, old, new)\\n # propagate state changes\\n self._update_state_from_infos()\",\n \"def rename_label(self, *args):\\n return _ida_hexrays.vdui_t_rename_label(self, *args)\",\n \"def _update_label(self, change: Dict[str, Any]):\\n dim = change['owner'].description\\n coord = self.controls[dim]['coord'][dim, change['new']]\\n self.controls[dim]['label'].value = coord_element_to_string(coord)\",\n \"def test_issue_list_labels(self):\\n pass\",\n \"def test_unreleased_version_label_string(self):\\n\\n gitchangelog.file_put_contents(\\n \\\".gitchangelog.rc\\\",\\n \\\"unreleased_version_label = 'bar'\\\")\\n changelog = w('$tprog \\\"HEAD^..HEAD\\\"')\\n self.assertNoDiff(\\n textwrap.dedent(\\\"\\\"\\\"\\\\\\n bar\\n ---\\n\\n New\\n ~~~\\n - Begin. [Bob]\\n\\n\\n \\\"\\\"\\\"),\\n changelog)\",\n \"def update_labels(source_repo, service, source_service, destination):\\n app = App()\\n if source_repo:\\n serv = app.get_service(source_service, repo=source_repo)\\n else:\\n serv = app.guess_service()\\n repo_labels = serv.list_labels()\\n if not repo_labels:\\n print(\\\"No labels.\\\")\\n return\\n\\n for repo_for_copy in destination:\\n other_serv = app.get_service(service, repo=repo_for_copy)\\n changes = other_serv.update_labels(labels=repo_labels)\\n\\n click.echo(\\\"{changes} labels of {labels_count} copied to {repo_name}\\\".format(\\n changes=changes,\\n labels_count=len(repo_labels),\\n repo_name=repo_for_copy\\n ))\",\n \"def rdf_update_labels(rdf, node):\\n final_list = []\\n for i in node.get_labels():\\n # print(i)\\n final_list += rdf_get_branch(rdf, i)\\n for i in final_list:\\n node.add_label(i)\",\n \"def _update_label(self, outer_pos, inner_pos, new_label):\\n r, c = outer_pos\\n ir, ic = inner_pos\\n self.inner_boards[r][c][ir][ic][\\\"text\\\"] = new_label\",\n \"def add_labels_to_issue(self, installation_id, repo_owner, repo_name,\\n issue_num, predictions):\\n # take an action if the prediction is confident enough\\n if predictions['labels']:\\n label_names, label_probabilities = self.filter_specified_labels(repo_owner,\\n repo_name,\\n predictions)\\n else:\\n label_names = []\\n\\n # get the isssue handle\\n issue = get_issue_handle(installation_id, repo_owner, repo_name, issue_num)\\n\\n if label_names:\\n # create message\\n message = \\\"\\\"\\\"Issue-Label Bot is automatically applying the labels `{labels}` to this issue, with the confidence of {confidence}.\\n Please mark this comment with :thumbsup: or :thumbsdown: to give our bot feedback!\\n Links: [app homepage](https://github.com/marketplace/issue-label-bot), [dashboard]({app_url}data/{repo_owner}/{repo_name}) and [code](https://github.com/hamelsmu/MLapp) for this bot.\\n \\\"\\\"\\\".format(labels=\\\"`, `\\\".join(label_names),\\n confidence=\\\", \\\".join([\\\"{:.2f}\\\".format(p) for p in label_probabilities]),\\n app_url=self.app_url,\\n repo_owner=repo_owner,\\n repo_name=repo_name)\\n # label the issue using the GitHub api\\n issue.add_labels(*label_names)\\n logging.info(f'Add `{\\\"`, `\\\".join(label_names)}` to the issue # {issue_num}')\\n else:\\n message = \\\"\\\"\\\"Issue Label Bot is not confident enough to auto-label this issue.\\n See [dashboard]({app_url}data/{repo_owner}/{repo_name}) for more details.\\n \\\"\\\"\\\".format(app_url=self.app_url,\\n repo_owner=repo_owner,\\n repo_name=repo_name)\\n logging.warning(f'Not confident enough to label this issue: # {issue_num}')\\n\\n # make a comment using the GitHub api\\n comment = issue.create_comment(message)\",\n \"def update_labels(self,label_dict):\\n \\t\\tfor key in self.deps:\\n \\t\\t\\tfor dependent in self.deps[key]:\\n \\t\\t\\t\\tlabel = dependent[1]\\n \\t\\t\\t\\tlabel_dict[label] = label_dict.get(label,0) + 1\\n \\t\\treturn label_dict\",\n \"def ResetReviewLabels(host, change, label, value='0', revision='current',\\n message=None, notify=None):\\n # This is tricky when working on the \\\"current\\\" revision, because there's\\n # always the risk that the \\\"current\\\" revision will change in between API\\n # calls. So, the code dereferences the \\\"current\\\" revision down to a literal\\n # sha1 at the beginning and uses it for all subsequent calls. As a sanity\\n # check, the \\\"current\\\" revision is dereferenced again at the end, and if it\\n # differs from the previous \\\"current\\\" revision, an exception is raised.\\n current = (revision == 'current')\\n jmsg = GetChangeDetail(\\n host, change, o_params=['CURRENT_REVISION', 'CURRENT_COMMIT'])\\n if current:\\n revision = jmsg['current_revision']\\n value = str(value)\\n path = '%s/revisions/%s/review' % (_GetChangePath(change), revision)\\n message = message or (\\n '%s label set to %s programmatically by chromite.' % (label, value))\\n for review in jmsg.get('labels', {}).get(label, {}).get('all', []):\\n if str(review.get('value', value)) != value:\\n body = {\\n 'message': message,\\n 'labels': {label: value},\\n 'on_behalf_of': review['_account_id'],\\n }\\n if notify:\\n body['notify'] = notify\\n response = FetchUrlJson(host, path, reqtype='POST', body=body)\\n if str(response['labels'][label]) != value:\\n username = review.get('email', jmsg.get('name', ''))\\n raise GOBError(200, 'Unable to set %s label for user \\\"%s\\\"'\\n ' on change %s.' % (label, username, change))\\n if current:\\n new_revision = GetChangeCurrentRevision(host, change)\\n if not new_revision:\\n raise GOBError(\\n 200, 'Could not get review information for change \\\"%s\\\"' % change)\\n elif new_revision != revision:\\n raise GOBError(200, 'While resetting labels on change \\\"%s\\\", '\\n 'a new patchset was uploaded.' % change)\",\n \"def test_issue_get_labels(self):\\n pass\",\n \"def update_label(label1, label2, idx):\\n for i in range(0, len(idx)):\\n label1[i] = label2[idx[i]]\\n return label1\",\n \"def replaceLabels(self, old, new):\\n for c in self.iterCurves():\\n c.update({'label': c.getAttribute('label').replace(old, new)})\",\n \"def bump(self, *, rc: int = 0) -> str:\\n try:\\n current = Version(self.pkg_version)\\n assert isinstance(current, Version)\\n # bump the version\\n self.pkg_version = str(bump_version(post_bump=True, current=current, rc=rc))\\n except Exception as e: # pragma: no cover\\n log.error(f\\\"Error: {e}\\\")\\n return self.pkg_version\",\n \"def change_labels(labels, cluster_name, idx_to_change, target_labels):\\n assert(type(idx_to_change) == list)\\n assert(type(target_labels) == list)\\n assert(len(idx_to_change) == len(target_labels))\\n\\n sub_list = labels[labels == cluster_name]\\n\\n for idx, target in zip(idx_to_change, target_labels):\\n sub_list[idx] = target\\n\\n labels[labels == cluster_name] = sub_list\\n\\n return labels\",\n \"def updatelabel(task, label, eid):\\n ServerManager.get()\\n result = ServerManager.api.update_property(task, eid, prop='label', value=label)\\n if result.response_type == 'success':\\n click.echo(click.style(result.message, fg='green'))\\n else:\\n click.echo(click.style(result.message, fg='red'))\",\n \"def test_issue_delete_label(self):\\n pass\",\n \"def rename(self, label_value: int, new_name: str) -> None:\\n seginfo = self.infos[label_value]\\n seginfo.name = new_name\\n # propagate state changes\\n self._update_state_from_infos()\",\n \"def set_labels(repo: Repository, labels: list[Label]):\\n\\n log.info(f\\\"Fetching existing labels from {repo.full_name}\\\")\\n existing_labels = {label.name.casefold(): label for label in repo.get_labels()}\\n log.info(f\\\"Found {len(existing_labels)} existing labels\\\")\\n\\n for label in labels:\\n qualified_name = label.qualified_name\\n folded_name = qualified_name.casefold()\\n if folded_name not in existing_labels:\\n log.info(f\\\"Creating label {qualified_name}\\\")\\n repo.create_label(**label.api_arguments)\\n elif label != existing_labels[folded_name]:\\n log.info(f\\\"Updating label {qualified_name}\\\")\\n existing_label = existing_labels[folded_name]\\n existing_label.edit(**label.api_arguments)\\n else:\\n log.info(f\\\"Label {qualified_name} already exists\\\")\",\n \"def set_labels(self, new_labels=None):\\n self.labels = new_labels\",\n \"def projects_label(args):\\n session = GithubSession()\\n\\n label_datas = list(session.get_labels())\\n\\n team = args.team\\n team_label_data = None\\n if team:\\n team_label = utils.get_label(team, prefix=\\\"team\\\")\\n team_label_data = [x for x in label_datas if x[\\\"name\\\"] == team_label][0]\\n\\n # get the project label\\n project_label = utils.get_label(args.name, prefix=\\\"project\\\")\\n project_label_data = [x for x in label_datas if x[\\\"name\\\"] == project_label][0]\\n\\n print(f\\\"label cards in project {args.name} column {args.column}\\\")\\n\\n project_board = session.get_project(args.name)\\n project_backlog_grooming = session.get_column(project_board, \\\"backlog grooming\\\")\\n\\n cards = list(session.get_cards(project_backlog_grooming))\\n for card_data in cards:\\n issue_number = utils.get_issue_number_from_card_data(card_data)\\n\\n print(issue_number)\\n\\n # add the project label\\n session.add_label(project_label_data, number=issue_number)\\n\\n if team_label_data:\\n session.add_label(team_label_data, number=issue_number)\",\n \"def test_unreleased_version_label_callable(self):\\n\\n gitchangelog.file_put_contents(\\n \\\".gitchangelog.rc\\\",\\n \\\"unreleased_version_label = lambda : 'foo'\\\")\\n changelog = w('$tprog \\\"HEAD^..HEAD\\\"')\\n self.assertNoDiff(\\n textwrap.dedent(\\\"\\\"\\\"\\\\\\n foo\\n ---\\n\\n New\\n ~~~\\n - Begin. [Bob]\\n\\n\\n \\\"\\\"\\\"),\\n changelog)\",\n \"def UpdatePipelineToolStatusLabel( gui, statusMessage ):\\n gui.pipelineToolStatusLabel.setText( statusMessage )\",\n \"def update_tips(self, refname, old, new):\",\n \"def update_label(self, uuid, name):\\n payload = self._build_params(uuid=uuid, name=name)\\n return Label.deserialize(self._post('labels', None, payload))\",\n \"def delete_issue_label(repo, project, issue_number, delete_label_id):\\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\\n\\n current_label_ids = dao.get_issue_label_ids(issue_path)\\n\\n revised_label_ids = [label_id for label_id in current_label_ids\\n if label_id != delete_label_id]\\n\\n dao.set_issue_label_ids(issue_path, revised_label_ids)\\n\\n return if_found(dao.get_issue_labels(issue_path))\",\n \"def bump_version(ctx, branch, semantic):\\n\\n try:\\n log.echo('Bumping version...', break_line=False)\\n bump = ctx.obj.github.bump_version(branch=branch, semantic=semantic)\\n log.checkmark()\\n log.echo('Bumped version from {} to {}'.format(bump.prev_version, bump.next_version))\\n except BaseException as _:\\n log.xmark()\\n raise\",\n \"def rename_issue(self, msg, chat):\\n msg, text = self.split_message(msg)\\n\\n if text == '':\\n self.url_handler.send_message(\\\"\\\"\\\"\\n You want to modify the issue {},\\n but you didn't provide any new text\\n \\\"\\\"\\\".format(msg), chat)\\n return\\n\\n payload = \\\"{\\\\n \\\\\\\"title\\\\\\\": \\\\\\\"\\\"+text+\\\"\\\\\\\",\\\\n \\\\\\\"labels\\\\\\\": [\\\\n \\\\\\\"telegram\\\\\\\"\\\\n ]\\\\n}\\\"\\n print(payload)\\n headers = {\\n 'Content-Type': \\\"application/json\\\",\\n 'Authorization': \\\"Basic WWVzdGVyZGF5WW91U2FpZFRvbW9ycm93Qm90Olllc3RlcmRheVlvdVNhaWRUb21vcnJvdw==\\\",\\n 'Cache-Control': \\\"no-cache\\\",\\n 'Postman-Token': \\\"49817fa1-698d-496d-b6e3-252e81bc792f\\\"\\n }\\n result = requests.request(\\\"GET\\\", constants.URL_GITHUB + '/' + msg)\\n result_json = result.json()\\n print(result_json)\\n try:\\n result_json['state']\\n except:\\n return self.url_handler.send_message(\\\"Issue does not exist\\\", chat)\\n\\n requests.request(\\\"POST\\\", constants.URL_GITHUB+'/'+msg, data=payload, headers=headers)\\n return self.url_handler.send_message(\\\"Issue renamed {}\\\".format(text), chat)\",\n \"def on_change(self, attr, old, new):\\n n = self.labels.index(new) + 1 # Select 0-indexed\\n self.notify(set_figures(n))\",\n \"def put(self, id):\\n context = request.environ.get('context')\\n resp = dbapi.netdevices_labels_update(context, id, request.json)\\n response = {\\\"labels\\\": list(resp.labels)}\\n return response, 200, None\",\n \"def labels(self):\\r\\n return labels.IssueLabels(self)\",\n \"def update_ref_label_by_prediction(\\n ref_label: torch.Tensor,\\n pre_label: torch.Tensor) -> torch.Tensor:\\n ref_label = utils.update_ref_label_with_mask(\\n ref_label, pre_label == 1, utils.REF_POSSIBLY_FOREGROUND)\\n ref_label = utils.update_ref_label_with_mask(\\n ref_label, pre_label == 0, utils.REF_POSSIBLY_BACKGROUND)\\n return ref_label\",\n \"def label_milestone_issues():\\n session = GithubSession()\\n\\n labels = list(session.get_labels())\\n labels_by_name = dict([(x['name'], x) for x in labels])\\n\\n milestones = list(session.get_milestones())\\n\\n for milestone in milestones:\\n label_data = labels_by_name[f'epic:{milestone[\\\"title\\\"].strip()}']\\n\\n for issue in session.get_issues(milestone=milestone[\\\"number\\\"], state='all'):\\n session.add_label(label_data, number=issue['number'])\",\n \"def update_versions(new_py_version: str) -> None:\\n current_py_version = get_current_py_version()\\n current_py_version_without_suffix = replace_dev_suffix_with(current_py_version, \\\"\\\")\\n\\n # Python\\n replace_occurrences(\\n files=[Path(\\\"mlflow\\\", \\\"version.py\\\")],\\n pattern=re.escape(current_py_version),\\n repl=new_py_version,\\n )\\n # JS\\n replace_occurrences(\\n files=[\\n Path(\\n \\\"mlflow\\\",\\n \\\"server\\\",\\n \\\"js\\\",\\n \\\"src\\\",\\n \\\"common\\\",\\n \\\"constants.tsx\\\",\\n )\\n ],\\n pattern=re.escape(current_py_version),\\n repl=new_py_version,\\n )\\n\\n # Java\\n for java_extension in [\\\"xml\\\", \\\"java\\\"]:\\n replace_occurrences(\\n files=Path(\\\"mlflow\\\", \\\"java\\\").rglob(f\\\"*.{java_extension}\\\"),\\n pattern=rf\\\"{re.escape(current_py_version_without_suffix)}(-SNAPSHOT)?\\\",\\n repl=replace_dev_suffix_with(new_py_version, \\\"-SNAPSHOT\\\"),\\n )\\n\\n # R\\n replace_occurrences(\\n files=[Path(\\\"mlflow\\\", \\\"R\\\", \\\"mlflow\\\", \\\"DESCRIPTION\\\")],\\n pattern=f\\\"Version: {re.escape(current_py_version_without_suffix)}\\\",\\n repl=f\\\"Version: {replace_dev_suffix_with(new_py_version, '')}\\\",\\n )\",\n \"def set_base_image_labels(driver, user_disk, img_name, branch, target):\\n\\n dashes = [i for i, c in enumerate(img_name) if c=='-']\\n cf_version = img_name[dashes[0]+1:dashes[3]]\\n build_id = img_name[dashes[-1]+1:]\\n\\n driver.ex_set_volume_labels(user_disk,\\n {'cf_version': cf_version, 'branch': branch,\\n 'target': target, 'build_id': build_id})\",\n \"def add_label_to_existing(hostname, new_labels):\\n\\n logging.debug('going to add labels %s to existing host %s' % (new_labels, hostname))\\n\\n checkmk_api_url = config['checkmk_api_url']\\n checkmk_api_username = config['checkmk_api_username']\\n checkmk_api_secret = config['checkmk_api_secret']\\n checkmk_puppetdb_label = config['checkmk_puppetdb_label']\\n\\n # Save the attributes, save the ~world~ existing labels\\n req_params = { 'action': 'get_host',\\n '_username': config['checkmk_api_username'],\\n '_secret': config['checkmk_api_secret'],\\n 'hostname': hostname,\\n 'output_format': 'json' }\\n r = requests.post(checkmk_api_url, req_params)\\n\\n existing_labels = {}\\n try:\\n existing_labels.update(r.json()['result']['attributes']['labels'])\\n except:\\n pass\\n\\n # add new labels to existing labels and ensure from_puppetdb label present\\n existing_labels.update(new_labels)\\n existing_labels.update({ 'from_puppetdb': checkmk_puppetdb_label })\\n\\n payload = {'request': json.dumps({\\n 'hostname': hostname,\\n 'attributes': {\\n 'labels': existing_labels\\n }\\n })}\\n\\n logging.debug('-- adding labels %s to host %s' % (existing_labels, hostname))\\n r = requests.post(\\\"%s?action=edit_host&_username=%s&_secret=%s\\\" % (checkmk_api_url, checkmk_api_username, checkmk_api_secret), data=payload)\\n logging.debug('-- got resp code = %d' % r.status_code)\\n logging.debug('-- got resp text = %s' % r.text)\\n r_json = json.loads(r.text)\\n\\n # Successful edit_host gives response of {\\\"result\\\": null, \\\"result_code\\\": 0}\\n if r_json['result_code'] == 0 and r_json['result'] is None:\\n logging.info('added labels %s to %s successfully' % (existing_labels, hostname))\\n else:\\n logging.warn('failed to add labels %s to host %s' % (r_json['result'], hostname))\",\n \"def add_labels(number, labels):\\n\\n cmds = [github_cli, 'pr', 'edit', str(number)]\\n for lab in labels:\\n cmds += ['--add-label', lab]\\n\\n with subprocess.Popen(cmds) as p:\\n _, err = p.communicate()\\n print(err)\",\n \"def update_version() -> str:\\n cur_version = get_current_version(args.stage)\\n\\n if args.stage == \\\"prod\\\":\\n prv_version = get_current_version(stage='staging')\\n new_version = semver.finalize_version(prv_version)\\n elif args.stage == \\\"staging\\\":\\n prv_version = get_current_version(stage='integration')\\n assert '-integration' in prv_version\\n new_version = prv_version.replace('-integration', '-rc') # don't bump the version number\\n else:\\n new_version = getattr(semver, f'bump_{args.release}')(str(cur_version))\\n new_version = new_version if semver.parse_version_info(new_version).prerelease \\\\\\n else semver.bump_prerelease(new_version, token='integration')\\n\\n if cur_version == new_version:\\n print(\\\"Nothing to promote\\\")\\n exit(0)\\n else:\\n print(f\\\"Upgrading: {cur_version} -> {new_version}\\\")\\n return new_version\",\n \"def bump_oa_release_number(**kwargs):\\n\\n oa_folder = kwargs['workdir'] + '/openstack-ansible/'\\n fpth, cver = get_oa_version(oa_folder)\\n LOGGER.info(\\\"Current version {} in {}\\\".format(cver, fpth))\\n\\n if cver == \\\"master\\\":\\n click.confirm(\\\"Master should only changed when necessary. Sure?\\\")\\n\\n if kwargs['version'] == \\\"auto\\\":\\n LOGGER.info(\\\"Guessing next version\\\")\\n cver_l = cver.split(\\\".\\\")\\n try:\\n cver_l[-1] = str(int(cver_l[-1]) + 1)\\n except ValueError as vee:\\n LOGGER.error(\\\"Cannot up the version: {}\\\".format(vee))\\n nver = click.prompt(\\\"New version?\\\")\\n else:\\n nver = \\\".\\\".join(cver_l)\\n else:\\n nver = kwargs['version']\\n\\n for line in fileinput.input(\\\"{}/{}\\\".format(oa_folder, fpth),\\n inplace=True):\\n print(line.replace(\\n \\\"openstack_release: {}\\\".format(cver),\\n \\\"openstack_release: {}\\\".format(nver))),\\n LOGGER.info(\\\"Updated the version in repo to {}\\\".format(nver))\\n\\n msg = (\\\"Here is a commit message you could use:\\\\n\\\"\\n \\\"Update all SHAs for {new_version}\\\\n\\\\n\\\"\\n \\\"This patch updates all the roles to the latest available stable \\\\n\\\"\\n \\\"SHA's, copies the release notes from the updated roles into the \\\\n\\\"\\n \\\"integrated repo, updates all the OpenStack Service SHA's, and \\\\n\\\"\\n \\\"updates the appropriate python requirements pins. \\\\n\\\\n\\\"\\n \\\"Depends-On: {release_changeid}\\\").format(\\n new_version=os.environ.get('new_version', nver),\\n release_changeid=os.environ.get('release_changeid', '<TODO>'))\\n\\n if kwargs['commit']:\\n repo = Repo(oa_folder)\\n repo.git.add('.')\\n repo.index.commit(msg)\\n click.echo(\\\"Commit done. Please verify before review.\\\")\\n else:\\n click.echo(msg)\",\n \"def fix_label_names():\\n\\n assert trace.cpu.trace_done\\n binary_addr = memorymanager.BinaryAddr(0)\\n while binary_addr < len(classifications):\\n c = classifications[binary_addr]\\n if c is not None:\\n dummy = [str(x) for x in c.as_string_list(binary_addr, None)]\\n binary_addr += c.length()\\n else:\\n binary_addr += 1\",\n \"def add_label(self, new_name, status):\\n api_uri = self._uri_dict.get('addLabel')\\n data = {\\n 'newName': new_name,\\n 'status': status\\n }\\n r_data = self._post(api_uri, data)\\n return r_data\",\n \"def label(self, name):\\r\\n return labels.RepoLabel(self, name)\",\n \"def version_changed(\\n self, entity: Union[str, dict], attribute: str, old: str, new: str, kwargs: dict\\n ) -> None:\\n self.log(old)\\n self.log(new)\\n if new != old:\\n app = entity.split(\\\".\\\")[1].split(\\\"_\\\")[0]\\n self.notification_app.notify(\\n kind=\\\"single\\\",\\n level=\\\"home\\\",\\n title=f\\\"Neue Version für {app}!\\\",\\n message=f\\\"Die Version {self.get_state(entity)} \\\"\\n f\\\"für {app} ist verfügbar.\\\",\\n targets=self.notifications[\\\"targets\\\"],\\n )\",\n \"def test_issue_clear_labels(self):\\n pass\",\n \"def update(self, src, labels): # real signature unknown; restored from __doc__\\n pass\",\n \"def swaplabel(self, label_a: int, label_b: int) -> None:\\n if not (isinstance(label_a, int) and isinstance(label_b, int)):\\n msg = f'Expecting integer arguments, got {type(label_a)} and {type(label_b)}!'\\n raise ValueError(msg)\\n\\n self.infos[label_a].label_value = label_b\\n self.infos[label_b].label_value = label_a\\n labels = [label_a, label_b]\\n # modify array data\\n self.data = relabel(self.data, labels, reversed(labels))\\n # propagate state changes\\n self._update_state_from_infos()\",\n \"def bump_version(version_number):\\n print(f\\\"Bumping version to: {version_number}\\\")\\n _create_new_changelog_release()\\n for filename, replacer in get_files_to_change().items():\\n print(\\\"Bumping version in %s\\\" % filename)\\n with open(filename, 'r') as f:\\n contents = f.read()\\n if callable(replacer):\\n new_contents = replacer(version_number, contents)\\n else:\\n new_contents = _regex_based_version_bump(\\n version_number,\\n replacer,\\n contents)\\n with open(filename, 'w') as f:\\n f.write(new_contents)\",\n \"def setLabel2(*args):\",\n \"def update_tt_label(video: dict):\\n # try to get all text tracks\\n if tts := video.get('text_tracks'):\\n # go through all tracks\\n for track in tts:\\n # change the setting\\n track['label'] = track['srclang']\\n\\n # get the video ID\\n video_id = video.get('id')\\n # create the JSON body\\n json_body = { 'text_tracks': tts }\\n # make the PATCH call\\n r = get_cms().UpdateVideo(video_id=video_id, json_body=json_body)\\n # check if all went well\\n if r.status_code in [200,202]:\\n print(f'Updated track labels for video ID {video_id} with status {r.status_code}.')\\n # otherwise report the error\\n else:\\n print(f'Error code {r.status_code} updating track labels for video ID {video_id}:')\\n print(r.text)\",\n \"def remove_labels(number, labels):\\n\\n cmds = [github_cli, 'pr', 'edit', str(number)]\\n for lab in labels:\\n cmds += ['--remove-label', lab]\\n\\n with subprocess.Popen(cmds) as p:\\n _, err = p.communicate()\\n print(err)\",\n \"def propagate_labels(image,labels,conflict=0):\\n rlabels,_ = label(image)\\n cors = correspondences(rlabels,labels,False)\\n outputs = zeros(amax(rlabels)+1,'i')\\n oops = -(1<<30)\\n for o,i in cors.T:\\n if outputs[o]!=0: outputs[o] = oops\\n else: outputs[o] = i\\n outputs[outputs==oops] = conflict\\n outputs[0] = 0\\n return outputs[rlabels]\",\n \"def _update_team_labels(self, host, team_name):\\n # host = Host.get(identifier=identifier)\\n # function to get labelFingerprint and Labels info from an instance of GCP\\n instance = self.get_instance(host.name, host.zone)\\n labelFingerprint = instance['labelFingerprint']\\n labels = instance['labels']\\n\\n # function to get information from a team\\n team = TeamClient(api_url=TEAM_API_URL, team_name=team_name)\\n team = team.team\\n\\n # add info of new team\\n labels['servico_de_negocio'] = team.get('servico-de-negocio')\\n labels['cliente'] = team.get('cliente')\\n labels['team_slug_name'] = team.get('slug')\\n labels['team_id'] = team.get('id')\\n\\n # create the body of function with Label Fingerprint and Labels\\n body = dict()\\n body['labels'] = labels\\n body['labelFingerprint'] = labelFingerprint\\n\\n update_label = self.client.instances().setLabels(project=self.credential.project,\\n zone=host.zone,\\n instance=host.name,\\n body=body).execute()\\n\\n return self.wait_operation(operation=update_label.get('name'),\\n zone=host.zone)\",\n \"def rename(old, new):\",\n \"def rename(old, new):\",\n \"def rename(self, group, new_title):\\n old_md = '[[{}]]'.format(self.title)\\n new_md = '[[{}]]'.format(new_title)\\n \\n old_html = render_wiki_link(group, self.id, self.title)\\n new_html = render_wiki_link(group, self.id, new_title)\\n \\n # `switch_db(WikiPage, group)` has already been done in `main.wiki_rename_page`.\\n for p in self.__class__.objects(refs__contains=self.id).exclude('comments').all():\\n p.md = p.md.replace(old_md, new_md)\\n p.html = p.html.replace(old_html, new_html)\\n p.save()\\n \\n with switch_db(WikiPageVersion, group) as _WikiPageVersion:\\n for pv in _WikiPageVersion.objects.search_text(old_md).all():\\n pv.diff = pv.diff.replace(old_md, new_md)\\n pv.save()\\n with switch_db(WikiCache, group) as _WikiCache:\\n _WikiCache.objects(changes_id_title=[self.id, self.title]).\\\\\\n update(set__changes_id_title__S=[self.id, new_title])\\n _WikiCache.objects(keypages_id_title=[self.id, self.title]).\\\\\\n update(set__keypages_id_title__S=[self.id, new_title])\\n\\n self.title = new_title\\n self.save()\",\n \"def relabel_topic(user, map_id, old_topic_text, new_topic_text):\\n the_map = get_map(user, map_id)\\n topic = the_map.subtopics.pop(old_topic_text)\\n topic.text = new_topic_text\\n the_map.subtopics[new_topic_text] = topic\\n save_map(user, map_id, the_map)\",\n \"def change_version(new_version):\\n global __model\\n __model = __ITU1511(new_version)\\n utils.memory.clear()\",\n \"def test_version_rename_error_bad_version(self):\\n test_name = sys._getframe().f_code.co_name\\n rv, output = self._execute('version rename bad_version changed_name')\\n self.assertEqual(2, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def update(self):\\n\\n params = {\\n \\\"title\\\": self.title,\\n \\\"body\\\": self.body,\\n \\\"state\\\": self.state,\\n \\\"labels\\\": self.labels,\\n \\\"assignees\\\": self.assignees,\\n }\\n\\n if self.milestone:\\n params[\\\"milestone\\\"] = self.milestone\\n\\n resp = yield self.client.request(\\n \\\"{}/issues/{}\\\".format(self.repo.base_path, self.num),\\n params=params, method=\\\"PATCH\\\")\\n self.c = resp.data\\n self.after_sync()\\n raise gen.Return(self)\",\n \"def modify_labels(self, body, update_labels):\\n for label_modifier in self.schema.get(\\\"labels\\\", []):\\n\\n # handle the service selector lookup\\n spec = label_modifier.get(\\\"service\\\")\\n if spec is not None:\\n try:\\n # get the service we're after\\n service = self.k8s_inventory.k8s_client.get_service(\\n name=spec.get(\\\"name\\\"),\\n namespace=spec.get(\\\"namespace\\\"),\\n )\\n # reset the existing labels\\n update_labels()\\n # update with the selectors from the service\\n update_labels(**service.spec.selector)\\n except:\\n return False\\n\\n # handle the static label modifications\\n spec = label_modifier.get(\\\"label\\\")\\n if spec is not None:\\n updates = dict()\\n updates[spec.get(\\\"key\\\")] = spec.get(\\\"value\\\")\\n update_labels(**updates)\\n\\n return body\",\n \"def test_changeVersions(self):\\n self._testVersionChanging(8, 2, 3)\",\n \"def test_priority_change_error_bad_new_name(self):\\n test_name = sys._getframe().f_code.co_name\\n rv, output = self._execute('priority change major minor')\\n self.assertEqual(2, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def rename_value(model: onnx.ModelProto, old_name: str, new_name: str):\\n if old_name == new_name:\\n return\\n logger = get_root_logger()\\n logger.info(f'rename {old_name} -> {new_name}')\\n for n in model.graph.node:\\n for i, output in enumerate(n.output):\\n if output == old_name:\\n n.output[i] = new_name\\n for i, input in enumerate(n.input):\\n if input == old_name:\\n n.input[i] = new_name\\n for v in model.graph.value_info:\\n if v.name == old_name:\\n v.name = new_name\\n for i, input in enumerate(model.graph.input):\\n if input.name == old_name:\\n input.name = new_name\\n for i, output in enumerate(model.graph.output):\\n if output.name == old_name:\\n output.name = new_name\",\n \"def test_severity_change_error_bad_new_name(self):\\n test_name = sys._getframe().f_code.co_name\\n self._execute('severity add major')\\n self._execute('severity add critical')\\n rv, output = self._execute('severity change critical major')\\n self.assertEqual(2, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def update_frame_label(self):\\n count = len(self.main_frame_list)\\n\\n for idx in range(count): #Start, count) \\n s1 = \\\"\\\"\\n for i in range(16): #self.main_frame_nibble_list: # 16\\n s = \\\"\\\"\\n for j in range(4):\\n s += str(self.main_button_bit_list[idx][i*4 + j].get_label())\\n s = s[::-1]\\n self.main_frame_nibble_list[idx][i].set_label(str(hex(int(s,2)))[2:].upper())\\n s1 += str(self.main_frame_nibble_list[idx][i].get_label())\\n s1 = s1[::-1]\\n if DEBUG: print(s1[:8] + \\\" \\\" + s1[8:])\\n self.main_frame_list[idx].set_label(s1[:8] + \\\" \\\" + s1[8:])\",\n \"def increment_milestone_version(old_version, release_type):\\n if release_type == \\\"milestone\\\":\\n if \\\"b\\\" in old_version[-1]:\\n # Not the first milestone\\n new_version_parts = old_version[:-1]\\n next_milestone = int(old_version[-1][2:]) + 1\\n new_version_parts.append(\\\"0b{}\\\".format(next_milestone))\\n else:\\n new_version_parts = increment_version(old_version, (1, 0, 0))\\n new_version_parts.append(\\\"0b1\\\")\\n elif release_type == \\\"rc\\\":\\n new_version_parts = old_version[:-1]\\n if \\\"b\\\" in old_version[-1]:\\n # First RC\\n new_version_parts.append(\\\"0rc1\\\")\\n else:\\n next_rc = int(old_version[-1][3:]) + 1\\n new_version_parts.append(\\\"0rc{}\\\".format(next_rc))\\n else:\\n raise ValueError(\\\"Unknown release type {!r}\\\".format(release_type))\\n return new_version_parts\",\n \"def rename(self, old_path, new_path):\\n self.rename_file(old_path, new_path)\\n self.checkpoints.rename_all_checkpoints(old_path, new_path)\",\n \"def process_mtn_bug(self,\\n bug,\\n project_name,\\n old_milestone_name,\\n new_milestone):\\n updates_milestone = self.get_updates_milestone_for(\\n old_milestone_name,\\n project_name\\n )\\n\\n if not updates_milestone:\\n return None\\n\\n errors = self.add_target_to_bug(bug, project_name, new_milestone)\\n\\n self.logging.debug(\\\"Set milestone %s\\\", updates_milestone.name, )\\n if not self.is_debug() and not errors:\\n bug.milestone = updates_milestone\\n\\n try:\\n bug.lp_save()\\n except Exception as exc: # pylint: disable=W0703\\n errors = True\\n self.logging.error(\\n \\\"Can't target bug for maintenance '%s' for bug #%s : %s\\\",\\n old_milestone_name + '-updates',\\n bug.bug.id,\\n exc\\n )\\n self.logging.exception(exc)\\n\\n if not errors:\\n self.get_stats()[project_name][old_milestone_name]['migrated'] += 1\\n self.increase_proccessed_issues()\\n else:\\n self.logging.error(\\\"Can't reassign the bug #%s.\\\", bug.bug.id)\",\n \"def test_correct_labels(self):\\n timeseries = self.build_timeseries()\\n\\n metric_labels_list = main.get_labels(timeseries[\\\"metric\\\"],\\\"labels\\\") \\n expected_metric_labels_list = self.build_metric_labels()\\n self.assertEqual(sorted(metric_labels_list), sorted(expected_metric_labels_list))\\n\\n resource_labels_list = main.get_labels(timeseries[\\\"resource\\\"],\\\"labels\\\")\\n expected_resource_labels_list = self.build_resource_labels()\\n self.assertEqual(sorted(resource_labels_list), sorted(expected_resource_labels_list))\\n\\n user_labels_list = main.get_labels(self.build_user_labels_request(),\\\"userLabels\\\")\\n expected_user_labels_list = self.build_expected_user_labels_response()\\n self.assertEqual(sorted(user_labels_list), sorted(expected_user_labels_list))\\n\\n system_labels_list = main.get_system_labels(self.build_user_labels_request(),\\\"systemLabels\\\")\\n expected_system_labels_list = self.build_expected_system_labels_response()\\n self.assertEqual(sorted(system_labels_list), sorted(expected_system_labels_list))\",\n \"def update_label(UniqueLabel, Label):\\n\\n UniqueLabel['Confidence'] = ((UniqueLabel['Confidence'] * UniqueLabel['Count']) + Label['Label']['Confidence'])/(UniqueLabel['Count'] + 1)\\n UniqueLabel['TimeStamps'].append(Label['Timestamp'])\\n UniqueLabel['Count'] += 1\\n\\t\\n return\",\n \"def update_status(self, msg):\\n self.status_lbl.config(text=msg)\",\n \"def _update_head(self, index_entry, branch, new_id):\\r\\n index_entry['versions'][branch] = new_id\\r\\n self.db_connection.update_course_index(index_entry)\",\n \"def label_experiment(self, exp_id):\\n exp = experiment.experiment(new_experiment=False, ts=str(exp_id))\\n label = request.form.get('label')\\n exp.update_metadata(change_label=True, label=label)\\n\\n return \\\"OK\\\"\",\n \"def update_sentence_and_label_fields(self, new_sentence: str, new_label: str):\\n self.sentence = new_sentence\\n self.label = new_label\",\n \"def test_labels(self):\\n self.compliance_tester.test_labels(self.oi)\",\n \"def bump_tag(\\n tag: Optional[semantic_version.Version],\\n change_type: int) -> semantic_version.Version:\\n if tag is None:\\n return semantic_version.Version('0.0.1')\\n\\n if change_type == constants.MAJOR:\\n return tag.next_major()\\n\\n if change_type == constants.MINOR:\\n return tag.next_minor()\\n\\n return tag.next_patch()\",\n \"def setLabel(*args):\",\n \"def setLabel(*args):\",\n \"def setLabel(*args):\",\n \"def setLabel(*args):\",\n \"def setLabel(*args):\",\n \"def apply(labels: Dict[str, str], dry=False) -> None:\\n for old, new in labels.items():\\n log.rename_label(old, new, subroutine, dry=dry)\",\n \"def test_milestone_rename_ok(self):\\n test_name = sys._getframe().f_code.co_name\\n self._execute('milestone rename milestone1 changed_milestone')\\n rv, output = self._execute('milestone list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6792092","0.6410261","0.63336664","0.62997633","0.6212655","0.6188349","0.6131808","0.6078506","0.60697824","0.5982465","0.59715044","0.58801395","0.58639926","0.5816984","0.576189","0.57202476","0.57188237","0.5707912","0.570286","0.565688","0.5632423","0.5613924","0.56072116","0.55909085","0.55902797","0.5589008","0.5561104","0.55466795","0.5495012","0.54757917","0.5469786","0.5456499","0.5451979","0.5406383","0.5404633","0.53835005","0.527141","0.52664","0.5251849","0.52461696","0.5245241","0.52295184","0.5224342","0.5212448","0.5195476","0.51892555","0.51859975","0.51604134","0.51590896","0.5157238","0.513951","0.5117417","0.5069742","0.5050227","0.50450265","0.5031703","0.5026124","0.50103456","0.5008384","0.49867916","0.4980614","0.49700147","0.49562868","0.49533263","0.4950649","0.49243277","0.4908688","0.49079958","0.48980427","0.48908287","0.48908287","0.4888868","0.48818275","0.48812732","0.48797363","0.4867418","0.48659474","0.48603296","0.48577738","0.48442668","0.48438367","0.48377132","0.4836341","0.4831462","0.48253575","0.4822655","0.48145142","0.48087797","0.48087257","0.48007163","0.4793928","0.47929624","0.47927243","0.47900912","0.47900912","0.47900912","0.47900912","0.47900912","0.47874558","0.47707257"],"string":"[\n \"0.6792092\",\n \"0.6410261\",\n \"0.63336664\",\n \"0.62997633\",\n \"0.6212655\",\n \"0.6188349\",\n \"0.6131808\",\n \"0.6078506\",\n \"0.60697824\",\n \"0.5982465\",\n \"0.59715044\",\n \"0.58801395\",\n \"0.58639926\",\n \"0.5816984\",\n \"0.576189\",\n \"0.57202476\",\n \"0.57188237\",\n \"0.5707912\",\n \"0.570286\",\n \"0.565688\",\n \"0.5632423\",\n \"0.5613924\",\n \"0.56072116\",\n \"0.55909085\",\n \"0.55902797\",\n \"0.5589008\",\n \"0.5561104\",\n \"0.55466795\",\n \"0.5495012\",\n \"0.54757917\",\n \"0.5469786\",\n \"0.5456499\",\n \"0.5451979\",\n \"0.5406383\",\n \"0.5404633\",\n \"0.53835005\",\n \"0.527141\",\n \"0.52664\",\n \"0.5251849\",\n \"0.52461696\",\n \"0.5245241\",\n \"0.52295184\",\n \"0.5224342\",\n \"0.5212448\",\n \"0.5195476\",\n \"0.51892555\",\n \"0.51859975\",\n \"0.51604134\",\n \"0.51590896\",\n \"0.5157238\",\n \"0.513951\",\n \"0.5117417\",\n \"0.5069742\",\n \"0.5050227\",\n \"0.50450265\",\n \"0.5031703\",\n \"0.5026124\",\n \"0.50103456\",\n \"0.5008384\",\n \"0.49867916\",\n \"0.4980614\",\n \"0.49700147\",\n \"0.49562868\",\n \"0.49533263\",\n \"0.4950649\",\n \"0.49243277\",\n \"0.4908688\",\n \"0.49079958\",\n \"0.48980427\",\n \"0.48908287\",\n \"0.48908287\",\n \"0.4888868\",\n \"0.48818275\",\n \"0.48812732\",\n \"0.48797363\",\n \"0.4867418\",\n \"0.48659474\",\n \"0.48603296\",\n \"0.48577738\",\n \"0.48442668\",\n \"0.48438367\",\n \"0.48377132\",\n \"0.4836341\",\n \"0.4831462\",\n \"0.48253575\",\n \"0.4822655\",\n \"0.48145142\",\n \"0.48087797\",\n \"0.48087257\",\n \"0.48007163\",\n \"0.4793928\",\n \"0.47929624\",\n \"0.47927243\",\n \"0.47900912\",\n \"0.47900912\",\n \"0.47900912\",\n \"0.47900912\",\n \"0.47900912\",\n \"0.47874558\",\n \"0.47707257\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75369704"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74590,"cells":{"query":{"kind":"string","value":"Create comment for an issue"},"document":{"kind":"string","value":"def comment_issue(self, msg, issue_id, comment):\n self._asset_bind(msg)\n client = self._github_operator(msg)\n comment_obj = client.issue_comment(task_repository_name(), issue_id, comment)\n yield comment_obj.html_url"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_issue_create_comment(self):\n pass","def create_comment(self, body):\n return self.client.request(\n \"{}/issues/{}/comments\".format(self.repo.base_path, self.num),\n params={\"body\": body},\n method=\"POST\"\n )","def test_issue_get_comment(self):\n pass","def add_comment() -> str:\n if \"markdown\" in request.form:\n if \"file\" in request.form:\n comment = Comment(\n markdown=request.form[\"markdown\"],\n submission_id=Submission.query.filter(\n Submission.filepath.contains(request.form[\"file\"])\n )\n .first()\n .id,\n cell_id=request.form[\"cell_id\"] if \"cell_id\" in request.form else None,\n user=UserModel.get_by_token(session[\"token\"]),\n )\n # If not cell_id this is a general comment\n comment.save()\n else:\n return \"Missing file or cell_id\", 400\n else:\n return \"Missing markdown\", 400\n\n comment_maker = get_template_attribute(\"_macros.html\", \"comment_block\")\n return comment_maker(comment)","async def createComment(self, *args, **kwargs):\n\n return await self._makeApiCall(self.funcinfo[\"createComment\"], *args, **kwargs)","def comment(self, comment_id):\r\n return IssueComment(self, comment_id)","def comment(self, comment_id):\r\n return IssueComment(self, comment_id)","def addcomment(accountable, body):\n\n r = accountable.issue_add_comment(body)\n headers = sorted(['author_name', 'body', 'updated'])\n rows = [[v for k, v in sorted(r.items()) if k in headers]]\n rows.insert(0, headers)\n print_table(SingleTable(rows))","def createcomment(request, pk):\n issue = get_object_or_404(Issue, pk=pk)\n if request.method == \"POST\":\n form = CommentCreationForm(request.POST)\n if form.is_valid():\n comment = form.save(commit=False)\n comment.issue = issue\n comment.author = request.user\n comment.created_at = timezone.now()\n comment.save()\n return redirect('office:issue', pk=pk)\n else:\n form = CommentForm()\n return render(request, 'blog/add_comment_to_post.html', {'form': form})","def create(self, number, message, user=None, repo=None):\n request = self.make_request('issues.comments.create', user=user,\n repo=repo, number=number, body={'body': message})\n return self._post(request)","def on_issue_comment(self, payload):\n pass","def test_issue_edit_comment(self):\n pass","def make_comment(self, offset, comment):\n #self.ret = idc.MakeComm(offset, comment)\n return self.ret","def add_comment(self, issue, comment):\n return self.get_jira().add_comment(issue, comment)","def __add_comment(self, issue_id, comment):\n import httplib2\n http = httplib2.Http() \n response, content = http.request(\n uri=self.__issue_url % int(issue_id),\n method='PUT',\n body=comment,\n headers={\n 'X-Redmine-API-Key': self.__api_key,\n 'Content-type': 'application/json'\n }\n )\n print(response)\n print(content)","def create_issue(self, data, **kwargs):\n raise NotImplementedError","def create_issue(self, data, **kwargs):\n raise NotImplementedError","def add_comment_to_issue(self, issue, comment, visibility=None):\r\n self.jira.add_comment(issue=issue, body=comment)","def _generate_pr_comment_markdown(self, data):\n pass","def post(self, pid, sid, aid):\n return create_comment(pid, sid, aid)","def cli(ctx, comment, metadata=\"\"):\n return ctx.gi.cannedcomments.add_comment(comment, metadata=metadata)","def post(self, pid, sid, aid, cid):\n return create_comment(pid, sid, aid, cid)","def make_comment(body, who, when):\n out = et.Element(\"comment\")\n et.SubElement(out, \"author\").text = who\n et.SubElement(out, \"date\").text = when\n et.SubElement(out, \"content-type\").text = \"text/plain\"\n # BE import doesn't like empty comment bodies\n if body==\"\":\n body = \"(no content)\"\n et.SubElement(out, \"body\").text = body\n return out","def create(self, comment):\r\n url = self.get_url()\r\n\r\n # when creating commits they don't get wrapped in {\"body\": <comment>}\r\n return http.Request('POST', url, params=comment), parsers.parse_json","def test_issue_get_comments(self):\n pass","def create_incident_comment(self, incident_id, data):\n try:\n uri = '/incidents/{}/comments'.format(incident_id)\n resilient_client = self.rest_client()\n heading = \"Raw Proofpoint TRAP Event Payload:\\n\"\n note = {\n 'format': 'text',\n 'content': '{}{}'.format(heading, pprint.pformat(data, indent=4))\n }\n payload = {'text': note}\n comment_response = resilient_client.post(uri=uri, payload=payload)\n return comment_response\n\n except SimpleHTTPException as ex:\n LOG.error(\"Failed to add note for incident %d: %s\", incident_id, ex)","def create_comment(request, course_id, thread_id):\r\n if cc_settings.MAX_COMMENT_DEPTH is not None:\r\n if cc_settings.MAX_COMMENT_DEPTH < 0:\r\n return JsonError(_(\"Comment level too deep\"))\r\n return _create_comment(request, SlashSeparatedCourseKey.from_deprecated_string(course_id), thread_id=thread_id)","def add_comment_to_issue(repo, issue_number, body, allow_duplicates):\n found = False\n issue = repo.issue(issue_number)\n\n if not allow_duplicates:\n for comment in issue.iter_comments():\n if comment.body == body:\n found = True\n break\n\n if allow_duplicates or not found:\n success = issue.create_comment(body)\n if success:\n click.echo(\"The comment was successfully posted to the issue.\")\n else:\n click.echo(\"There was a failure commenting on the issue.\")\n raise SystemExit(1)\n else:\n click.echo(\"An identical comment was found, skipping posting comment.\")","def test_issue_create_issue(self):\n pass","def problem_comments_append(self, identifier, comment, html=None):\n params = {\"text\": comment}\n if html is not None:\n params[\"html\"] = html\n \n self._post(\"problems/%d/comments\" % identifier, json=params)","def _add_duplicate_comment(\n issue: Issue, *, image: str, repo: str, run: str, stacktrace: str\n) -> IssueComment:\n body = (\n f\"Probably duplicate error:\\n\"\n f\"{_report_body(image=image, repo=repo, run=run, stacktrace=stacktrace)}\"\n )\n return issue.create_comment(body)","def issue_create():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-a\", \"--assignees\", default=[], nargs=\"*\", help=\"users to assign to this issue\"\n )\n parser.add_argument(\"-b\", \"--body\", default=None, help=\"text body of the issue\")\n parser.add_argument(\n \"-c\",\n \"--column\",\n default=DEFAULT_COLUMN_NAME,\n help=\"name of column to place card in\",\n )\n parser.add_argument(\n \"-i\",\n \"--interactive\",\n action=\"store_true\",\n default=DEFAULT_COLUMN_NAME,\n help=\"Edit issue title and body in vim\",\n )\n parser.add_argument(\n \"-l\", \"--labels\", default=None, nargs=\"*\", help=\"labels to add to the new issue\"\n )\n parser.add_argument(\n \"-m\",\n \"--milestone\",\n default=None,\n help=\"milestone id to place this issue in. \"\n \"This should be an integer. \"\n \"Find milestone ids with the `milestones` command.\",\n )\n parser.add_argument(\n \"-p\", \"--project\", default=SCRUM_BOARD_NAME, help=\"project to create issue in\"\n )\n parser.add_argument(\"title\", default=None, nargs=\"?\", help=\"issue title\")\n\n args = parser.parse_args()\n\n # only required arg for creating an issue. can be overridden in interactive mode\n title = args.title\n\n # this can be overridden in interactive mode\n body = args.body\n\n if args.interactive:\n with tempfile.NamedTemporaryFile(\"w\") as fh:\n path = fh.name\n\n editor = os.environ.get(\"EDITOR\", os.environ.get(\"VISUAL\", \"vi\"))\n\n proc = getattr(sh, editor)\n\n proc(path, _fg=True)\n\n with open(path, \"r\") as rfh:\n\n # grab top line as title\n title = rfh.readline().replace(\"\\n\", \"\")\n\n # grab remaining lines as body\n body = \"\".join(rfh.readlines())\n\n session = GithubSession()\n\n additional_args = {\n \"assignees\": args.assignees,\n \"body\": body,\n \"labels\": args.labels,\n \"milestone\": args.milestone,\n }\n\n issue = session.create_issue(title, **additional_args)\n\n column_name = args.column\n project_name = args.project\n\n project = session.get_project(project_name)\n column = session.get_column(project, column_name)\n\n # finally, create the card\n session.create_card(column, issue)\n\n print(json.dumps(issue, indent=2))","def comment():","def comment(self, msg):\n\t\tself._client.add_comment(self, msg)","def create_comment(data):\n\tchecksum = sha256(data).hexdigest()\n\tcomment = checksum + ' ' + data\n\treturn comment","def create_comment(\n match_id: MatchID, creator_id: UserID, body: str\n) -> DbMatchComment:\n comment = DbMatchComment(match_id, creator_id, body)\n\n db.session.add(comment)\n db.session.commit()\n\n return comment","def test_issue_delete_comment(self):\n pass","def create_source_comment(self, overrides=None):\n comment = make_minimal_cs_comment({\n \"id\": \"test_comment\",\n \"thread_id\": self.thread_id,\n \"user_id\": str(self.user.id),\n \"username\": self.user.username,\n \"created_at\": \"2015-05-11T00:00:00Z\",\n \"updated_at\": \"2015-05-11T11:11:11Z\",\n \"body\": \"Test body\",\n \"votes\": {\"up_count\": 4},\n })\n\n comment.update(overrides or {})\n return comment","def create_comment(data):\n checksum = sha256(data).hexdigest()\n comment = checksum + ' ' + data\n return comment","def create_or_update_comment(comment, message, repo, pr_number, token):\n # repo is in the form of \"org/repo\"\n if comment is not None:\n print(\"updating existing comment\")\n # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#update-an-issue-comment # noqa\n response = requests.patch(\n f\"https://api.github.com/repos/{repo}/issues/comments/{comment['id']}\",\n headers=get_headers(token),\n json={\"body\": message},\n )\n else:\n print(\"creating new comment\")\n # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#create-an-issue-comment # noqa\n response = requests.post(\n f\"https://api.github.com/repos/{repo}/issues/{pr_number}/comments\",\n headers=get_headers(token),\n json={\"body\": message},\n )\n\n response.raise_for_status()","def test_issue_get_repo_comments(self):\n pass","def __post_ticket_comment(ticket_id, user_id, comments, token):\n\n user = user_profile(user_id)\n display_name = user['user']['profile']['real_name']\n rt_api.ticket_comment(ticket_id, comments + \"\\n\\n- \" + display_name, True, token=token)","def comment(args):\n message = args.message\n if message is None:\n title = gh.get_issue(owner, repo, args.issue)['title']\n last_comment = '<No previous comments>'\n try:\n last_comment = gh.get_comments(owner, repo, args.issue)[0]['body']\n except IndexError:\n pass\n\n message = get_text_editor_input('Please enter the message for your '\n 'comment. Remember that comments '\n 'support GitHub Flavored Markdown '\n '(GFM). An empty message aborts the '\n 'operation.\\n\\n'\n '#%s %s\\n' % (args.issue, title) +\n 'Last comment:\\n' + last_comment)\n if message == '':\n print('Aborting comment due to empty message.')\n sys.exit(1)\n\n gh.post_comment(owner, repo, args.issue, message)\n sys.exit(0)","def comments(self):\r\n return IssueComments(self)","def comments(self):\r\n return IssueComments(self)","def _comments(self, r, widget, **attr):\n\n label = widget.get(\"label\", \"\")\n # Activate if-required\n #if label and isinstance(label, str):\n if label:\n label = current.T(label)\n icon = widget.get(\"icon\", \"\")\n if icon:\n icon = ICON(icon)\n\n _class = self._lookup_class(r, widget)\n\n comments = \"@ToDo\"\n\n # Render the widget\n output = DIV(H4(icon,\n label,\n _class = \"profile-sub-header\",\n ),\n DIV(comments,\n _class = \"card-holder\",\n ),\n _class = _class,\n )\n\n return output","def add_comment(self):\n comment = Comment(\n title=self.title,\n comment=self.comment,\n rating=self.rating,\n user_from_id=g.user.id,\n user_to_id=self.user_to_id\n )\n db.session.add(comment)\n db.session.commit()\n return comment","def _create_check_complete_pr_comment(self, payload):\n data = self._get_conclusion_data(payload)\n return self._generate_pr_comment_markdown(data)","def create_comment(post, author, content):\n return Comment.objects.create(post=post, author=author, content=content)","async def create_comment_task(\n self, *, new_comment: CommentCreate, task=TaskInDB, requesting_user: UserInDB\n ) -> CommentInDB:\n # check if todo exist\n comment = await self.db.fetch_one(\n query=CREATE_COMMENT_QUERY,\n values={**new_comment.dict(), \"todo_id\": task.todo_id, \"comment_owner\": requesting_user.id},\n )\n return CommentInDB(**comment)","def add_comment_to_announcement():\n vars = request.vars\n logger.info(\"vars.comment_text: %r\" % (vars.comment_text))\n comment_id = db.Comments.insert(\n comment_text = vars.comment_text,\n score = 1,\n ann_id= vars.ann_id,\n )\n comment = db.Announcements(comment_id)\n\n logger.info(\"api:add_comment_to_announcement ==> comment= %r\" % (comment))\n\n return response.json(comment)","def test_issue_post_comment_reaction(self):\n pass","def add_comment(self, text, displayed, username, time,\n proposal, node_id, parent_id, moderator):\n raise NotImplementedError()","def create_comment(self, cr, uid, line_id, context=None):\n if context is None:\n context = {}\n value = self._prepare_comment(cr, uid, line_id, context=context)\n comm_ids = self.create(cr, uid, value, context=context)\n\n return comm_ids","def create_comment(bid, pid):\n # pylint: disable=unused-argument\n form = CommentForm(request.form)\n if request.method == 'POST':\n if form.validate():\n DB.session.add(Comment(pid, current_user.uid, form.text.data))\n DB.session.commit()\n flash('Comment successfully created!')\n else:\n flash(constants.DEFAULT_SUBMISSION_ERR)\n return redirect(request.referrer)","def add_comment(self, project_id, forum_id, comment):\n url = base_url + 'portal/' + str(self.portal_id) + '/projects/' + str(project_id) + '/forums/' + str(forum_id) + '/comments/' \n data = parser.comment_to_json(comment)\n response = zoho_http_client.post(url, self.details, data)\n return parser.get_comments(response)[0]","async def create_comment_todo(\n self, *, new_comment: CommentCreate, todo=TodoInDB, requesting_user: UserInDB\n ) -> CommentInDB:\n comment = await self.db.fetch_one(\n query=CREATE_COMMENT_QUERY,\n values={**new_comment.dict(), \"todo_id\": todo.id, \"comment_owner\": requesting_user.id},\n )\n return CommentInDB(**comment)","def _create_comment(request, course_key, thread_id=None, parent_id=None):\r\n assert isinstance(course_key, CourseKey)\r\n post = request.POST\r\n\r\n if 'body' not in post or not post['body'].strip():\r\n return JsonError(_(\"Body can't be empty\"))\r\n\r\n course = get_course_with_access(request.user, 'load', course_key)\r\n if course.allow_anonymous:\r\n anonymous = post.get('anonymous', 'false').lower() == 'true'\r\n else:\r\n anonymous = False\r\n\r\n if course.allow_anonymous_to_peers:\r\n anonymous_to_peers = post.get('anonymous_to_peers', 'false').lower() == 'true'\r\n else:\r\n anonymous_to_peers = False\r\n\r\n comment = cc.Comment(\r\n anonymous=anonymous,\r\n anonymous_to_peers=anonymous_to_peers,\r\n user_id=request.user.id,\r\n course_id=course_key.to_deprecated_string(),\r\n thread_id=thread_id,\r\n parent_id=parent_id,\r\n body=post[\"body\"]\r\n )\r\n comment.save()\r\n if post.get('auto_subscribe', 'false').lower() == 'true':\r\n user = cc.User.from_django_user(request.user)\r\n user.follow(comment.thread)\r\n if request.is_ajax():\r\n return ajax_content_response(request, course_key, comment.to_dict())\r\n else:\r\n return JsonResponse(utils.safe_content(comment.to_dict()))","def _apply_comment(self, iid, comment):\n data = {\"body\" : comment._body}\n resp = self._post(\n self._base + \"/issues/{}/comments\".format(iid),\n data=self._format_data(data))","def comment(self, content):\n pass","def comment_added(self, event):\n import simplejson\n comment = str(event[\"comment\"])\n author_name = str(event[\"author\"][\"name\"])\n change_url = str(event[\"change\"][\"url\"])\n change_subject = str(event[\"change\"][\"subject\"])\n comment = simplejson.dumps({\n \"issue\": {\n \"notes\": self._prepare_comment_added_template(event)\n }\n })\n # get a unique list of issue IDs\n subject_issue_ids = self.__get_issue_ids(change_subject)\n comment_issue_ids = self.__get_issue_ids(comment)\n issue_ids = list(set(subject_issue_ids + comment_issue_ids))\n for issue_id in issue_ids:\n self.__add_comment(issue_id, comment)","def write_last_jira_comment(f, jira, issue):\n c = jira.comments(issue)\n if len(c) > 0:\n try:\n comment = \"# Last comment:\\n# ---8<---\\n# %s\\n# --->8---\\n\" % \"\\n# \".join(\n c[-1].body.splitlines()\n )\n f.write(comment)\n except UnicodeEncodeError:\n log.debug(\"Can't encode character\")","def comment(self, comment):\r\n\r\n core.FW_conf['connection'].comment(comment)","def test_creates_comment(self):\n comment_id = CommentAndSummary.objects.get(note=self.note).pk\n report = Report.objects.filter(internal_comments__pk=comment_id)[0]\n self.assertEqual(report.pk, self.pk)","def post_comment(self):\n self.post_question()\n return self.client.post(\"api/v2/1/comments\", headers={\"Authorization\": \"{}\".format(self.token())}, data=json.dumps(self.comment), content_type='application/json')","def action_create_task(self):\n self.ensure_one()\n res = super(ProjectIssue, self).action_create_task()\n task_id = res.get('res_id', False)\n if task_id:\n task_obj = self.env['project.task'].browse(task_id)\n task_obj.description = self.description\n return res","def generate_body(issue):\n markdown = \"### {}\\n\".format(issue.pop('title'))\n for k, v in issue.iteritems():\n markdown += \"- {}: {}\\n\".format(k, v)\n return markdown","async def create_reply(*, comment: models.Comment = Depends(resolve_comment), created_comment: CreateComment,\n current_user: models.User = Depends(resolve_current_user), db: Session = Depends(get_db)):\n return crud.create_comment(db, author_id=current_user.id, parent_resub_id=comment.parent_resub_id,\n parent_post_id=comment.parent_post_id, parent_comment_id=comment.id,\n content=created_comment.content)","def generate_comment_body():\n body = {\n \"external_id\": 1,\n \"external_slug\": factories.random_str(),\n \"description\": \"External comment\",\n \"context\": None,\n }\n\n return body","def test_projects_id_comments_post(self):\n project = Comment()\n response = self.client.open('/project-tracker/projects/{id}/comments'.format(id=56),\n method='POST',\n data=json.dumps(project),\n content_type='application/json')\n self.assert200(response, \"Response body is : \" + response.data.decode('utf-8'))","def test_issue_edit_comment_deprecated(self):\n pass","def test_comment_creation(self):\n response = self.client.post(reverse('posts:comment_create'),\n data={\n 'post': self.post.id,\n 'user': self.user.id,\n 'comment_description': 'This is a '\n 'test_comment'\n }, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(response.data, {\n 'post': self.post.id,\n 'user': self.user.id,\n 'comment_description': 'This is a '\n 'test_comment'\n })","def issue(msg: telebot.types.Message) -> None:\n data = msg.text.split()\n if len(data) == 1:\n bot.send_message(\n msg.from_user.id,\n 'Use this command to tell the developer about an issue. '\n 'Example usage: `/issue I got 4 in a row but game did not end.`',\n parse_mode='Markdown'\n )\n else:\n m = ' '.join(data[1:])\n for dev_id in config.DEV_ID:\n bot.send_message(\n dev_id,\n '<b>Issue</b> from <a href=\"tg://user?id={id}\">{first_name}</a>.\\n'.format(\n first_name=msg.from_user.first_name,\n id=msg.from_user.id\n ) +\n 'ID: {id}\\n'.format(id=msg.from_user.id) +\n '<i>{message}</i>\\n'.format(message=m),\n parse_mode='HTML'\n )\n bot.reply_to(\n msg,\n 'Developer was notified. Thank you for your time.'\n )","def _create_system_comment(\n action_type, user, post, changelog=None, attachments=None, stage=None\n) -> Comment:\n if not config.SYSTEM_USER_ID:\n return None\n\n if not attachments:\n attachments = ()\n if not stage:\n stage = post.stage\n\n system_user = User.objects.get(id=config.SYSTEM_USER_ID)\n comment = Comment()\n comment.commentable = post\n comment.type = Comment.TYPE_SYSTEM\n comment.user = system_user\n\n # Depending on action type, fill different fields\n meta = {\n \"action\": action_type,\n \"user\": {\"id\": user.id, \"str\": user.__str__()},\n \"files\": [],\n }\n\n if action_type == Comment.SYSTEM_ACTION_CHANGE_META:\n meta[\"changelog\"] = changelog\n\n elif action_type == Comment.SYSTEM_ACTION_UPDATE:\n if len(attachments) > 0:\n meta[\"files\"] = [\n {\"id\": a.id, \"str\": a.original_filename} for a in attachments\n ]\n\n elif action_type == Comment.SYSTEM_ACTION_SET_STAGE:\n meta[\"stage\"] = {\"id\": post.stage.id, \"str\": post.stage.__str__()}\n\n # Assign builded meta to comment and save\n comment.meta[\"comment\"] = meta\n comment.save()\n\n return comment","def format_comment(filename, line_n, offset, code, text):\n return f'{code}: {text}'","def _create_issue(self, dep_name, dep_latest_version, is_subtask=False, parent_key=None):\n logging.info(\"Creating a new JIRA issue to track {0} upgrade process\".format(dep_name))\n assignee, owners = self._find_owners(dep_name)\n summary = _ISSUE_SUMMARY_PREFIX + dep_name\n if dep_latest_version:\n summary = summary + \" \" + dep_latest_version\n description = \"\"\"\\n\\n{0}\\n\n Please review and upgrade the {1} to the latest version {2} \\n \n cc: \"\"\".format(\n datetime.today(),\n dep_name,\n dep_latest_version\n )\n for owner in owners:\n description += \"[~{0}], \".format(owner)\n try:\n if not is_subtask:\n issue = self.jira.create_issue(summary, [_JIRA_COMPONENT], description, assignee=assignee)\n else:\n issue = self.jira.create_issue(summary, [_JIRA_COMPONENT], description, assignee=assignee, parent_key=parent_key)\n except Exception as e:\n logging.error(\"Failed creating issue: \"+ str(e))\n raise e\n return issue","def add_comment(request):\n if request.method != 'POST':\n return HttpResponseRedirect(reverse('wainz.views.composite'))\n else:\n img_id = request.POST['id']\n try:\n img = Image.objects.get(pk=img_id)\n except:\n return HttpResponseRedirect(reverse('wainz.views.composite'))\n comment_text = request.POST['comment']\n #TODO sanitize input\n comment = ImageComment()\n comment.submission_date = timezone.now()\n comment.comment_text= comment_text\n comment.image_id = img_id\n comment.submitter_id = int(request.POST['uid'])\n comment.save()\n return rest.rest_success(request, img_id)","def _create_issue(*, image: str, repo: str, run: str, stacktrace: str) -> Issue:\n title = f\"Automatic error report from {repo}\"\n body = _report_body(image=image, repo=repo, run=run, stacktrace=stacktrace)\n return TAGBOT_ISSUES_REPO.create_issue(title, body)","def comment(request, object_id):\n send_charob = request.user.char_ob\n rec_charob = get_character_from_ob(object_id)\n comment_txt = request.POST[\"comment\"]\n roster.create_comment(send_charob, rec_charob, comment_txt)\n return HttpResponseRedirect(reverse(\"character:sheet\", args=(object_id,)))","def create_comment(project_id, session_id, annotation_id, comment_id=None):\n helpers.abort_if_invalid_parameters(project_id, session_id)\n user = helpers.abort_if_unauthorized(Project.query.get(project_id))\n if comment_id:\n helpers.abort_if_unknown_comment(comment_id, annotation_id)\n\n data = helpers.jsonify_request_or_abort()\n\n schema = UserAnnotationCommentSchema()\n helpers.abort_if_errors_in_validation(schema.validate(data))\n # Note: comment_id can be null, which represents that it is a parent\n comment = CommentsModel(data['content'], comment_id, user.id, annotation_id)\n db.session.add(comment)\n db.session.commit()\n\n # Determine which type of comment the response is to: nested or a root comment\n if comment_id:\n _comment = CommentsModel.query.filter_by(parent_id=comment_id).first()\n else:\n _comment = RootComment.query.get(annotation_id)\n parent_user_id = _comment.user_id\n usr = User.query.get(parent_user_id)\n\n if user.id != usr.id:\n InterviewSession.email_commentor(usr, project_id, session_id)\n\n fcm.notify_participants_user_commented(project_id, session_id)\n return custom_response(200, data=schema.dump(comment))","def create_comment(request):\n\n # get data\n in_data = getRequestData(request)\n\n # get the Thread associated with the comments\n mythread = Thread.objects.get(id=in_data.get('mythreadid'))\n\n # save in database\n try:\n comment = Comment(pub_date = datetime.datetime.now(pytz.timezone('US/Eastern')), username = in_data.get('myusername'), text = in_data.get('mytext'), score = 0, thread = mythread )\n comment.save()\n except:\n return HttpResponseBadRequest('Error saving to database!')\n\n return JsonResponse(in_data)","def comment(self, text):\n url = \"https://api.imgur.com/3/comment\"\n payload = {'image_id': self.id, 'comment': text}\n resp = self._imgur._send_request(url, params=payload, needs_auth=True,\n method='POST')\n return Comment(resp, imgur=self._imgur, has_fetched=False)","def _format_comment(self):\n return '\"\"\"' + self.comment + '\"\"\"'","def _get_comment(self, cell: NotebookNode, resources: ResourcesDict) -> None:\n\n # retrieve or create the comment object from the database\n comment = self.gradebook.find_comment(\n cell.metadata['nbgrader']['grade_id'],\n self.notebook_id,\n self.assignment_id,\n self.student_id)\n\n # save it in the notebook\n cell.metadata.nbgrader['comment'] = comment.comment","def generate_description(jira_key: str, comment: str) -> str:\n if jira_key in comment:\n return comment\n else:\n return '{}: {}'.format(jira_key, comment)","def create_issue(self, group, form_data, **kwargs):\n headers = { \"X-Redmine-API-Key\": self.get_option('key', group.project),\n 'content-type': 'application/json' }\n verifySSL = self.get_option('verify_ssl', group.project)\n url = urlparse.urljoin(self.get_option('host', group.project), \"issues.json\")\n payload = {\n 'project_id': self.get_option('project_id', group.project),\n 'tracker_id': self.get_option('tracker_id', group.project),\n 'status_id': '0',\n 'subject': form_data['title'].encode('utf-8'),\n 'description': form_data['description'].encode('utf-8'),\n }\n #print >> sys.stderr, \"url:\", url\n #print >> sys.stderr, \"payload:\\n\", pformat(payload)\n #print >> sys.stderr, pformat(group)\n #print >> sys.stderr, pformat(dir(group))\n\n try:\n r = requests.post(url, data=json.dumps({'issue': payload}), headers=headers, verify=verifySSL)\n except requests.exceptions.HTTPError as e:\n raise forms.ValidationError('Unable to reach Redmine host: %s' % repr(e))\n\n try:\n data = json.loads(r.text)\n except json.JSONDecodeError as e:\n #print >> sys.stderr, \"ERROR: %s\" % e\n #print >> sys.stderr, \"RESP:\", r.text\n raise forms.ValidationError('Unable to reach Redmine host: %s' % repr(e))\n\n if not 'issue' in data or not 'id' in data['issue']:\n raise forms.ValidationError('Unable to create redmine ticket')\n\n return data['issue']['id']","def comment(self,obj_ID=None,comment='empty',**kwargs):\n\n if obj_ID==None:\n print('No object UID given, or invalid UID given',file=sys.stderr)\n return {'error':-1}\n\n if not (isinstance(comment,str) or isinstance(comment,unicode)):\n print('Error in mpo_commment, should be a plain string')\n return {'error':-1}\n\n r=self.post(self.COMMENT_RT,None,obj_ID,data={'content':str(comment)})\n return r","def proto_comment(self, proto_user, proto_post):\n return baker.make(Comment, user=proto_user, post=proto_post)","def register_comment(self, overrides=None):\n cs_comment = make_minimal_cs_comment({\n \"id\": \"test_comment\",\n \"course_id\": str(self.course.id),\n \"thread_id\": \"test_thread\",\n \"username\": self.user.username,\n \"user_id\": str(self.user.id),\n \"body\": \"Original body\",\n })\n cs_comment.update(overrides or {})\n self.register_get_comment_response(cs_comment)\n self.register_put_comment_response(cs_comment)\n self.register_post_comment_response(cs_comment, thread_id=\"test_thread\")","def make_comment_data(self, comment_id, parent_id=None, children=[]): # pylint: disable=W0102\n return make_minimal_cs_comment({\n \"id\": comment_id,\n \"parent_id\": parent_id,\n \"course_id\": str(self.course.id),\n \"thread_id\": self.thread_id,\n \"thread_type\": \"discussion\",\n \"username\": self.user.username,\n \"user_id\": str(self.user.id),\n \"created_at\": \"2015-06-03T00:00:00Z\",\n \"updated_at\": \"2015-06-03T00:00:00Z\",\n \"body\": \"Original body\",\n \"children\": children,\n })","def should_add_pr_comment(self):\n pass","def create_new_issue(\n self,\n token: str,\n object_id: str,\n customer_id: str,\n project_id: str,\n scope_id: str,\n issue_name: str,\n region: str,\n business_unit: str,\n date_of_raise: str,\n due_date: str,\n nature_of_issue: str,\n criticality: str,\n issue_description: str,\n impact_value: str,\n currency: str,\n impact_on: str,\n document_ref: dict,\n issue_owner: dict,\n resolution_path: str,\n ):\n\n # Type guarding\n assert check_argument_types()\n\n # TODO: make table name environment variable\n table_name = f\"Projects-{customer_id}\"\n\n # Key\n key = {\"projectId\": project_id, \"customerId\": customer_id}\n\n # Projection Expression\n projection_expression = \", \".join([\"projectId\", \"code\"])\n\n # Check if customer and project exist\n logger.info(f\"Checking if project ID or organization ID exists: {key}\")\n response, _ = self._db.read_single_item(table_name, key, projection_expression)\n\n # Get project code\n project_code = response[\"code\"]\n\n # Request body\n dynamo_object = {\n \"scopeId\": scope_id,\n \"issueName\": issue_name,\n \"region\": region,\n \"businessUnit\": business_unit,\n \"dateOfRaise\": date_of_raise,\n \"dueDate\": due_date,\n \"natureOfIssue\": nature_of_issue,\n \"criticality\": criticality,\n \"issueDescription\": issue_description,\n \"status\": \"open\",\n \"impactValue\": impact_value,\n \"currency\": currency,\n \"impactOn\": impact_on,\n \"documentRef\": document_ref,\n \"issueOwner\": issue_owner,\n \"resolutionPath\": resolution_path,\n \"lastUpdated\": str(date.today()),\n \"issueId\": object_id,\n }\n\n # Send project onboarding email\n logger.info(\"Sending project onboarding email\")\n self._email.send_template_email(\n source=getenv(\"SOURCE_EMAIL_ADDRESS\"),\n template_name=getenv(\"ISSUE_ASSIGNMENT_TEMPLATE\"),\n template_data=json.dumps(\n {\n \"issueId\": f'\"{dynamo_object[\"issueName\"]}\"',\n \"projectCode\": project_code,\n }\n ),\n bcc_addresses=[issue_owner[\"email\"]],\n )\n\n # Dynamo update expressions & update\n logger.info(\"Create new project issue\")\n update_expression = (\n f\"SET scopes.#scopeId.issues.#IssueId = :{dynamo_object['issueId']}\"\n )\n expression_attribute_names = {\n \"#scopeId\": scope_id,\n \"#IssueId\": dynamo_object[\"issueId\"]\n }\n expression_attribute_values = {f\":{dynamo_object['issueId']}\": dynamo_object}\n self._db.update_item(\n table_name,\n key,\n update_expression,\n expression_attribute_names,\n expression_attribute_values,\n )\n\n # Log workflow\n message = [f\"Created new issue: {issue_name}\"]\n workflow = Workflows.update_workflows(\n token, \"Create\", message, project_id, dynamo_object[\"issueId\"]\n )\n self._db.create_item(f\"Workflows-{customer_id}\", workflow)\n\n logger.info(\"New issue created successfully\")\n return \"New issue created successfully\", 200","def comment(self, body, incident_id):\n payload = {\"comment\":{\"body\":body, \"is_private\":\"false\"}}\n response = self.session.post(\n \"{0}/incidents/{1}/comments.json\".format(self.uri, incident_id),\n json=payload\n )\n return response.status_code","def add_comment(self, message):\n params = {\"ffauth_device_id\": self._DiscretelyAuthenticatedObject__device_id,\n \"ffauth_secret\": self._DiscretelyAuthenticatedObject__device_token}\n data = {\"data\": str(\n {\"event\": {\"type\": \"comment\", \"message\": message, \"assessment_details_id\": self.__assessmentDetailsId},\n \"recipient\": {\"guid\": self._DiscretelyAuthenticatedObject__guid, \"type\": \"user\"}})}\n requests.post(\n self._DiscretelyAuthenticatedObject__portal + \"/_api/1.0/tasks/\" + str(self.id) + \"/responses\",\n params=params, data=data)","def post_comment_on_line(owner, repo, commit_sha, line_n, filename, message):\n res = post(\n GIT_COMMENT_URL.format(host=host_api, owner=owner, repo=repo,\n commit_sha=commit_sha),\n auth=auth, json={\n 'body': message,\n 'path': filename,\n 'position': line_n,\n })\n\n assert res.status_code == 201, f'Got non 201 status, ' \\\n f'error message: {res.content}'","def create_issue(\n self,\n summary: str,\n body: str,\n labels: Optional[Iterable[str]] = None,\n links: Iterable[str] = (),\n ) -> Issue:\n issue = self.jira.create_issue(\n project=self.project,\n summary=summary,\n description=body,\n labels=labels,\n issuetype={\"name\": \"Task\"},\n )\n for ln in links:\n self.jira.create_issue_link(\n type=\"is caused by\", inwardIssue=issue.key, outwardIssue=ln\n )\n return issue","def add_comment(cls, post_id, user_id, content):\n c = cls(parent=comment_key(),\n post_id=post_id,\n user_id=user_id,\n content=content)\n c.put()","def on_commit_comment(self, payload):\n pass","def get_comment(self, uid: str) -> _model.AbstractComment:\n pass","def submit_feedback(self, title, description, state):\n\n body = f\"\"\"\n**User Issue**\nEmail: {self.user.email}\nUser Agent: {get_user_agent(self.request)}\n\n{description}\n\n<details>\n\n<summary>Redux state</summary>\n\n<p>\n\n```json\n{json.dumps(state, indent=2)}\n```\n\n</p>\n</details>\n \"\"\"\n\n r = requests.post(\n 'https://api.github.com/repos/alexmojaki/futurecoder/issues',\n json={'title': title,\n 'body': body,\n 'labels': ['user', 'bug']},\n headers=dict(\n Authorization='token ' + settings.GITHUB_TOKEN,\n ),\n )\n\n assert r.status_code == 201"],"string":"[\n \"def test_issue_create_comment(self):\\n pass\",\n \"def create_comment(self, body):\\n return self.client.request(\\n \\\"{}/issues/{}/comments\\\".format(self.repo.base_path, self.num),\\n params={\\\"body\\\": body},\\n method=\\\"POST\\\"\\n )\",\n \"def test_issue_get_comment(self):\\n pass\",\n \"def add_comment() -> str:\\n if \\\"markdown\\\" in request.form:\\n if \\\"file\\\" in request.form:\\n comment = Comment(\\n markdown=request.form[\\\"markdown\\\"],\\n submission_id=Submission.query.filter(\\n Submission.filepath.contains(request.form[\\\"file\\\"])\\n )\\n .first()\\n .id,\\n cell_id=request.form[\\\"cell_id\\\"] if \\\"cell_id\\\" in request.form else None,\\n user=UserModel.get_by_token(session[\\\"token\\\"]),\\n )\\n # If not cell_id this is a general comment\\n comment.save()\\n else:\\n return \\\"Missing file or cell_id\\\", 400\\n else:\\n return \\\"Missing markdown\\\", 400\\n\\n comment_maker = get_template_attribute(\\\"_macros.html\\\", \\\"comment_block\\\")\\n return comment_maker(comment)\",\n \"async def createComment(self, *args, **kwargs):\\n\\n return await self._makeApiCall(self.funcinfo[\\\"createComment\\\"], *args, **kwargs)\",\n \"def comment(self, comment_id):\\r\\n return IssueComment(self, comment_id)\",\n \"def comment(self, comment_id):\\r\\n return IssueComment(self, comment_id)\",\n \"def addcomment(accountable, body):\\n\\n r = accountable.issue_add_comment(body)\\n headers = sorted(['author_name', 'body', 'updated'])\\n rows = [[v for k, v in sorted(r.items()) if k in headers]]\\n rows.insert(0, headers)\\n print_table(SingleTable(rows))\",\n \"def createcomment(request, pk):\\n issue = get_object_or_404(Issue, pk=pk)\\n if request.method == \\\"POST\\\":\\n form = CommentCreationForm(request.POST)\\n if form.is_valid():\\n comment = form.save(commit=False)\\n comment.issue = issue\\n comment.author = request.user\\n comment.created_at = timezone.now()\\n comment.save()\\n return redirect('office:issue', pk=pk)\\n else:\\n form = CommentForm()\\n return render(request, 'blog/add_comment_to_post.html', {'form': form})\",\n \"def create(self, number, message, user=None, repo=None):\\n request = self.make_request('issues.comments.create', user=user,\\n repo=repo, number=number, body={'body': message})\\n return self._post(request)\",\n \"def on_issue_comment(self, payload):\\n pass\",\n \"def test_issue_edit_comment(self):\\n pass\",\n \"def make_comment(self, offset, comment):\\n #self.ret = idc.MakeComm(offset, comment)\\n return self.ret\",\n \"def add_comment(self, issue, comment):\\n return self.get_jira().add_comment(issue, comment)\",\n \"def __add_comment(self, issue_id, comment):\\n import httplib2\\n http = httplib2.Http() \\n response, content = http.request(\\n uri=self.__issue_url % int(issue_id),\\n method='PUT',\\n body=comment,\\n headers={\\n 'X-Redmine-API-Key': self.__api_key,\\n 'Content-type': 'application/json'\\n }\\n )\\n print(response)\\n print(content)\",\n \"def create_issue(self, data, **kwargs):\\n raise NotImplementedError\",\n \"def create_issue(self, data, **kwargs):\\n raise NotImplementedError\",\n \"def add_comment_to_issue(self, issue, comment, visibility=None):\\r\\n self.jira.add_comment(issue=issue, body=comment)\",\n \"def _generate_pr_comment_markdown(self, data):\\n pass\",\n \"def post(self, pid, sid, aid):\\n return create_comment(pid, sid, aid)\",\n \"def cli(ctx, comment, metadata=\\\"\\\"):\\n return ctx.gi.cannedcomments.add_comment(comment, metadata=metadata)\",\n \"def post(self, pid, sid, aid, cid):\\n return create_comment(pid, sid, aid, cid)\",\n \"def make_comment(body, who, when):\\n out = et.Element(\\\"comment\\\")\\n et.SubElement(out, \\\"author\\\").text = who\\n et.SubElement(out, \\\"date\\\").text = when\\n et.SubElement(out, \\\"content-type\\\").text = \\\"text/plain\\\"\\n # BE import doesn't like empty comment bodies\\n if body==\\\"\\\":\\n body = \\\"(no content)\\\"\\n et.SubElement(out, \\\"body\\\").text = body\\n return out\",\n \"def create(self, comment):\\r\\n url = self.get_url()\\r\\n\\r\\n # when creating commits they don't get wrapped in {\\\"body\\\": <comment>}\\r\\n return http.Request('POST', url, params=comment), parsers.parse_json\",\n \"def test_issue_get_comments(self):\\n pass\",\n \"def create_incident_comment(self, incident_id, data):\\n try:\\n uri = '/incidents/{}/comments'.format(incident_id)\\n resilient_client = self.rest_client()\\n heading = \\\"Raw Proofpoint TRAP Event Payload:\\\\n\\\"\\n note = {\\n 'format': 'text',\\n 'content': '{}{}'.format(heading, pprint.pformat(data, indent=4))\\n }\\n payload = {'text': note}\\n comment_response = resilient_client.post(uri=uri, payload=payload)\\n return comment_response\\n\\n except SimpleHTTPException as ex:\\n LOG.error(\\\"Failed to add note for incident %d: %s\\\", incident_id, ex)\",\n \"def create_comment(request, course_id, thread_id):\\r\\n if cc_settings.MAX_COMMENT_DEPTH is not None:\\r\\n if cc_settings.MAX_COMMENT_DEPTH < 0:\\r\\n return JsonError(_(\\\"Comment level too deep\\\"))\\r\\n return _create_comment(request, SlashSeparatedCourseKey.from_deprecated_string(course_id), thread_id=thread_id)\",\n \"def add_comment_to_issue(repo, issue_number, body, allow_duplicates):\\n found = False\\n issue = repo.issue(issue_number)\\n\\n if not allow_duplicates:\\n for comment in issue.iter_comments():\\n if comment.body == body:\\n found = True\\n break\\n\\n if allow_duplicates or not found:\\n success = issue.create_comment(body)\\n if success:\\n click.echo(\\\"The comment was successfully posted to the issue.\\\")\\n else:\\n click.echo(\\\"There was a failure commenting on the issue.\\\")\\n raise SystemExit(1)\\n else:\\n click.echo(\\\"An identical comment was found, skipping posting comment.\\\")\",\n \"def test_issue_create_issue(self):\\n pass\",\n \"def problem_comments_append(self, identifier, comment, html=None):\\n params = {\\\"text\\\": comment}\\n if html is not None:\\n params[\\\"html\\\"] = html\\n \\n self._post(\\\"problems/%d/comments\\\" % identifier, json=params)\",\n \"def _add_duplicate_comment(\\n issue: Issue, *, image: str, repo: str, run: str, stacktrace: str\\n) -> IssueComment:\\n body = (\\n f\\\"Probably duplicate error:\\\\n\\\"\\n f\\\"{_report_body(image=image, repo=repo, run=run, stacktrace=stacktrace)}\\\"\\n )\\n return issue.create_comment(body)\",\n \"def issue_create():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"-a\\\", \\\"--assignees\\\", default=[], nargs=\\\"*\\\", help=\\\"users to assign to this issue\\\"\\n )\\n parser.add_argument(\\\"-b\\\", \\\"--body\\\", default=None, help=\\\"text body of the issue\\\")\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--column\\\",\\n default=DEFAULT_COLUMN_NAME,\\n help=\\\"name of column to place card in\\\",\\n )\\n parser.add_argument(\\n \\\"-i\\\",\\n \\\"--interactive\\\",\\n action=\\\"store_true\\\",\\n default=DEFAULT_COLUMN_NAME,\\n help=\\\"Edit issue title and body in vim\\\",\\n )\\n parser.add_argument(\\n \\\"-l\\\", \\\"--labels\\\", default=None, nargs=\\\"*\\\", help=\\\"labels to add to the new issue\\\"\\n )\\n parser.add_argument(\\n \\\"-m\\\",\\n \\\"--milestone\\\",\\n default=None,\\n help=\\\"milestone id to place this issue in. \\\"\\n \\\"This should be an integer. \\\"\\n \\\"Find milestone ids with the `milestones` command.\\\",\\n )\\n parser.add_argument(\\n \\\"-p\\\", \\\"--project\\\", default=SCRUM_BOARD_NAME, help=\\\"project to create issue in\\\"\\n )\\n parser.add_argument(\\\"title\\\", default=None, nargs=\\\"?\\\", help=\\\"issue title\\\")\\n\\n args = parser.parse_args()\\n\\n # only required arg for creating an issue. can be overridden in interactive mode\\n title = args.title\\n\\n # this can be overridden in interactive mode\\n body = args.body\\n\\n if args.interactive:\\n with tempfile.NamedTemporaryFile(\\\"w\\\") as fh:\\n path = fh.name\\n\\n editor = os.environ.get(\\\"EDITOR\\\", os.environ.get(\\\"VISUAL\\\", \\\"vi\\\"))\\n\\n proc = getattr(sh, editor)\\n\\n proc(path, _fg=True)\\n\\n with open(path, \\\"r\\\") as rfh:\\n\\n # grab top line as title\\n title = rfh.readline().replace(\\\"\\\\n\\\", \\\"\\\")\\n\\n # grab remaining lines as body\\n body = \\\"\\\".join(rfh.readlines())\\n\\n session = GithubSession()\\n\\n additional_args = {\\n \\\"assignees\\\": args.assignees,\\n \\\"body\\\": body,\\n \\\"labels\\\": args.labels,\\n \\\"milestone\\\": args.milestone,\\n }\\n\\n issue = session.create_issue(title, **additional_args)\\n\\n column_name = args.column\\n project_name = args.project\\n\\n project = session.get_project(project_name)\\n column = session.get_column(project, column_name)\\n\\n # finally, create the card\\n session.create_card(column, issue)\\n\\n print(json.dumps(issue, indent=2))\",\n \"def comment():\",\n \"def comment(self, msg):\\n\\t\\tself._client.add_comment(self, msg)\",\n \"def create_comment(data):\\n\\tchecksum = sha256(data).hexdigest()\\n\\tcomment = checksum + ' ' + data\\n\\treturn comment\",\n \"def create_comment(\\n match_id: MatchID, creator_id: UserID, body: str\\n) -> DbMatchComment:\\n comment = DbMatchComment(match_id, creator_id, body)\\n\\n db.session.add(comment)\\n db.session.commit()\\n\\n return comment\",\n \"def test_issue_delete_comment(self):\\n pass\",\n \"def create_source_comment(self, overrides=None):\\n comment = make_minimal_cs_comment({\\n \\\"id\\\": \\\"test_comment\\\",\\n \\\"thread_id\\\": self.thread_id,\\n \\\"user_id\\\": str(self.user.id),\\n \\\"username\\\": self.user.username,\\n \\\"created_at\\\": \\\"2015-05-11T00:00:00Z\\\",\\n \\\"updated_at\\\": \\\"2015-05-11T11:11:11Z\\\",\\n \\\"body\\\": \\\"Test body\\\",\\n \\\"votes\\\": {\\\"up_count\\\": 4},\\n })\\n\\n comment.update(overrides or {})\\n return comment\",\n \"def create_comment(data):\\n checksum = sha256(data).hexdigest()\\n comment = checksum + ' ' + data\\n return comment\",\n \"def create_or_update_comment(comment, message, repo, pr_number, token):\\n # repo is in the form of \\\"org/repo\\\"\\n if comment is not None:\\n print(\\\"updating existing comment\\\")\\n # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#update-an-issue-comment # noqa\\n response = requests.patch(\\n f\\\"https://api.github.com/repos/{repo}/issues/comments/{comment['id']}\\\",\\n headers=get_headers(token),\\n json={\\\"body\\\": message},\\n )\\n else:\\n print(\\\"creating new comment\\\")\\n # API doc: https://docs.github.com/en/rest/issues/comments?apiVersion=2022-11-28#create-an-issue-comment # noqa\\n response = requests.post(\\n f\\\"https://api.github.com/repos/{repo}/issues/{pr_number}/comments\\\",\\n headers=get_headers(token),\\n json={\\\"body\\\": message},\\n )\\n\\n response.raise_for_status()\",\n \"def test_issue_get_repo_comments(self):\\n pass\",\n \"def __post_ticket_comment(ticket_id, user_id, comments, token):\\n\\n user = user_profile(user_id)\\n display_name = user['user']['profile']['real_name']\\n rt_api.ticket_comment(ticket_id, comments + \\\"\\\\n\\\\n- \\\" + display_name, True, token=token)\",\n \"def comment(args):\\n message = args.message\\n if message is None:\\n title = gh.get_issue(owner, repo, args.issue)['title']\\n last_comment = '<No previous comments>'\\n try:\\n last_comment = gh.get_comments(owner, repo, args.issue)[0]['body']\\n except IndexError:\\n pass\\n\\n message = get_text_editor_input('Please enter the message for your '\\n 'comment. Remember that comments '\\n 'support GitHub Flavored Markdown '\\n '(GFM). An empty message aborts the '\\n 'operation.\\\\n\\\\n'\\n '#%s %s\\\\n' % (args.issue, title) +\\n 'Last comment:\\\\n' + last_comment)\\n if message == '':\\n print('Aborting comment due to empty message.')\\n sys.exit(1)\\n\\n gh.post_comment(owner, repo, args.issue, message)\\n sys.exit(0)\",\n \"def comments(self):\\r\\n return IssueComments(self)\",\n \"def comments(self):\\r\\n return IssueComments(self)\",\n \"def _comments(self, r, widget, **attr):\\n\\n label = widget.get(\\\"label\\\", \\\"\\\")\\n # Activate if-required\\n #if label and isinstance(label, str):\\n if label:\\n label = current.T(label)\\n icon = widget.get(\\\"icon\\\", \\\"\\\")\\n if icon:\\n icon = ICON(icon)\\n\\n _class = self._lookup_class(r, widget)\\n\\n comments = \\\"@ToDo\\\"\\n\\n # Render the widget\\n output = DIV(H4(icon,\\n label,\\n _class = \\\"profile-sub-header\\\",\\n ),\\n DIV(comments,\\n _class = \\\"card-holder\\\",\\n ),\\n _class = _class,\\n )\\n\\n return output\",\n \"def add_comment(self):\\n comment = Comment(\\n title=self.title,\\n comment=self.comment,\\n rating=self.rating,\\n user_from_id=g.user.id,\\n user_to_id=self.user_to_id\\n )\\n db.session.add(comment)\\n db.session.commit()\\n return comment\",\n \"def _create_check_complete_pr_comment(self, payload):\\n data = self._get_conclusion_data(payload)\\n return self._generate_pr_comment_markdown(data)\",\n \"def create_comment(post, author, content):\\n return Comment.objects.create(post=post, author=author, content=content)\",\n \"async def create_comment_task(\\n self, *, new_comment: CommentCreate, task=TaskInDB, requesting_user: UserInDB\\n ) -> CommentInDB:\\n # check if todo exist\\n comment = await self.db.fetch_one(\\n query=CREATE_COMMENT_QUERY,\\n values={**new_comment.dict(), \\\"todo_id\\\": task.todo_id, \\\"comment_owner\\\": requesting_user.id},\\n )\\n return CommentInDB(**comment)\",\n \"def add_comment_to_announcement():\\n vars = request.vars\\n logger.info(\\\"vars.comment_text: %r\\\" % (vars.comment_text))\\n comment_id = db.Comments.insert(\\n comment_text = vars.comment_text,\\n score = 1,\\n ann_id= vars.ann_id,\\n )\\n comment = db.Announcements(comment_id)\\n\\n logger.info(\\\"api:add_comment_to_announcement ==> comment= %r\\\" % (comment))\\n\\n return response.json(comment)\",\n \"def test_issue_post_comment_reaction(self):\\n pass\",\n \"def add_comment(self, text, displayed, username, time,\\n proposal, node_id, parent_id, moderator):\\n raise NotImplementedError()\",\n \"def create_comment(self, cr, uid, line_id, context=None):\\n if context is None:\\n context = {}\\n value = self._prepare_comment(cr, uid, line_id, context=context)\\n comm_ids = self.create(cr, uid, value, context=context)\\n\\n return comm_ids\",\n \"def create_comment(bid, pid):\\n # pylint: disable=unused-argument\\n form = CommentForm(request.form)\\n if request.method == 'POST':\\n if form.validate():\\n DB.session.add(Comment(pid, current_user.uid, form.text.data))\\n DB.session.commit()\\n flash('Comment successfully created!')\\n else:\\n flash(constants.DEFAULT_SUBMISSION_ERR)\\n return redirect(request.referrer)\",\n \"def add_comment(self, project_id, forum_id, comment):\\n url = base_url + 'portal/' + str(self.portal_id) + '/projects/' + str(project_id) + '/forums/' + str(forum_id) + '/comments/' \\n data = parser.comment_to_json(comment)\\n response = zoho_http_client.post(url, self.details, data)\\n return parser.get_comments(response)[0]\",\n \"async def create_comment_todo(\\n self, *, new_comment: CommentCreate, todo=TodoInDB, requesting_user: UserInDB\\n ) -> CommentInDB:\\n comment = await self.db.fetch_one(\\n query=CREATE_COMMENT_QUERY,\\n values={**new_comment.dict(), \\\"todo_id\\\": todo.id, \\\"comment_owner\\\": requesting_user.id},\\n )\\n return CommentInDB(**comment)\",\n \"def _create_comment(request, course_key, thread_id=None, parent_id=None):\\r\\n assert isinstance(course_key, CourseKey)\\r\\n post = request.POST\\r\\n\\r\\n if 'body' not in post or not post['body'].strip():\\r\\n return JsonError(_(\\\"Body can't be empty\\\"))\\r\\n\\r\\n course = get_course_with_access(request.user, 'load', course_key)\\r\\n if course.allow_anonymous:\\r\\n anonymous = post.get('anonymous', 'false').lower() == 'true'\\r\\n else:\\r\\n anonymous = False\\r\\n\\r\\n if course.allow_anonymous_to_peers:\\r\\n anonymous_to_peers = post.get('anonymous_to_peers', 'false').lower() == 'true'\\r\\n else:\\r\\n anonymous_to_peers = False\\r\\n\\r\\n comment = cc.Comment(\\r\\n anonymous=anonymous,\\r\\n anonymous_to_peers=anonymous_to_peers,\\r\\n user_id=request.user.id,\\r\\n course_id=course_key.to_deprecated_string(),\\r\\n thread_id=thread_id,\\r\\n parent_id=parent_id,\\r\\n body=post[\\\"body\\\"]\\r\\n )\\r\\n comment.save()\\r\\n if post.get('auto_subscribe', 'false').lower() == 'true':\\r\\n user = cc.User.from_django_user(request.user)\\r\\n user.follow(comment.thread)\\r\\n if request.is_ajax():\\r\\n return ajax_content_response(request, course_key, comment.to_dict())\\r\\n else:\\r\\n return JsonResponse(utils.safe_content(comment.to_dict()))\",\n \"def _apply_comment(self, iid, comment):\\n data = {\\\"body\\\" : comment._body}\\n resp = self._post(\\n self._base + \\\"/issues/{}/comments\\\".format(iid),\\n data=self._format_data(data))\",\n \"def comment(self, content):\\n pass\",\n \"def comment_added(self, event):\\n import simplejson\\n comment = str(event[\\\"comment\\\"])\\n author_name = str(event[\\\"author\\\"][\\\"name\\\"])\\n change_url = str(event[\\\"change\\\"][\\\"url\\\"])\\n change_subject = str(event[\\\"change\\\"][\\\"subject\\\"])\\n comment = simplejson.dumps({\\n \\\"issue\\\": {\\n \\\"notes\\\": self._prepare_comment_added_template(event)\\n }\\n })\\n # get a unique list of issue IDs\\n subject_issue_ids = self.__get_issue_ids(change_subject)\\n comment_issue_ids = self.__get_issue_ids(comment)\\n issue_ids = list(set(subject_issue_ids + comment_issue_ids))\\n for issue_id in issue_ids:\\n self.__add_comment(issue_id, comment)\",\n \"def write_last_jira_comment(f, jira, issue):\\n c = jira.comments(issue)\\n if len(c) > 0:\\n try:\\n comment = \\\"# Last comment:\\\\n# ---8<---\\\\n# %s\\\\n# --->8---\\\\n\\\" % \\\"\\\\n# \\\".join(\\n c[-1].body.splitlines()\\n )\\n f.write(comment)\\n except UnicodeEncodeError:\\n log.debug(\\\"Can't encode character\\\")\",\n \"def comment(self, comment):\\r\\n\\r\\n core.FW_conf['connection'].comment(comment)\",\n \"def test_creates_comment(self):\\n comment_id = CommentAndSummary.objects.get(note=self.note).pk\\n report = Report.objects.filter(internal_comments__pk=comment_id)[0]\\n self.assertEqual(report.pk, self.pk)\",\n \"def post_comment(self):\\n self.post_question()\\n return self.client.post(\\\"api/v2/1/comments\\\", headers={\\\"Authorization\\\": \\\"{}\\\".format(self.token())}, data=json.dumps(self.comment), content_type='application/json')\",\n \"def action_create_task(self):\\n self.ensure_one()\\n res = super(ProjectIssue, self).action_create_task()\\n task_id = res.get('res_id', False)\\n if task_id:\\n task_obj = self.env['project.task'].browse(task_id)\\n task_obj.description = self.description\\n return res\",\n \"def generate_body(issue):\\n markdown = \\\"### {}\\\\n\\\".format(issue.pop('title'))\\n for k, v in issue.iteritems():\\n markdown += \\\"- {}: {}\\\\n\\\".format(k, v)\\n return markdown\",\n \"async def create_reply(*, comment: models.Comment = Depends(resolve_comment), created_comment: CreateComment,\\n current_user: models.User = Depends(resolve_current_user), db: Session = Depends(get_db)):\\n return crud.create_comment(db, author_id=current_user.id, parent_resub_id=comment.parent_resub_id,\\n parent_post_id=comment.parent_post_id, parent_comment_id=comment.id,\\n content=created_comment.content)\",\n \"def generate_comment_body():\\n body = {\\n \\\"external_id\\\": 1,\\n \\\"external_slug\\\": factories.random_str(),\\n \\\"description\\\": \\\"External comment\\\",\\n \\\"context\\\": None,\\n }\\n\\n return body\",\n \"def test_projects_id_comments_post(self):\\n project = Comment()\\n response = self.client.open('/project-tracker/projects/{id}/comments'.format(id=56),\\n method='POST',\\n data=json.dumps(project),\\n content_type='application/json')\\n self.assert200(response, \\\"Response body is : \\\" + response.data.decode('utf-8'))\",\n \"def test_issue_edit_comment_deprecated(self):\\n pass\",\n \"def test_comment_creation(self):\\n response = self.client.post(reverse('posts:comment_create'),\\n data={\\n 'post': self.post.id,\\n 'user': self.user.id,\\n 'comment_description': 'This is a '\\n 'test_comment'\\n }, format='json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n self.assertEqual(response.data, {\\n 'post': self.post.id,\\n 'user': self.user.id,\\n 'comment_description': 'This is a '\\n 'test_comment'\\n })\",\n \"def issue(msg: telebot.types.Message) -> None:\\n data = msg.text.split()\\n if len(data) == 1:\\n bot.send_message(\\n msg.from_user.id,\\n 'Use this command to tell the developer about an issue. '\\n 'Example usage: `/issue I got 4 in a row but game did not end.`',\\n parse_mode='Markdown'\\n )\\n else:\\n m = ' '.join(data[1:])\\n for dev_id in config.DEV_ID:\\n bot.send_message(\\n dev_id,\\n '<b>Issue</b> from <a href=\\\"tg://user?id={id}\\\">{first_name}</a>.\\\\n'.format(\\n first_name=msg.from_user.first_name,\\n id=msg.from_user.id\\n ) +\\n 'ID: {id}\\\\n'.format(id=msg.from_user.id) +\\n '<i>{message}</i>\\\\n'.format(message=m),\\n parse_mode='HTML'\\n )\\n bot.reply_to(\\n msg,\\n 'Developer was notified. Thank you for your time.'\\n )\",\n \"def _create_system_comment(\\n action_type, user, post, changelog=None, attachments=None, stage=None\\n) -> Comment:\\n if not config.SYSTEM_USER_ID:\\n return None\\n\\n if not attachments:\\n attachments = ()\\n if not stage:\\n stage = post.stage\\n\\n system_user = User.objects.get(id=config.SYSTEM_USER_ID)\\n comment = Comment()\\n comment.commentable = post\\n comment.type = Comment.TYPE_SYSTEM\\n comment.user = system_user\\n\\n # Depending on action type, fill different fields\\n meta = {\\n \\\"action\\\": action_type,\\n \\\"user\\\": {\\\"id\\\": user.id, \\\"str\\\": user.__str__()},\\n \\\"files\\\": [],\\n }\\n\\n if action_type == Comment.SYSTEM_ACTION_CHANGE_META:\\n meta[\\\"changelog\\\"] = changelog\\n\\n elif action_type == Comment.SYSTEM_ACTION_UPDATE:\\n if len(attachments) > 0:\\n meta[\\\"files\\\"] = [\\n {\\\"id\\\": a.id, \\\"str\\\": a.original_filename} for a in attachments\\n ]\\n\\n elif action_type == Comment.SYSTEM_ACTION_SET_STAGE:\\n meta[\\\"stage\\\"] = {\\\"id\\\": post.stage.id, \\\"str\\\": post.stage.__str__()}\\n\\n # Assign builded meta to comment and save\\n comment.meta[\\\"comment\\\"] = meta\\n comment.save()\\n\\n return comment\",\n \"def format_comment(filename, line_n, offset, code, text):\\n return f'{code}: {text}'\",\n \"def _create_issue(self, dep_name, dep_latest_version, is_subtask=False, parent_key=None):\\n logging.info(\\\"Creating a new JIRA issue to track {0} upgrade process\\\".format(dep_name))\\n assignee, owners = self._find_owners(dep_name)\\n summary = _ISSUE_SUMMARY_PREFIX + dep_name\\n if dep_latest_version:\\n summary = summary + \\\" \\\" + dep_latest_version\\n description = \\\"\\\"\\\"\\\\n\\\\n{0}\\\\n\\n Please review and upgrade the {1} to the latest version {2} \\\\n \\n cc: \\\"\\\"\\\".format(\\n datetime.today(),\\n dep_name,\\n dep_latest_version\\n )\\n for owner in owners:\\n description += \\\"[~{0}], \\\".format(owner)\\n try:\\n if not is_subtask:\\n issue = self.jira.create_issue(summary, [_JIRA_COMPONENT], description, assignee=assignee)\\n else:\\n issue = self.jira.create_issue(summary, [_JIRA_COMPONENT], description, assignee=assignee, parent_key=parent_key)\\n except Exception as e:\\n logging.error(\\\"Failed creating issue: \\\"+ str(e))\\n raise e\\n return issue\",\n \"def add_comment(request):\\n if request.method != 'POST':\\n return HttpResponseRedirect(reverse('wainz.views.composite'))\\n else:\\n img_id = request.POST['id']\\n try:\\n img = Image.objects.get(pk=img_id)\\n except:\\n return HttpResponseRedirect(reverse('wainz.views.composite'))\\n comment_text = request.POST['comment']\\n #TODO sanitize input\\n comment = ImageComment()\\n comment.submission_date = timezone.now()\\n comment.comment_text= comment_text\\n comment.image_id = img_id\\n comment.submitter_id = int(request.POST['uid'])\\n comment.save()\\n return rest.rest_success(request, img_id)\",\n \"def _create_issue(*, image: str, repo: str, run: str, stacktrace: str) -> Issue:\\n title = f\\\"Automatic error report from {repo}\\\"\\n body = _report_body(image=image, repo=repo, run=run, stacktrace=stacktrace)\\n return TAGBOT_ISSUES_REPO.create_issue(title, body)\",\n \"def comment(request, object_id):\\n send_charob = request.user.char_ob\\n rec_charob = get_character_from_ob(object_id)\\n comment_txt = request.POST[\\\"comment\\\"]\\n roster.create_comment(send_charob, rec_charob, comment_txt)\\n return HttpResponseRedirect(reverse(\\\"character:sheet\\\", args=(object_id,)))\",\n \"def create_comment(project_id, session_id, annotation_id, comment_id=None):\\n helpers.abort_if_invalid_parameters(project_id, session_id)\\n user = helpers.abort_if_unauthorized(Project.query.get(project_id))\\n if comment_id:\\n helpers.abort_if_unknown_comment(comment_id, annotation_id)\\n\\n data = helpers.jsonify_request_or_abort()\\n\\n schema = UserAnnotationCommentSchema()\\n helpers.abort_if_errors_in_validation(schema.validate(data))\\n # Note: comment_id can be null, which represents that it is a parent\\n comment = CommentsModel(data['content'], comment_id, user.id, annotation_id)\\n db.session.add(comment)\\n db.session.commit()\\n\\n # Determine which type of comment the response is to: nested or a root comment\\n if comment_id:\\n _comment = CommentsModel.query.filter_by(parent_id=comment_id).first()\\n else:\\n _comment = RootComment.query.get(annotation_id)\\n parent_user_id = _comment.user_id\\n usr = User.query.get(parent_user_id)\\n\\n if user.id != usr.id:\\n InterviewSession.email_commentor(usr, project_id, session_id)\\n\\n fcm.notify_participants_user_commented(project_id, session_id)\\n return custom_response(200, data=schema.dump(comment))\",\n \"def create_comment(request):\\n\\n # get data\\n in_data = getRequestData(request)\\n\\n # get the Thread associated with the comments\\n mythread = Thread.objects.get(id=in_data.get('mythreadid'))\\n\\n # save in database\\n try:\\n comment = Comment(pub_date = datetime.datetime.now(pytz.timezone('US/Eastern')), username = in_data.get('myusername'), text = in_data.get('mytext'), score = 0, thread = mythread )\\n comment.save()\\n except:\\n return HttpResponseBadRequest('Error saving to database!')\\n\\n return JsonResponse(in_data)\",\n \"def comment(self, text):\\n url = \\\"https://api.imgur.com/3/comment\\\"\\n payload = {'image_id': self.id, 'comment': text}\\n resp = self._imgur._send_request(url, params=payload, needs_auth=True,\\n method='POST')\\n return Comment(resp, imgur=self._imgur, has_fetched=False)\",\n \"def _format_comment(self):\\n return '\\\"\\\"\\\"' + self.comment + '\\\"\\\"\\\"'\",\n \"def _get_comment(self, cell: NotebookNode, resources: ResourcesDict) -> None:\\n\\n # retrieve or create the comment object from the database\\n comment = self.gradebook.find_comment(\\n cell.metadata['nbgrader']['grade_id'],\\n self.notebook_id,\\n self.assignment_id,\\n self.student_id)\\n\\n # save it in the notebook\\n cell.metadata.nbgrader['comment'] = comment.comment\",\n \"def generate_description(jira_key: str, comment: str) -> str:\\n if jira_key in comment:\\n return comment\\n else:\\n return '{}: {}'.format(jira_key, comment)\",\n \"def create_issue(self, group, form_data, **kwargs):\\n headers = { \\\"X-Redmine-API-Key\\\": self.get_option('key', group.project),\\n 'content-type': 'application/json' }\\n verifySSL = self.get_option('verify_ssl', group.project)\\n url = urlparse.urljoin(self.get_option('host', group.project), \\\"issues.json\\\")\\n payload = {\\n 'project_id': self.get_option('project_id', group.project),\\n 'tracker_id': self.get_option('tracker_id', group.project),\\n 'status_id': '0',\\n 'subject': form_data['title'].encode('utf-8'),\\n 'description': form_data['description'].encode('utf-8'),\\n }\\n #print >> sys.stderr, \\\"url:\\\", url\\n #print >> sys.stderr, \\\"payload:\\\\n\\\", pformat(payload)\\n #print >> sys.stderr, pformat(group)\\n #print >> sys.stderr, pformat(dir(group))\\n\\n try:\\n r = requests.post(url, data=json.dumps({'issue': payload}), headers=headers, verify=verifySSL)\\n except requests.exceptions.HTTPError as e:\\n raise forms.ValidationError('Unable to reach Redmine host: %s' % repr(e))\\n\\n try:\\n data = json.loads(r.text)\\n except json.JSONDecodeError as e:\\n #print >> sys.stderr, \\\"ERROR: %s\\\" % e\\n #print >> sys.stderr, \\\"RESP:\\\", r.text\\n raise forms.ValidationError('Unable to reach Redmine host: %s' % repr(e))\\n\\n if not 'issue' in data or not 'id' in data['issue']:\\n raise forms.ValidationError('Unable to create redmine ticket')\\n\\n return data['issue']['id']\",\n \"def comment(self,obj_ID=None,comment='empty',**kwargs):\\n\\n if obj_ID==None:\\n print('No object UID given, or invalid UID given',file=sys.stderr)\\n return {'error':-1}\\n\\n if not (isinstance(comment,str) or isinstance(comment,unicode)):\\n print('Error in mpo_commment, should be a plain string')\\n return {'error':-1}\\n\\n r=self.post(self.COMMENT_RT,None,obj_ID,data={'content':str(comment)})\\n return r\",\n \"def proto_comment(self, proto_user, proto_post):\\n return baker.make(Comment, user=proto_user, post=proto_post)\",\n \"def register_comment(self, overrides=None):\\n cs_comment = make_minimal_cs_comment({\\n \\\"id\\\": \\\"test_comment\\\",\\n \\\"course_id\\\": str(self.course.id),\\n \\\"thread_id\\\": \\\"test_thread\\\",\\n \\\"username\\\": self.user.username,\\n \\\"user_id\\\": str(self.user.id),\\n \\\"body\\\": \\\"Original body\\\",\\n })\\n cs_comment.update(overrides or {})\\n self.register_get_comment_response(cs_comment)\\n self.register_put_comment_response(cs_comment)\\n self.register_post_comment_response(cs_comment, thread_id=\\\"test_thread\\\")\",\n \"def make_comment_data(self, comment_id, parent_id=None, children=[]): # pylint: disable=W0102\\n return make_minimal_cs_comment({\\n \\\"id\\\": comment_id,\\n \\\"parent_id\\\": parent_id,\\n \\\"course_id\\\": str(self.course.id),\\n \\\"thread_id\\\": self.thread_id,\\n \\\"thread_type\\\": \\\"discussion\\\",\\n \\\"username\\\": self.user.username,\\n \\\"user_id\\\": str(self.user.id),\\n \\\"created_at\\\": \\\"2015-06-03T00:00:00Z\\\",\\n \\\"updated_at\\\": \\\"2015-06-03T00:00:00Z\\\",\\n \\\"body\\\": \\\"Original body\\\",\\n \\\"children\\\": children,\\n })\",\n \"def should_add_pr_comment(self):\\n pass\",\n \"def create_new_issue(\\n self,\\n token: str,\\n object_id: str,\\n customer_id: str,\\n project_id: str,\\n scope_id: str,\\n issue_name: str,\\n region: str,\\n business_unit: str,\\n date_of_raise: str,\\n due_date: str,\\n nature_of_issue: str,\\n criticality: str,\\n issue_description: str,\\n impact_value: str,\\n currency: str,\\n impact_on: str,\\n document_ref: dict,\\n issue_owner: dict,\\n resolution_path: str,\\n ):\\n\\n # Type guarding\\n assert check_argument_types()\\n\\n # TODO: make table name environment variable\\n table_name = f\\\"Projects-{customer_id}\\\"\\n\\n # Key\\n key = {\\\"projectId\\\": project_id, \\\"customerId\\\": customer_id}\\n\\n # Projection Expression\\n projection_expression = \\\", \\\".join([\\\"projectId\\\", \\\"code\\\"])\\n\\n # Check if customer and project exist\\n logger.info(f\\\"Checking if project ID or organization ID exists: {key}\\\")\\n response, _ = self._db.read_single_item(table_name, key, projection_expression)\\n\\n # Get project code\\n project_code = response[\\\"code\\\"]\\n\\n # Request body\\n dynamo_object = {\\n \\\"scopeId\\\": scope_id,\\n \\\"issueName\\\": issue_name,\\n \\\"region\\\": region,\\n \\\"businessUnit\\\": business_unit,\\n \\\"dateOfRaise\\\": date_of_raise,\\n \\\"dueDate\\\": due_date,\\n \\\"natureOfIssue\\\": nature_of_issue,\\n \\\"criticality\\\": criticality,\\n \\\"issueDescription\\\": issue_description,\\n \\\"status\\\": \\\"open\\\",\\n \\\"impactValue\\\": impact_value,\\n \\\"currency\\\": currency,\\n \\\"impactOn\\\": impact_on,\\n \\\"documentRef\\\": document_ref,\\n \\\"issueOwner\\\": issue_owner,\\n \\\"resolutionPath\\\": resolution_path,\\n \\\"lastUpdated\\\": str(date.today()),\\n \\\"issueId\\\": object_id,\\n }\\n\\n # Send project onboarding email\\n logger.info(\\\"Sending project onboarding email\\\")\\n self._email.send_template_email(\\n source=getenv(\\\"SOURCE_EMAIL_ADDRESS\\\"),\\n template_name=getenv(\\\"ISSUE_ASSIGNMENT_TEMPLATE\\\"),\\n template_data=json.dumps(\\n {\\n \\\"issueId\\\": f'\\\"{dynamo_object[\\\"issueName\\\"]}\\\"',\\n \\\"projectCode\\\": project_code,\\n }\\n ),\\n bcc_addresses=[issue_owner[\\\"email\\\"]],\\n )\\n\\n # Dynamo update expressions & update\\n logger.info(\\\"Create new project issue\\\")\\n update_expression = (\\n f\\\"SET scopes.#scopeId.issues.#IssueId = :{dynamo_object['issueId']}\\\"\\n )\\n expression_attribute_names = {\\n \\\"#scopeId\\\": scope_id,\\n \\\"#IssueId\\\": dynamo_object[\\\"issueId\\\"]\\n }\\n expression_attribute_values = {f\\\":{dynamo_object['issueId']}\\\": dynamo_object}\\n self._db.update_item(\\n table_name,\\n key,\\n update_expression,\\n expression_attribute_names,\\n expression_attribute_values,\\n )\\n\\n # Log workflow\\n message = [f\\\"Created new issue: {issue_name}\\\"]\\n workflow = Workflows.update_workflows(\\n token, \\\"Create\\\", message, project_id, dynamo_object[\\\"issueId\\\"]\\n )\\n self._db.create_item(f\\\"Workflows-{customer_id}\\\", workflow)\\n\\n logger.info(\\\"New issue created successfully\\\")\\n return \\\"New issue created successfully\\\", 200\",\n \"def comment(self, body, incident_id):\\n payload = {\\\"comment\\\":{\\\"body\\\":body, \\\"is_private\\\":\\\"false\\\"}}\\n response = self.session.post(\\n \\\"{0}/incidents/{1}/comments.json\\\".format(self.uri, incident_id),\\n json=payload\\n )\\n return response.status_code\",\n \"def add_comment(self, message):\\n params = {\\\"ffauth_device_id\\\": self._DiscretelyAuthenticatedObject__device_id,\\n \\\"ffauth_secret\\\": self._DiscretelyAuthenticatedObject__device_token}\\n data = {\\\"data\\\": str(\\n {\\\"event\\\": {\\\"type\\\": \\\"comment\\\", \\\"message\\\": message, \\\"assessment_details_id\\\": self.__assessmentDetailsId},\\n \\\"recipient\\\": {\\\"guid\\\": self._DiscretelyAuthenticatedObject__guid, \\\"type\\\": \\\"user\\\"}})}\\n requests.post(\\n self._DiscretelyAuthenticatedObject__portal + \\\"/_api/1.0/tasks/\\\" + str(self.id) + \\\"/responses\\\",\\n params=params, data=data)\",\n \"def post_comment_on_line(owner, repo, commit_sha, line_n, filename, message):\\n res = post(\\n GIT_COMMENT_URL.format(host=host_api, owner=owner, repo=repo,\\n commit_sha=commit_sha),\\n auth=auth, json={\\n 'body': message,\\n 'path': filename,\\n 'position': line_n,\\n })\\n\\n assert res.status_code == 201, f'Got non 201 status, ' \\\\\\n f'error message: {res.content}'\",\n \"def create_issue(\\n self,\\n summary: str,\\n body: str,\\n labels: Optional[Iterable[str]] = None,\\n links: Iterable[str] = (),\\n ) -> Issue:\\n issue = self.jira.create_issue(\\n project=self.project,\\n summary=summary,\\n description=body,\\n labels=labels,\\n issuetype={\\\"name\\\": \\\"Task\\\"},\\n )\\n for ln in links:\\n self.jira.create_issue_link(\\n type=\\\"is caused by\\\", inwardIssue=issue.key, outwardIssue=ln\\n )\\n return issue\",\n \"def add_comment(cls, post_id, user_id, content):\\n c = cls(parent=comment_key(),\\n post_id=post_id,\\n user_id=user_id,\\n content=content)\\n c.put()\",\n \"def on_commit_comment(self, payload):\\n pass\",\n \"def get_comment(self, uid: str) -> _model.AbstractComment:\\n pass\",\n \"def submit_feedback(self, title, description, state):\\n\\n body = f\\\"\\\"\\\"\\n**User Issue**\\nEmail: {self.user.email}\\nUser Agent: {get_user_agent(self.request)}\\n\\n{description}\\n\\n<details>\\n\\n<summary>Redux state</summary>\\n\\n<p>\\n\\n```json\\n{json.dumps(state, indent=2)}\\n```\\n\\n</p>\\n</details>\\n \\\"\\\"\\\"\\n\\n r = requests.post(\\n 'https://api.github.com/repos/alexmojaki/futurecoder/issues',\\n json={'title': title,\\n 'body': body,\\n 'labels': ['user', 'bug']},\\n headers=dict(\\n Authorization='token ' + settings.GITHUB_TOKEN,\\n ),\\n )\\n\\n assert r.status_code == 201\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8301317","0.7754753","0.72062963","0.7073606","0.69860274","0.6936139","0.6936139","0.6907152","0.6887853","0.6854214","0.6846589","0.68176407","0.67910486","0.67743784","0.67724115","0.6749794","0.6749794","0.67153496","0.66674685","0.664144","0.66202986","0.65565896","0.6542951","0.6533347","0.65274996","0.651514","0.6507702","0.6495731","0.6491929","0.64820063","0.64160025","0.63882506","0.63783646","0.63374245","0.6315437","0.62959766","0.62919974","0.6285433","0.627933","0.6247946","0.6240561","0.6233126","0.6210444","0.6204745","0.6204745","0.6174229","0.615582","0.61430955","0.61251813","0.6111985","0.6111096","0.6110954","0.6105708","0.6099271","0.60823894","0.6076181","0.60612255","0.60568666","0.6041611","0.60260713","0.6007076","0.60049105","0.6001473","0.5994861","0.5990373","0.5988633","0.5980066","0.5966742","0.5935508","0.59144694","0.5910102","0.5905811","0.589715","0.58949304","0.5878694","0.58779794","0.58765465","0.5830578","0.58258206","0.5812114","0.58093417","0.5806578","0.5802438","0.58022875","0.5801095","0.57963014","0.5781057","0.57754725","0.5770827","0.5770382","0.57669646","0.5749342","0.57436854","0.5737273","0.5735153","0.5734676","0.57285875","0.57280904","0.5727621","0.5724327"],"string":"[\n \"0.8301317\",\n \"0.7754753\",\n \"0.72062963\",\n \"0.7073606\",\n \"0.69860274\",\n \"0.6936139\",\n \"0.6936139\",\n \"0.6907152\",\n \"0.6887853\",\n \"0.6854214\",\n \"0.6846589\",\n \"0.68176407\",\n \"0.67910486\",\n \"0.67743784\",\n \"0.67724115\",\n \"0.6749794\",\n \"0.6749794\",\n \"0.67153496\",\n \"0.66674685\",\n \"0.664144\",\n \"0.66202986\",\n \"0.65565896\",\n \"0.6542951\",\n \"0.6533347\",\n \"0.65274996\",\n \"0.651514\",\n \"0.6507702\",\n \"0.6495731\",\n \"0.6491929\",\n \"0.64820063\",\n \"0.64160025\",\n \"0.63882506\",\n \"0.63783646\",\n \"0.63374245\",\n \"0.6315437\",\n \"0.62959766\",\n \"0.62919974\",\n \"0.6285433\",\n \"0.627933\",\n \"0.6247946\",\n \"0.6240561\",\n \"0.6233126\",\n \"0.6210444\",\n \"0.6204745\",\n \"0.6204745\",\n \"0.6174229\",\n \"0.615582\",\n \"0.61430955\",\n \"0.61251813\",\n \"0.6111985\",\n \"0.6111096\",\n \"0.6110954\",\n \"0.6105708\",\n \"0.6099271\",\n \"0.60823894\",\n \"0.6076181\",\n \"0.60612255\",\n \"0.60568666\",\n \"0.6041611\",\n \"0.60260713\",\n \"0.6007076\",\n \"0.60049105\",\n \"0.6001473\",\n \"0.5994861\",\n \"0.5990373\",\n \"0.5988633\",\n \"0.5980066\",\n \"0.5966742\",\n \"0.5935508\",\n \"0.59144694\",\n \"0.5910102\",\n \"0.5905811\",\n \"0.589715\",\n \"0.58949304\",\n \"0.5878694\",\n \"0.58779794\",\n \"0.58765465\",\n \"0.5830578\",\n \"0.58258206\",\n \"0.5812114\",\n \"0.58093417\",\n \"0.5806578\",\n \"0.5802438\",\n \"0.58022875\",\n \"0.5801095\",\n \"0.57963014\",\n \"0.5781057\",\n \"0.57754725\",\n \"0.5770827\",\n \"0.5770382\",\n \"0.57669646\",\n \"0.5749342\",\n \"0.57436854\",\n \"0.5737273\",\n \"0.5735153\",\n \"0.5734676\",\n \"0.57285875\",\n \"0.57280904\",\n \"0.5727621\",\n \"0.5724327\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71473557"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":74591,"cells":{"query":{"kind":"string","value":"Show issue url by its id."},"document":{"kind":"string","value":"def show_issue(self, msg, issue_id):\n self._asset_bind(msg)\n yield \"https://github.com/{}/issues/{}\".format(task_repository_name(), issue_id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def issue(ctx, number: Option(int, \"Issue number\")):\n url = f\"{repo}/issues/{number}\"\n view = discord.ui.View()\n view.add_item(discord.ui.Button(label=\"View Issue\", url=url))\n await ctx.respond(f\"Here's a link\", view=view)","def build_issue_tracker_url(issue_id):\n issue_tracker_tmpl = settings.ISSUE_TRACKER_BUG_URL_TMPL\n url_tmpl = issue_tracker_tmpl if issue_tracker_tmpl else 'http://issue/%s'\n return url_tmpl % issue_id","def get_issue_url(self, key):\n raise NotImplementedError","def get_issue(self, issue_id, **kwargs):\n raise NotImplementedError","def get_issue(self, issue_id, **kwargs):\n raise NotImplementedError","def openNewIssueUrl(self):\r\n url = QUrl(\"https://github.com/Freeseer/freeseer/issues/new\")\r\n QDesktopServices.openUrl(url)","def get_issue_url(testcase):\n issue_tracker = get_issue_tracker_for_testcase(testcase)\n if not issue_tracker:\n return None\n\n issue_id = (\n testcase.bug_information\n if testcase.bug_information else testcase.group_bug_information)\n if not issue_id:\n return None\n\n # Use str(issue_id) as |group_bug_information| might be an integer.\n return issue_tracker.issue_url(str(issue_id))","async def pr(ctx, number: Option(int, \"Pull request number\")):\n url = f\"{repo}/issues/{number}\"\n view = discord.ui.View()\n view.add_item(discord.ui.Button(label=\"View Pull Request\", url=url))\n await ctx.respond(f\"Here's a link\", view=view)","def TrackerURL(issue):\n # make the server/project customizable?\n return 'http://code.google.com/p/lilypond/issues/detail?id=%s' % issue","def find_url(ticketID):\n uri = 'https://jira.duraspace.org/browse/' + ticketID\n print uri\n return uri","def issue(self, id):\r\n return issues.RepoIssue(self, id)","def show(self, solution_id):\n token = self._get_token()\n __kargs = self.kargs.copy()\n __kargs[\"url_path\"] += \"/%s\"%(str(solution_id))\n return process_result(Request(__kargs,token).get())","def get_issue(self, context):","def issueView(context, issue):\n\n args = {\n 'item': None,\n 'group': None\n }\n \n if issue.item != None:\n item = issue.item.item\n args['item'] = item\n args['status'] = item.status.all()\n if issue.group != None:\n args['group'] = issue.group.group\n\n return render_to_string('issueView.html', args, context)","def get_issue(self, issue_id):\n try:\n json = self.get('repos/%(owner)s/%(repo)s/issues/%(issue_id)d' % {\n 'owner': self.repo_owner,\n 'repo': self.repo_name,\n 'issue_id': issue_id,\n })\n\n label_list = [label_dict['name'] for label_dict in json['labels']]\n\n return Issue(json['number'], label_list)\n except ResourceNotFound:\n return None","def find_issue_id(self):","def test_issue_detail(self):\n response = self.client.get(url_for(\n 'issues.issuedetailresource',\n issue_number=self.TARGET_ISSUE_NUMBER))\n self.assertEqual(response.status_code, 200)\n self.assertTrue(response.json)","def get_detail_url(order_id):\n return reverse(\"order-detail\", args=[str(order_id)])","def show(ID):\n review = parse(gh_request('GET', '/repos/:user/:repo/pulls/:id', uri_vars={'id': ID}))\n printers.print_review(review)","def report(issues, show_urls=False):\r\n # titles may have unicode in them, so we must encode everything below\r\n if show_urls:\r\n for i in issues:\r\n role = 'ghpull' if 'merged' in i else 'ghissue'\r\n print('* :%s:`%d`: %s' % (role, i['number'],\r\n i['title'].encode('utf-8')))\r\n else:\r\n for i in issues:\r\n print('* %d: %s' % (i['number'], i['title'].encode('utf-8')))","def pr_link(repo, id):\n\n return '[#{id}](https://github.com/{repo}/pull/{id})'.format(id=id, repo=repo)","def link(self, id):\r\n return links.RepoLink(self, id)","def get_issue(self):\n issue_id = self.kwargs['issue_id']\n try:\n issue = Issue.objects.get(pk=issue_id)\n except ObjectDoesNotExist:\n raise ObjectNotFound('Not found')\n if issue.project.pk != self.project.pk:\n raise ObjectNotFound('Not found')\n return issue","def get_redmine_issue_url():\n win32clipboard.OpenClipboard()\n url = win32clipboard.GetClipboardData()\n win32clipboard.CloseClipboard()\n # check the URL http:xxx.xxx.xxx.xx:8080/redmine/issues/1234\n path, issue_num = os.path.split(url)\n path, issue = os.path.split(path)\n path, redmine = os.path.split(path)\n # check the URL is redmine issues\n if validators.url(url) and issue_num.isdigit() and issue=='issues' and redmine=='redmine' :\n URL = url;\n print(f'URL for redmine issue (get from Clipboard)')\n print(f'{URL}')\n else:\n print(f'Clipboard contents :')\n if validators.url(url):\n print(f'\\t{url}')\n else:\n print(f'\\tnon-url data')\n print(f'\\n')\n print(f'Usage: Copy (e.g. Ctrl+C) redmine issue URL (e.g. http://192.168.0.1:8080/redmine/issues/1234 ) to clipboard and execute script')\n URL = \"\"\n\n return URL","def get_absolute_url(self):\n return reverse(\"jewelry_detail\", args = [str(self.id)])","def detail_url(reteta_id):\n return reverse('reteta:reteta-detail', args=[reteta_id])","def get_absolute_url(self):\n return reverse('injury-detail', args=[str(self.id)])","def problem(request, problemid):\n curr_problem = get_object_or_404(Problem, id=problemid)\n context = {'problem': curr_problem}\n return render(request, 'code_challenge/problem.html', context)","def get_url_from_id(doc_id):\n return f\"{BASE_URL}/view/{doc_id}\"","async def launchbyid(self, ctx, launchid, *args):\n launches = launchlibrary.Launch.fetch(api, id=launchid)\n if launches:\n launch = launches[0]\n if launch.agency != None:\n embedcolor = discord.Colour(await get_color(launch.agency.id))\n else:\n embedcolor = discord.Colour(5592405)\n embed = discord.Embed(title=launch.name, colour=embedcolor)\n embed.set_footer(text=\"ID: {0}\".format(launch.id))\n embed.set_thumbnail(url=launch.rocket.image_url)\n embed = time_description(embed, launch)\n for arg, formatter in (('-r', reasons), ('-v', videourls)):\n if arg in args:\n msg = formatter(embed, launch)\n await ctx.send(embed=embed)\n else:\n await ctx.send(\"No launch with that ID.\")","def detail(request, question_id):\n question = get_object_or_404(Question, pk=question_id)\n return render(request, 'problemfinder/details.html', {'question': question})","def issue(self, issue_id):\r\n return issues.RepoIssue(self, issue_id)","def get_absolute_url(self):\n\t\treturn reverse('source-detail', args=[str(self.id)])","def detail_url(movie_id):\n return reverse('movie:movie-detail', args=[movie_id])","def show(self, item_id):\n pass","def _get_issue(repo, objid):\n issue = None\n if not repo.settings.get(\"issue_tracker\", True):\n raise PagureEvException(\"No issue tracker found for this project\")\n\n session = _get_session()\n issue = pagure.lib.query.search_issues(session, repo, issueid=objid)\n\n if issue is None or issue.project != repo:\n raise PagureEvException(\"Issue '%s' not found\" % objid)\n\n if issue.private:\n # TODO: find a way to do auth\n raise PagureEvException(\n \"This issue is private and you are not allowed to view it\"\n )\n\n return issue","def get_absolute_url(self):\n\n return reverse('kid-detail', args=[str(self.id)])","def category_details_url(id):\n return reverse('category:category-detail', args=[id])","def _LoginOrIssueEntryURL(mr, config):\n issue_entry_url = servlet_helpers.ComputeIssueEntryURL(mr, config)\n if mr.auth.user_id:\n return issue_entry_url\n else:\n after_login_url = framework_helpers.FormatAbsoluteURL(\n mr, urls.ISSUE_ENTRY_AFTER_LOGIN)\n return _SafeCreateLoginURL(mr, after_login_url)","def status_url(self, username, id):\n return urllib.parse.urljoin(self.instance, f'/p/{urllib.parse.quote(username)}/{id}')","def get_absolute_url(self):\n return reverse('bleedinfo-detail', args=[str(self.id)])","def detail_url(product_id):\n return reverse('product:product-detail', args=[product_id])","def exam_url(self, obj):\n request = self.context.get(\"request\")\n return reverse(\"exam-detail\", args=[obj.id], request=request)","def detail_url(recipe_id):\n return reverse('recipe:recipe-detail',args=[recipe_id])","def show(request):\n patchsets = request.issue.get_patchset_info(request.user, None)\n last_patchset = first_patch = None\n if patchsets:\n last_patchset = patchsets[-1]\n if last_patchset.patches:\n first_patch = last_patchset.patches[0]\n messages = []\n has_draft_message = False\n for msg in request.issue.messages:\n if not msg.draft:\n messages.append(msg)\n elif msg.draft and request.user and msg.sender == request.user.email():\n has_draft_message = True\n num_patchsets = len(patchsets)\n return respond(request, 'issue.html', {\n 'first_patch': first_patch,\n 'has_draft_message': has_draft_message,\n 'is_editor': request.issue.edit_allowed,\n 'issue': request.issue,\n 'last_patchset': last_patchset,\n 'messages': messages,\n 'num_patchsets': num_patchsets,\n 'patchsets': patchsets,\n })","def detail_url(recipe_id):\n return reverse('recipe:recipe-detail', args=[recipe_id])","def detail_url(recipe_id):\n return reverse('recipe:recipe-detail', args=[recipe_id])","def detail_url(recipe_id):\n return reverse('recipe:recipe-detail', args=[recipe_id])","def handle_issue(self, evt):\n author = self.format_nickname(evt.author)\n if evt.new:\n short_url = \"https://dolp.in/i%d\" % evt.issue\n url = Tags.UnderlineBlue(short_url)\n msg = 'Issue %d created: \"%s\" by %s - %s'\n msg = msg % (evt.issue, evt.title, author, url)\n else:\n short_url = \"https://dolp.in/i%d/%d\" % (evt.issue, evt.update)\n url = Tags.UnderlineBlue(short_url)\n msg = 'Update %d to issue %d (\"%s\") by %s - %s'\n msg = msg % (evt.update, evt.issue, evt.title, author, url)\n self.bot.say(msg)","def show(request):\n if _use_new_ui(request):\n return _serve_new_ui(request)\n\n patchsets = request.issue.get_patchset_info(request.user, None)\n last_patchset = first_patch = None\n if patchsets:\n last_patchset = patchsets[-1]\n if last_patchset.patches:\n first_patch = last_patchset.patches[0]\n messages = []\n generated_messages = []\n has_draft_message = False\n # Keep track of the last non-generated message.\n message_index = -1\n last_user_message_index = -1\n for msg in request.issue.messages:\n if msg.auto_generated:\n generated_messages.append(msg)\n if not msg.draft:\n messages.append(msg)\n message_index += 1\n if not msg.auto_generated:\n last_user_message_index = message_index\n elif msg.draft and request.user and msg.sender == request.user.email():\n has_draft_message = True\n num_patchsets = len(patchsets)\n\n issue = request.issue\n issue.description = cgi.escape(issue.description)\n issue.description = urlize(issue.description)\n re_string = r\"(?<=BUG=)\"\n re_string += \"(\\s*(?:[a-z0-9-]+:)?\\d+\\s*(?:,\\s*(?:[a-z0-9-]+:)?\\d+\\s*)*)\"\n expression = re.compile(re_string, re.IGNORECASE)\n issue.description = re.sub(expression, _replace_bug, issue.description)\n src_url = _map_base_url(issue.base)\n\n display_generated_msgs = False\n if request.user:\n account = models.Account.current_user_account\n display_generated_msgs = account.display_generated_msgs\n\n landed_days_ago = issue.get_time_since_landed()\n landed_days_ago = landed_days_ago.days if landed_days_ago else 'unknown'\n\n return respond(request, 'issue.html', {\n 'first_patch': first_patch,\n 'has_draft_message': has_draft_message,\n 'is_editor': request.issue.edit_allowed,\n 'issue': request.issue,\n 'last_patchset': last_patchset,\n 'messages': messages,\n 'generated_messages': generated_messages,\n 'last_user_message_index': last_user_message_index,\n 'num_patchsets': num_patchsets,\n 'patchsets': patchsets,\n 'src_url': src_url,\n 'display_generated_msgs': display_generated_msgs,\n 'offer_cq': request.issue.is_cq_available,\n 'landed_days_ago': landed_days_ago,\n })","def get_detail_URL(recipe_id):\n return reverse('recipeapp:recipe-detail', args=[recipe_id])","def show_board(self, boardID):\n return self.app.get('/board/' + boardID, follow_redirects = True)","def issue_id(self) -> str:\n return self._yaml[\"commit\"][\"issue_id\"]","def instance_url(self) -> str:\n easypost_id = self.get(\"id\")\n if not easypost_id:\n raise Error(\"%s instance has invalid ID: %r\" % (type(self).__name__, easypost_id))\n return \"%s/%s\" % (self.class_url(), easypost_id)","def get_absolute_url(self):\n return reverse('model-detail-view', args=[str(self.id)])","def get_absolute_url(self):\n return reverse('report', args=[str(self.id)])","def get_absolute_url(self):\n return reverse('blogger-detail', args=[str(self.id)])","def get_absolute_url(self):\n return reverse('patient-detail', args=[str(self.id)])","def detail_url(recipe_id): # /api/recipe/recipes/ID\n return reverse('recipe:recipe-detail', args = [recipe_id])","def task_url(self, obj):\n request = self.context.get(\"request\")\n return reverse(\"task-detail\", args=[obj.id], request=request)","def command_show(problem):\r\n print problem.get_html()","def get_observation_url(self, observation_id):\n pass","def get_absolute_url(self):\n return reverse('bl-detail', args=[str(self.id)])","def project_detail(request, project_id):\n try:\n project = Project.objects.get(pk=project_id)\n project.description = markdown.markdown(bleach.clean(project.description, strip=True), extensions=['markdown.extensions.fenced_code'])\n p2 = Project.objects.get(pk=project_id)\n user_profile = UserProfile.objects.get(email=request.session['email'])\n submissions_list = Submission.objects.filter(project=project)\n except Project.DoesNotExist:\n raise Http404(\"Project does not exist\")\n context = {'project': project, 'submissions_list':submissions_list, 'current_user': request.session['email'], 'user_profile': user_profile}\n return render(request, 'projects/details.html', context)","def details_url(self):\n if self._data.get('details_url'):\n path = self._data.get('details_url')\n try:\n path, hash_ = path.split('#')\n hash_ = '#' + hash_\n except ValueError:\n hash_ = ''\n return '{}?from_activity={}{}'.format(path, self._data.get('id'), hash_)","def render_url(self, object_id):\r\n return reverse(\"%s:insert_%s_%s_render\" % (\r\n self.admin_site.name,\r\n self.model._meta.app_label,\r\n self.model._meta.module_name\r\n ), args=(object_id,))","def detail_url(pizza_id):\n return reverse('pizza:pizza-detail', args=[pizza_id])","def test_issue_get_issue(self):\n pass","def view_entry(id):\n db = get_db()\n cur = db.execute('select id, title, ingredients, steps, tags, url \\\n from entries where id = ? order by id desc',\n [id.strip()])\n entries = cur.fetchall()\n return render_template('view_entry.html', entries=entries)","def view_experiment(request,id):\n\texp = Experiment.objects.get(id=id)\n\tpossibly_related = get_related(exp)\n\treturn list_detail.object_detail(request,\n\t\t\t\t\t\t\t\t\tqueryset=Experiment.objects.filter(id=id),\n\t\t\t\t\t\t\t\t\tobject_id=exp.id,\n\t\t\t\t\t\t\t\t\ttemplate_name='experiments/experiment.html',\n\t\t\t\t\t\t\t\t\textra_context= {\"possibly_related\" : possibly_related})","def detail_url(ingredient_id):\n return reverse(\"recipe:ingredient-detail\", args=[ingredient_id])","def showtask(id):\n\n tasks = Task.query.filter_by(id=id)\n return render_template('home/taskshowall/dashboard_showtask.html',tasks=tasks,title=\"tasks\")","def show(self, req, id):\n context = req.environ['manila.context']\n\n try:\n message = self.message_api.get(context, id)\n except exception.MessageNotFound as error:\n raise exc.HTTPNotFound(explanation=error.msg)\n\n return self._view_builder.detail(req, message)","def get_absolute_url(self):\n return reverse('food-detail', args=[str(self.id)])","def detail_url(ingredient_id):\n return reverse('recipe:ingredient-detail', args=[ingredient_id])","def detail_url(author_id):\n return reverse('author-books', args=[author_id])","def get_absolute_url(self):\n return reverse('model-detail-view', args=[str(self.id)])","def get_absolute_url(self):\n return reverse('model-detail-view', args=[str(self.id)])","def explore_href(self, job_id):\n ## class QueuePage()\n jdict = mysql.job_get_dict(job_id)\n\n if self.admin:\n page = \"admin\"\n else:\n page = \"explore\"\n\n if self.admin:\n if jdict[\"tls_model\"] == \"ANISO\":\n l = ['<a href=\"webtlsmd.cgi?page=%s&job_id=%s\">%s</a> (ANISO)' % (\n page, job_id, job_id)]\n else:\n l = ['<a href=\"webtlsmd.cgi?page=%s&job_id=%s\">%s</a>' % (\n page, job_id, job_id)]\n\n if jdict[\"user_name\"] != \"\":\n l.append('<br>%s' % (jdict[\"user_name\"]))\n\n if jdict[\"user_comment\"] != \"\" or jdict[\"user_comment\"] != None:\n l.append('<br>%s' % (jdict[\"user_comment\"]))\n\n if jdict[\"email\"] != \"\":\n l.append('<br>%s' % (jdict[\"email\"]))\n\n return \"\".join(l)\n\n if mysql.job_get_private_job(job_id):\n ## Return job number only (non-clickable)\n job_number = re.match(r'[^_]*', job_id)\n if job_number:\n return job_number.group(0)\n return 'private'\n\n return '<a href=\"webtlsmd.cgi?page=%s&job_id=%s\">%s</a>' % (\n page, job_id, job_id)","def get_url(self, item):\n config = {}\n uuid = self.data.get('uuid', None)\n obj = uuidToObject(uuid)\n if uuid and obj:\n config = copy.copy(self.get_config(obj))\n\n url = u'{0}{1}'.format(self.view_url(obj), item.id.value)\n if config.get('modify_url', True):\n url = u'{0}___{1}-{2}'.format(\n url,\n item.title.value,\n item.location.value,\n )\n return url","def issues_by_id(self, repository_id, access_token=None):\n return self._complete_request_by_id(\n repository_id, \"issues\", access_token)","async def get_issue(self, issue: int) -> \"AIOGitHubAPIRepositoryIssue\":\n _endpoint = f\"/repos/{self.full_name}/issues/{issue}\"\n\n response = await self.client.get(endpoint=_endpoint)\n return AIOGitHubAPIRepositoryIssue(self.client, response)","def get_absolute_url(self):\n return reverse('card-detail', args=[str(self.id)])","def issue_path(project: str, location: str, issue_model: str, issue: str,) -> str:\n return \"projects/{project}/locations/{location}/issueModels/{issue_model}/issues/{issue}\".format(\n project=project, location=location, issue_model=issue_model, issue=issue,\n )","def getIdLink(self):\n return self.urlLink()","def issue_details(board, issue_number):\n issue = BoardIssue(board, issue_number)\n return issue.details()","def recipe_detail_url(recipe_id):\n return reverse('recipes:recipe-detail', args=[recipe_id])","def get_problem(id):\n return query(WEB_EXAMPLE_BASE + f\"/classical/problem/{id}\")","def self_link_with_id(self) -> str:\n return pulumi.get(self, \"self_link_with_id\")","def self_link_with_id(self) -> str:\n return pulumi.get(self, \"self_link_with_id\")","def display_url(self) -> Optional[str]:\n return pulumi.get(self, \"display_url\")","def show_orion_url(self, obj):\n return obj.orion_url","def view(self, _type, task_id):\n try:\n return self._super.do(**{\n \"method\": \"GET\",\n \"url\": \"{}/tasks/report/{}\".format(self.baseurl, task_id)\n })\n except AnalyserUpstreamError as e:\n if \"404\" in str(e.code):\n return None","def show_story(story_id, arguments):\r\n\r\n story = load_story(story_id, arguments)\r\n\r\n\r\n print\r\n print colored('{:12s}{:4s}{:9s}{:10s} {}'.format('#{}'.format(story.story_id),\r\n initials(story.owned_by),\r\n story.story_type,\r\n estimate_visual(story.estimate),\r\n story.name), 'white', attrs=['bold'])\r\n print\r\n print colored(\"Story Url: \", 'white', attrs=['bold']) + colored(story.url, 'blue', attrs=['underline'])\r\n print colored(\"Description: \", 'white', attrs=['bold']) + story.description\r\n\r\n if len(story.notes) > 0:\r\n print\r\n print bold(\"Notes:\")\r\n for note in story.notes:\r\n print \"[{}] {}\".format(initials(note.author), note.text)\r\n\r\n\r\n if len(story.tasks) > 0:\r\n print\r\n print bold(\"Tasks:\")\r\n for task in story.tasks:\r\n print \"[{}] {}\".format(x_or_space(task.complete), task.description)\r\n\r\n if len(story.attachments) > 0:\r\n print\r\n print bold(\"Attachments:\")\r\n for attachment in story.attachments:\r\n print \"{} {}\".format(attachment.description, colored(attachment.url,'blue',attrs=['underline']))\r\n\r\n print","def get_object_view_url(self, nuxeo_id):\n parts = urlparse.urlsplit(self.nx.conf[\"api\"])\n url = \"{}://{}/Nuxeo/nxdoc/default/{}/view_documents\".format(parts.scheme, parts.netloc, nuxeo_id) \n return url","def show( self, trans, id, **kwd ):\n # Example URL: http://localhost:9009/api/repository_revisions/bb125606ff9ea620\n try:\n repository_metadata = metadata_util.get_repository_metadata_by_id( trans, id )\n repository_metadata_dict = repository_metadata.as_dict( value_mapper=default_value_mapper( trans, repository_metadata ) )\n repository_metadata_dict[ 'url' ] = web.url_for( controller='repository_revisions',\n action='show',\n id=trans.security.encode_id( repository_metadata.id ) )\n return repository_metadata_dict\n except Exception, e:\n message = \"Error in the Tool Shed repository_revisions API in show: %s\" % str( e )\n log.error( message, exc_info=True )\n trans.response.status = 500\n return message","def get_absolute_url(self):\n return reverse('product-detail', args=[str(self.id)])","def get_absolute_url(self):\n return reverse('subject-detail', args=[str(self.id)])","def link(self, obj):\n return format_html(\n '<a href=\"{url}\">{url}</a>',\n url='https://sms.cam.ac.uk/collection/{}'.format(obj.id)\n )","def get_absolute_url(self):\n return reverse(\"projects-detail\", kwargs={'pk': self.pk})"],"string":"[\n \"async def issue(ctx, number: Option(int, \\\"Issue number\\\")):\\n url = f\\\"{repo}/issues/{number}\\\"\\n view = discord.ui.View()\\n view.add_item(discord.ui.Button(label=\\\"View Issue\\\", url=url))\\n await ctx.respond(f\\\"Here's a link\\\", view=view)\",\n \"def build_issue_tracker_url(issue_id):\\n issue_tracker_tmpl = settings.ISSUE_TRACKER_BUG_URL_TMPL\\n url_tmpl = issue_tracker_tmpl if issue_tracker_tmpl else 'http://issue/%s'\\n return url_tmpl % issue_id\",\n \"def get_issue_url(self, key):\\n raise NotImplementedError\",\n \"def get_issue(self, issue_id, **kwargs):\\n raise NotImplementedError\",\n \"def get_issue(self, issue_id, **kwargs):\\n raise NotImplementedError\",\n \"def openNewIssueUrl(self):\\r\\n url = QUrl(\\\"https://github.com/Freeseer/freeseer/issues/new\\\")\\r\\n QDesktopServices.openUrl(url)\",\n \"def get_issue_url(testcase):\\n issue_tracker = get_issue_tracker_for_testcase(testcase)\\n if not issue_tracker:\\n return None\\n\\n issue_id = (\\n testcase.bug_information\\n if testcase.bug_information else testcase.group_bug_information)\\n if not issue_id:\\n return None\\n\\n # Use str(issue_id) as |group_bug_information| might be an integer.\\n return issue_tracker.issue_url(str(issue_id))\",\n \"async def pr(ctx, number: Option(int, \\\"Pull request number\\\")):\\n url = f\\\"{repo}/issues/{number}\\\"\\n view = discord.ui.View()\\n view.add_item(discord.ui.Button(label=\\\"View Pull Request\\\", url=url))\\n await ctx.respond(f\\\"Here's a link\\\", view=view)\",\n \"def TrackerURL(issue):\\n # make the server/project customizable?\\n return 'http://code.google.com/p/lilypond/issues/detail?id=%s' % issue\",\n \"def find_url(ticketID):\\n uri = 'https://jira.duraspace.org/browse/' + ticketID\\n print uri\\n return uri\",\n \"def issue(self, id):\\r\\n return issues.RepoIssue(self, id)\",\n \"def show(self, solution_id):\\n token = self._get_token()\\n __kargs = self.kargs.copy()\\n __kargs[\\\"url_path\\\"] += \\\"/%s\\\"%(str(solution_id))\\n return process_result(Request(__kargs,token).get())\",\n \"def get_issue(self, context):\",\n \"def issueView(context, issue):\\n\\n args = {\\n 'item': None,\\n 'group': None\\n }\\n \\n if issue.item != None:\\n item = issue.item.item\\n args['item'] = item\\n args['status'] = item.status.all()\\n if issue.group != None:\\n args['group'] = issue.group.group\\n\\n return render_to_string('issueView.html', args, context)\",\n \"def get_issue(self, issue_id):\\n try:\\n json = self.get('repos/%(owner)s/%(repo)s/issues/%(issue_id)d' % {\\n 'owner': self.repo_owner,\\n 'repo': self.repo_name,\\n 'issue_id': issue_id,\\n })\\n\\n label_list = [label_dict['name'] for label_dict in json['labels']]\\n\\n return Issue(json['number'], label_list)\\n except ResourceNotFound:\\n return None\",\n \"def find_issue_id(self):\",\n \"def test_issue_detail(self):\\n response = self.client.get(url_for(\\n 'issues.issuedetailresource',\\n issue_number=self.TARGET_ISSUE_NUMBER))\\n self.assertEqual(response.status_code, 200)\\n self.assertTrue(response.json)\",\n \"def get_detail_url(order_id):\\n return reverse(\\\"order-detail\\\", args=[str(order_id)])\",\n \"def show(ID):\\n review = parse(gh_request('GET', '/repos/:user/:repo/pulls/:id', uri_vars={'id': ID}))\\n printers.print_review(review)\",\n \"def report(issues, show_urls=False):\\r\\n # titles may have unicode in them, so we must encode everything below\\r\\n if show_urls:\\r\\n for i in issues:\\r\\n role = 'ghpull' if 'merged' in i else 'ghissue'\\r\\n print('* :%s:`%d`: %s' % (role, i['number'],\\r\\n i['title'].encode('utf-8')))\\r\\n else:\\r\\n for i in issues:\\r\\n print('* %d: %s' % (i['number'], i['title'].encode('utf-8')))\",\n \"def pr_link(repo, id):\\n\\n return '[#{id}](https://github.com/{repo}/pull/{id})'.format(id=id, repo=repo)\",\n \"def link(self, id):\\r\\n return links.RepoLink(self, id)\",\n \"def get_issue(self):\\n issue_id = self.kwargs['issue_id']\\n try:\\n issue = Issue.objects.get(pk=issue_id)\\n except ObjectDoesNotExist:\\n raise ObjectNotFound('Not found')\\n if issue.project.pk != self.project.pk:\\n raise ObjectNotFound('Not found')\\n return issue\",\n \"def get_redmine_issue_url():\\n win32clipboard.OpenClipboard()\\n url = win32clipboard.GetClipboardData()\\n win32clipboard.CloseClipboard()\\n # check the URL http:xxx.xxx.xxx.xx:8080/redmine/issues/1234\\n path, issue_num = os.path.split(url)\\n path, issue = os.path.split(path)\\n path, redmine = os.path.split(path)\\n # check the URL is redmine issues\\n if validators.url(url) and issue_num.isdigit() and issue=='issues' and redmine=='redmine' :\\n URL = url;\\n print(f'URL for redmine issue (get from Clipboard)')\\n print(f'{URL}')\\n else:\\n print(f'Clipboard contents :')\\n if validators.url(url):\\n print(f'\\\\t{url}')\\n else:\\n print(f'\\\\tnon-url data')\\n print(f'\\\\n')\\n print(f'Usage: Copy (e.g. Ctrl+C) redmine issue URL (e.g. http://192.168.0.1:8080/redmine/issues/1234 ) to clipboard and execute script')\\n URL = \\\"\\\"\\n\\n return URL\",\n \"def get_absolute_url(self):\\n return reverse(\\\"jewelry_detail\\\", args = [str(self.id)])\",\n \"def detail_url(reteta_id):\\n return reverse('reteta:reteta-detail', args=[reteta_id])\",\n \"def get_absolute_url(self):\\n return reverse('injury-detail', args=[str(self.id)])\",\n \"def problem(request, problemid):\\n curr_problem = get_object_or_404(Problem, id=problemid)\\n context = {'problem': curr_problem}\\n return render(request, 'code_challenge/problem.html', context)\",\n \"def get_url_from_id(doc_id):\\n return f\\\"{BASE_URL}/view/{doc_id}\\\"\",\n \"async def launchbyid(self, ctx, launchid, *args):\\n launches = launchlibrary.Launch.fetch(api, id=launchid)\\n if launches:\\n launch = launches[0]\\n if launch.agency != None:\\n embedcolor = discord.Colour(await get_color(launch.agency.id))\\n else:\\n embedcolor = discord.Colour(5592405)\\n embed = discord.Embed(title=launch.name, colour=embedcolor)\\n embed.set_footer(text=\\\"ID: {0}\\\".format(launch.id))\\n embed.set_thumbnail(url=launch.rocket.image_url)\\n embed = time_description(embed, launch)\\n for arg, formatter in (('-r', reasons), ('-v', videourls)):\\n if arg in args:\\n msg = formatter(embed, launch)\\n await ctx.send(embed=embed)\\n else:\\n await ctx.send(\\\"No launch with that ID.\\\")\",\n \"def detail(request, question_id):\\n question = get_object_or_404(Question, pk=question_id)\\n return render(request, 'problemfinder/details.html', {'question': question})\",\n \"def issue(self, issue_id):\\r\\n return issues.RepoIssue(self, issue_id)\",\n \"def get_absolute_url(self):\\n\\t\\treturn reverse('source-detail', args=[str(self.id)])\",\n \"def detail_url(movie_id):\\n return reverse('movie:movie-detail', args=[movie_id])\",\n \"def show(self, item_id):\\n pass\",\n \"def _get_issue(repo, objid):\\n issue = None\\n if not repo.settings.get(\\\"issue_tracker\\\", True):\\n raise PagureEvException(\\\"No issue tracker found for this project\\\")\\n\\n session = _get_session()\\n issue = pagure.lib.query.search_issues(session, repo, issueid=objid)\\n\\n if issue is None or issue.project != repo:\\n raise PagureEvException(\\\"Issue '%s' not found\\\" % objid)\\n\\n if issue.private:\\n # TODO: find a way to do auth\\n raise PagureEvException(\\n \\\"This issue is private and you are not allowed to view it\\\"\\n )\\n\\n return issue\",\n \"def get_absolute_url(self):\\n\\n return reverse('kid-detail', args=[str(self.id)])\",\n \"def category_details_url(id):\\n return reverse('category:category-detail', args=[id])\",\n \"def _LoginOrIssueEntryURL(mr, config):\\n issue_entry_url = servlet_helpers.ComputeIssueEntryURL(mr, config)\\n if mr.auth.user_id:\\n return issue_entry_url\\n else:\\n after_login_url = framework_helpers.FormatAbsoluteURL(\\n mr, urls.ISSUE_ENTRY_AFTER_LOGIN)\\n return _SafeCreateLoginURL(mr, after_login_url)\",\n \"def status_url(self, username, id):\\n return urllib.parse.urljoin(self.instance, f'/p/{urllib.parse.quote(username)}/{id}')\",\n \"def get_absolute_url(self):\\n return reverse('bleedinfo-detail', args=[str(self.id)])\",\n \"def detail_url(product_id):\\n return reverse('product:product-detail', args=[product_id])\",\n \"def exam_url(self, obj):\\n request = self.context.get(\\\"request\\\")\\n return reverse(\\\"exam-detail\\\", args=[obj.id], request=request)\",\n \"def detail_url(recipe_id):\\n return reverse('recipe:recipe-detail',args=[recipe_id])\",\n \"def show(request):\\n patchsets = request.issue.get_patchset_info(request.user, None)\\n last_patchset = first_patch = None\\n if patchsets:\\n last_patchset = patchsets[-1]\\n if last_patchset.patches:\\n first_patch = last_patchset.patches[0]\\n messages = []\\n has_draft_message = False\\n for msg in request.issue.messages:\\n if not msg.draft:\\n messages.append(msg)\\n elif msg.draft and request.user and msg.sender == request.user.email():\\n has_draft_message = True\\n num_patchsets = len(patchsets)\\n return respond(request, 'issue.html', {\\n 'first_patch': first_patch,\\n 'has_draft_message': has_draft_message,\\n 'is_editor': request.issue.edit_allowed,\\n 'issue': request.issue,\\n 'last_patchset': last_patchset,\\n 'messages': messages,\\n 'num_patchsets': num_patchsets,\\n 'patchsets': patchsets,\\n })\",\n \"def detail_url(recipe_id):\\n return reverse('recipe:recipe-detail', args=[recipe_id])\",\n \"def detail_url(recipe_id):\\n return reverse('recipe:recipe-detail', args=[recipe_id])\",\n \"def detail_url(recipe_id):\\n return reverse('recipe:recipe-detail', args=[recipe_id])\",\n \"def handle_issue(self, evt):\\n author = self.format_nickname(evt.author)\\n if evt.new:\\n short_url = \\\"https://dolp.in/i%d\\\" % evt.issue\\n url = Tags.UnderlineBlue(short_url)\\n msg = 'Issue %d created: \\\"%s\\\" by %s - %s'\\n msg = msg % (evt.issue, evt.title, author, url)\\n else:\\n short_url = \\\"https://dolp.in/i%d/%d\\\" % (evt.issue, evt.update)\\n url = Tags.UnderlineBlue(short_url)\\n msg = 'Update %d to issue %d (\\\"%s\\\") by %s - %s'\\n msg = msg % (evt.update, evt.issue, evt.title, author, url)\\n self.bot.say(msg)\",\n \"def show(request):\\n if _use_new_ui(request):\\n return _serve_new_ui(request)\\n\\n patchsets = request.issue.get_patchset_info(request.user, None)\\n last_patchset = first_patch = None\\n if patchsets:\\n last_patchset = patchsets[-1]\\n if last_patchset.patches:\\n first_patch = last_patchset.patches[0]\\n messages = []\\n generated_messages = []\\n has_draft_message = False\\n # Keep track of the last non-generated message.\\n message_index = -1\\n last_user_message_index = -1\\n for msg in request.issue.messages:\\n if msg.auto_generated:\\n generated_messages.append(msg)\\n if not msg.draft:\\n messages.append(msg)\\n message_index += 1\\n if not msg.auto_generated:\\n last_user_message_index = message_index\\n elif msg.draft and request.user and msg.sender == request.user.email():\\n has_draft_message = True\\n num_patchsets = len(patchsets)\\n\\n issue = request.issue\\n issue.description = cgi.escape(issue.description)\\n issue.description = urlize(issue.description)\\n re_string = r\\\"(?<=BUG=)\\\"\\n re_string += \\\"(\\\\s*(?:[a-z0-9-]+:)?\\\\d+\\\\s*(?:,\\\\s*(?:[a-z0-9-]+:)?\\\\d+\\\\s*)*)\\\"\\n expression = re.compile(re_string, re.IGNORECASE)\\n issue.description = re.sub(expression, _replace_bug, issue.description)\\n src_url = _map_base_url(issue.base)\\n\\n display_generated_msgs = False\\n if request.user:\\n account = models.Account.current_user_account\\n display_generated_msgs = account.display_generated_msgs\\n\\n landed_days_ago = issue.get_time_since_landed()\\n landed_days_ago = landed_days_ago.days if landed_days_ago else 'unknown'\\n\\n return respond(request, 'issue.html', {\\n 'first_patch': first_patch,\\n 'has_draft_message': has_draft_message,\\n 'is_editor': request.issue.edit_allowed,\\n 'issue': request.issue,\\n 'last_patchset': last_patchset,\\n 'messages': messages,\\n 'generated_messages': generated_messages,\\n 'last_user_message_index': last_user_message_index,\\n 'num_patchsets': num_patchsets,\\n 'patchsets': patchsets,\\n 'src_url': src_url,\\n 'display_generated_msgs': display_generated_msgs,\\n 'offer_cq': request.issue.is_cq_available,\\n 'landed_days_ago': landed_days_ago,\\n })\",\n \"def get_detail_URL(recipe_id):\\n return reverse('recipeapp:recipe-detail', args=[recipe_id])\",\n \"def show_board(self, boardID):\\n return self.app.get('/board/' + boardID, follow_redirects = True)\",\n \"def issue_id(self) -> str:\\n return self._yaml[\\\"commit\\\"][\\\"issue_id\\\"]\",\n \"def instance_url(self) -> str:\\n easypost_id = self.get(\\\"id\\\")\\n if not easypost_id:\\n raise Error(\\\"%s instance has invalid ID: %r\\\" % (type(self).__name__, easypost_id))\\n return \\\"%s/%s\\\" % (self.class_url(), easypost_id)\",\n \"def get_absolute_url(self):\\n return reverse('model-detail-view', args=[str(self.id)])\",\n \"def get_absolute_url(self):\\n return reverse('report', args=[str(self.id)])\",\n \"def get_absolute_url(self):\\n return reverse('blogger-detail', args=[str(self.id)])\",\n \"def get_absolute_url(self):\\n return reverse('patient-detail', args=[str(self.id)])\",\n \"def detail_url(recipe_id): # /api/recipe/recipes/ID\\n return reverse('recipe:recipe-detail', args = [recipe_id])\",\n \"def task_url(self, obj):\\n request = self.context.get(\\\"request\\\")\\n return reverse(\\\"task-detail\\\", args=[obj.id], request=request)\",\n \"def command_show(problem):\\r\\n print problem.get_html()\",\n \"def get_observation_url(self, observation_id):\\n pass\",\n \"def get_absolute_url(self):\\n return reverse('bl-detail', args=[str(self.id)])\",\n \"def project_detail(request, project_id):\\n try:\\n project = Project.objects.get(pk=project_id)\\n project.description = markdown.markdown(bleach.clean(project.description, strip=True), extensions=['markdown.extensions.fenced_code'])\\n p2 = Project.objects.get(pk=project_id)\\n user_profile = UserProfile.objects.get(email=request.session['email'])\\n submissions_list = Submission.objects.filter(project=project)\\n except Project.DoesNotExist:\\n raise Http404(\\\"Project does not exist\\\")\\n context = {'project': project, 'submissions_list':submissions_list, 'current_user': request.session['email'], 'user_profile': user_profile}\\n return render(request, 'projects/details.html', context)\",\n \"def details_url(self):\\n if self._data.get('details_url'):\\n path = self._data.get('details_url')\\n try:\\n path, hash_ = path.split('#')\\n hash_ = '#' + hash_\\n except ValueError:\\n hash_ = ''\\n return '{}?from_activity={}{}'.format(path, self._data.get('id'), hash_)\",\n \"def render_url(self, object_id):\\r\\n return reverse(\\\"%s:insert_%s_%s_render\\\" % (\\r\\n self.admin_site.name,\\r\\n self.model._meta.app_label,\\r\\n self.model._meta.module_name\\r\\n ), args=(object_id,))\",\n \"def detail_url(pizza_id):\\n return reverse('pizza:pizza-detail', args=[pizza_id])\",\n \"def test_issue_get_issue(self):\\n pass\",\n \"def view_entry(id):\\n db = get_db()\\n cur = db.execute('select id, title, ingredients, steps, tags, url \\\\\\n from entries where id = ? order by id desc',\\n [id.strip()])\\n entries = cur.fetchall()\\n return render_template('view_entry.html', entries=entries)\",\n \"def view_experiment(request,id):\\n\\texp = Experiment.objects.get(id=id)\\n\\tpossibly_related = get_related(exp)\\n\\treturn list_detail.object_detail(request,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tqueryset=Experiment.objects.filter(id=id),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tobject_id=exp.id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\ttemplate_name='experiments/experiment.html',\\n\\t\\t\\t\\t\\t\\t\\t\\t\\textra_context= {\\\"possibly_related\\\" : possibly_related})\",\n \"def detail_url(ingredient_id):\\n return reverse(\\\"recipe:ingredient-detail\\\", args=[ingredient_id])\",\n \"def showtask(id):\\n\\n tasks = Task.query.filter_by(id=id)\\n return render_template('home/taskshowall/dashboard_showtask.html',tasks=tasks,title=\\\"tasks\\\")\",\n \"def show(self, req, id):\\n context = req.environ['manila.context']\\n\\n try:\\n message = self.message_api.get(context, id)\\n except exception.MessageNotFound as error:\\n raise exc.HTTPNotFound(explanation=error.msg)\\n\\n return self._view_builder.detail(req, message)\",\n \"def get_absolute_url(self):\\n return reverse('food-detail', args=[str(self.id)])\",\n \"def detail_url(ingredient_id):\\n return reverse('recipe:ingredient-detail', args=[ingredient_id])\",\n \"def detail_url(author_id):\\n return reverse('author-books', args=[author_id])\",\n \"def get_absolute_url(self):\\n return reverse('model-detail-view', args=[str(self.id)])\",\n \"def get_absolute_url(self):\\n return reverse('model-detail-view', args=[str(self.id)])\",\n \"def explore_href(self, job_id):\\n ## class QueuePage()\\n jdict = mysql.job_get_dict(job_id)\\n\\n if self.admin:\\n page = \\\"admin\\\"\\n else:\\n page = \\\"explore\\\"\\n\\n if self.admin:\\n if jdict[\\\"tls_model\\\"] == \\\"ANISO\\\":\\n l = ['<a href=\\\"webtlsmd.cgi?page=%s&job_id=%s\\\">%s</a> (ANISO)' % (\\n page, job_id, job_id)]\\n else:\\n l = ['<a href=\\\"webtlsmd.cgi?page=%s&job_id=%s\\\">%s</a>' % (\\n page, job_id, job_id)]\\n\\n if jdict[\\\"user_name\\\"] != \\\"\\\":\\n l.append('<br>%s' % (jdict[\\\"user_name\\\"]))\\n\\n if jdict[\\\"user_comment\\\"] != \\\"\\\" or jdict[\\\"user_comment\\\"] != None:\\n l.append('<br>%s' % (jdict[\\\"user_comment\\\"]))\\n\\n if jdict[\\\"email\\\"] != \\\"\\\":\\n l.append('<br>%s' % (jdict[\\\"email\\\"]))\\n\\n return \\\"\\\".join(l)\\n\\n if mysql.job_get_private_job(job_id):\\n ## Return job number only (non-clickable)\\n job_number = re.match(r'[^_]*', job_id)\\n if job_number:\\n return job_number.group(0)\\n return 'private'\\n\\n return '<a href=\\\"webtlsmd.cgi?page=%s&job_id=%s\\\">%s</a>' % (\\n page, job_id, job_id)\",\n \"def get_url(self, item):\\n config = {}\\n uuid = self.data.get('uuid', None)\\n obj = uuidToObject(uuid)\\n if uuid and obj:\\n config = copy.copy(self.get_config(obj))\\n\\n url = u'{0}{1}'.format(self.view_url(obj), item.id.value)\\n if config.get('modify_url', True):\\n url = u'{0}___{1}-{2}'.format(\\n url,\\n item.title.value,\\n item.location.value,\\n )\\n return url\",\n \"def issues_by_id(self, repository_id, access_token=None):\\n return self._complete_request_by_id(\\n repository_id, \\\"issues\\\", access_token)\",\n \"async def get_issue(self, issue: int) -> \\\"AIOGitHubAPIRepositoryIssue\\\":\\n _endpoint = f\\\"/repos/{self.full_name}/issues/{issue}\\\"\\n\\n response = await self.client.get(endpoint=_endpoint)\\n return AIOGitHubAPIRepositoryIssue(self.client, response)\",\n \"def get_absolute_url(self):\\n return reverse('card-detail', args=[str(self.id)])\",\n \"def issue_path(project: str, location: str, issue_model: str, issue: str,) -> str:\\n return \\\"projects/{project}/locations/{location}/issueModels/{issue_model}/issues/{issue}\\\".format(\\n project=project, location=location, issue_model=issue_model, issue=issue,\\n )\",\n \"def getIdLink(self):\\n return self.urlLink()\",\n \"def issue_details(board, issue_number):\\n issue = BoardIssue(board, issue_number)\\n return issue.details()\",\n \"def recipe_detail_url(recipe_id):\\n return reverse('recipes:recipe-detail', args=[recipe_id])\",\n \"def get_problem(id):\\n return query(WEB_EXAMPLE_BASE + f\\\"/classical/problem/{id}\\\")\",\n \"def self_link_with_id(self) -> str:\\n return pulumi.get(self, \\\"self_link_with_id\\\")\",\n \"def self_link_with_id(self) -> str:\\n return pulumi.get(self, \\\"self_link_with_id\\\")\",\n \"def display_url(self) -> Optional[str]:\\n return pulumi.get(self, \\\"display_url\\\")\",\n \"def show_orion_url(self, obj):\\n return obj.orion_url\",\n \"def view(self, _type, task_id):\\n try:\\n return self._super.do(**{\\n \\\"method\\\": \\\"GET\\\",\\n \\\"url\\\": \\\"{}/tasks/report/{}\\\".format(self.baseurl, task_id)\\n })\\n except AnalyserUpstreamError as e:\\n if \\\"404\\\" in str(e.code):\\n return None\",\n \"def show_story(story_id, arguments):\\r\\n\\r\\n story = load_story(story_id, arguments)\\r\\n\\r\\n\\r\\n print\\r\\n print colored('{:12s}{:4s}{:9s}{:10s} {}'.format('#{}'.format(story.story_id),\\r\\n initials(story.owned_by),\\r\\n story.story_type,\\r\\n estimate_visual(story.estimate),\\r\\n story.name), 'white', attrs=['bold'])\\r\\n print\\r\\n print colored(\\\"Story Url: \\\", 'white', attrs=['bold']) + colored(story.url, 'blue', attrs=['underline'])\\r\\n print colored(\\\"Description: \\\", 'white', attrs=['bold']) + story.description\\r\\n\\r\\n if len(story.notes) > 0:\\r\\n print\\r\\n print bold(\\\"Notes:\\\")\\r\\n for note in story.notes:\\r\\n print \\\"[{}] {}\\\".format(initials(note.author), note.text)\\r\\n\\r\\n\\r\\n if len(story.tasks) > 0:\\r\\n print\\r\\n print bold(\\\"Tasks:\\\")\\r\\n for task in story.tasks:\\r\\n print \\\"[{}] {}\\\".format(x_or_space(task.complete), task.description)\\r\\n\\r\\n if len(story.attachments) > 0:\\r\\n print\\r\\n print bold(\\\"Attachments:\\\")\\r\\n for attachment in story.attachments:\\r\\n print \\\"{} {}\\\".format(attachment.description, colored(attachment.url,'blue',attrs=['underline']))\\r\\n\\r\\n print\",\n \"def get_object_view_url(self, nuxeo_id):\\n parts = urlparse.urlsplit(self.nx.conf[\\\"api\\\"])\\n url = \\\"{}://{}/Nuxeo/nxdoc/default/{}/view_documents\\\".format(parts.scheme, parts.netloc, nuxeo_id) \\n return url\",\n \"def show( self, trans, id, **kwd ):\\n # Example URL: http://localhost:9009/api/repository_revisions/bb125606ff9ea620\\n try:\\n repository_metadata = metadata_util.get_repository_metadata_by_id( trans, id )\\n repository_metadata_dict = repository_metadata.as_dict( value_mapper=default_value_mapper( trans, repository_metadata ) )\\n repository_metadata_dict[ 'url' ] = web.url_for( controller='repository_revisions',\\n action='show',\\n id=trans.security.encode_id( repository_metadata.id ) )\\n return repository_metadata_dict\\n except Exception, e:\\n message = \\\"Error in the Tool Shed repository_revisions API in show: %s\\\" % str( e )\\n log.error( message, exc_info=True )\\n trans.response.status = 500\\n return message\",\n \"def get_absolute_url(self):\\n return reverse('product-detail', args=[str(self.id)])\",\n \"def get_absolute_url(self):\\n return reverse('subject-detail', args=[str(self.id)])\",\n \"def link(self, obj):\\n return format_html(\\n '<a href=\\\"{url}\\\">{url}</a>',\\n url='https://sms.cam.ac.uk/collection/{}'.format(obj.id)\\n )\",\n \"def get_absolute_url(self):\\n return reverse(\\\"projects-detail\\\", kwargs={'pk': self.pk})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6973641","0.67749053","0.64473206","0.63432825","0.63432825","0.6329339","0.6315905","0.6126929","0.6092506","0.603496","0.60116416","0.6002365","0.5886086","0.58832836","0.5794558","0.579267","0.57825845","0.5674633","0.565682","0.5643689","0.563147","0.5619779","0.56052077","0.5604773","0.5600051","0.5592567","0.55875933","0.55796075","0.5552871","0.5539025","0.55204153","0.5520057","0.5519885","0.5475923","0.5462073","0.54464793","0.5443029","0.54419255","0.54366815","0.5415072","0.5414791","0.5388805","0.538057","0.5363239","0.5350363","0.5344026","0.5344026","0.5344026","0.5333344","0.5331416","0.53294945","0.5327887","0.53278345","0.5327512","0.5324642","0.5323852","0.5302336","0.52949154","0.5283704","0.5274242","0.5271622","0.52713287","0.52637124","0.52583355","0.52546","0.5246361","0.5242617","0.5242588","0.5239718","0.5238522","0.5229358","0.52248937","0.5207321","0.5198864","0.519815","0.51923966","0.5184243","0.5184243","0.51718944","0.5159327","0.51592594","0.51588774","0.5157138","0.5151162","0.51502174","0.5142915","0.5131703","0.51283467","0.5122765","0.5122765","0.5099668","0.50976974","0.50968355","0.5080746","0.5074907","0.5071709","0.50688756","0.5066666","0.50630397","0.5055637"],"string":"[\n \"0.6973641\",\n \"0.67749053\",\n \"0.64473206\",\n \"0.63432825\",\n \"0.63432825\",\n \"0.6329339\",\n \"0.6315905\",\n \"0.6126929\",\n \"0.6092506\",\n \"0.603496\",\n \"0.60116416\",\n \"0.6002365\",\n \"0.5886086\",\n \"0.58832836\",\n \"0.5794558\",\n \"0.579267\",\n \"0.57825845\",\n \"0.5674633\",\n \"0.565682\",\n \"0.5643689\",\n \"0.563147\",\n \"0.5619779\",\n \"0.56052077\",\n \"0.5604773\",\n \"0.5600051\",\n \"0.5592567\",\n \"0.55875933\",\n \"0.55796075\",\n \"0.5552871\",\n \"0.5539025\",\n \"0.55204153\",\n \"0.5520057\",\n \"0.5519885\",\n \"0.5475923\",\n \"0.5462073\",\n \"0.54464793\",\n \"0.5443029\",\n \"0.54419255\",\n \"0.54366815\",\n \"0.5415072\",\n \"0.5414791\",\n \"0.5388805\",\n \"0.538057\",\n \"0.5363239\",\n \"0.5350363\",\n \"0.5344026\",\n \"0.5344026\",\n \"0.5344026\",\n \"0.5333344\",\n \"0.5331416\",\n \"0.53294945\",\n \"0.5327887\",\n \"0.53278345\",\n \"0.5327512\",\n \"0.5324642\",\n \"0.5323852\",\n \"0.5302336\",\n \"0.52949154\",\n \"0.5283704\",\n \"0.5274242\",\n \"0.5271622\",\n \"0.52713287\",\n \"0.52637124\",\n \"0.52583355\",\n \"0.52546\",\n \"0.5246361\",\n \"0.5242617\",\n \"0.5242588\",\n \"0.5239718\",\n \"0.5238522\",\n \"0.5229358\",\n \"0.52248937\",\n \"0.5207321\",\n \"0.5198864\",\n \"0.519815\",\n \"0.51923966\",\n \"0.5184243\",\n \"0.5184243\",\n \"0.51718944\",\n \"0.5159327\",\n \"0.51592594\",\n \"0.51588774\",\n \"0.5157138\",\n \"0.5151162\",\n \"0.51502174\",\n \"0.5142915\",\n \"0.5131703\",\n \"0.51283467\",\n \"0.5122765\",\n \"0.5122765\",\n \"0.5099668\",\n \"0.50976974\",\n \"0.50968355\",\n \"0.5080746\",\n \"0.5074907\",\n \"0.5071709\",\n \"0.50688756\",\n \"0.5066666\",\n \"0.50630397\",\n \"0.5055637\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77199227"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74592,"cells":{"query":{"kind":"string","value":"Save opening issues into a text file"},"document":{"kind":"string","value":"def cache_issue(self, msg, args):\n self._asset_bind(msg)\n yield (\"Processing....\")\n trans = self._translation_util(msg)\n query = \"repo:{} is:open type:issue\".format(\n task_repository_name()\n )\n res = trans.cache_issues(query, OPEN_CACHE, MAX_RESULT)\n yield \"{} records had been cached\".format(res)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def issue_text_dump(issue):\n file_path = os.path.join(BIO_REQUESTS_DIR,\n str(issue.id),\n str(issue.id) + '_' + str(issue.subject) + '_redmine_details.txt')\n with open(file_path, 'w+') as file:\n for attr in dir(issue):\n file.write('{}: {}\\n\\n'.format(attr, getattr(issue, attr)))\n return file_path","def save_changes(self):\n with open(base_dir + str(self.name) + \".txt\", mode=\"w\") as f:\n f.writelines([str(self.initial_amount)+'\\n', str(self.spent)+'\\n', \" \".join(self.allocs)+'\\n'])\n f.write(\" \".join([str(k) for k in self.alloc_amounts]) + \"\\n\")\n f.write(\" \".join([str(k) for k in self.alloc_spent]) + \"\\n\")\n f.writelines(self.expenditures)\n self.report()","def cleaning_file():\n f = open (\"report_for_judy_part2.txt\", \"w\")\n f.close()","def saveToFile(html):\n #print(\"Saving to file.\")\n html += \"\\n\"\n #open necessary files to save\n logFile = open(\"postLog_{0}_{1}.txt\".format(os.path.splitext(path)[0], dateTimeNow), \"a\")\n logFile.write(html)\n logFile.close()\n #print(\"Check Point.\")","def cmd_wq(self):\n out_file = open(\"DoubleLinkedList.txt\", \"w\")\n out_file.write(str(self.get_text()))\n out_file.close()\n print(\"File saved to DoubleLinkedList.txt\")","def WriteErrorsToFile():\n if(not __errorsTracked__ is None):\n if(len(__errorsTracked__)>0):\n formattedLogName = '_'.join[\"ErrorLog\",\"GarageChecker\",datetime.date,datetime.time]\n WriteToFile(formattedLogName,__errorsTracked__)\n __errorsTracked__ = []","def write_to_txt(self):\r\n file = open(self.output_path, 'w')\r\n for question_id in self.question_ids:\r\n file.write(self.questions[question_id].question_string+str(self.questions[question_id].answer)+'\\n')\r\n file.close()","def saveUsage(self, filePath):\n message = time.strftime('%c') + ' : '\n for spot in self.getParkingSpots():\n message += str(spot.id) + ', ' + spot.status + '; '\n with open(filePath, 'a+') as outfile:\n outfile.write(message + '\\n')\n pass","def _write_data_out(solutions, unable_to_resolve, unresolvables):\n print('')\n print('------------------------')\n print('--- Progress So Far: ---')\n print('Solved: ' + str(len(solutions)))\n print('Error while resolving: ' + str(len(unable_to_resolve)))\n print('Unresolvable conflicts: ' + str(len(unresolvables)))\n print('Saving progress to json.')\n print('------------------------')\n print('')\n json.dump(solutions, open(fname_solutions, 'w'))\n json.dump(unable_to_resolve, open(fname_errors, 'w'))\n json.dump(unresolvables, open(fname_unresolvables, 'w'))","def writingtxt(pathf,ticker,nb,typ):\n\n assert len(pathf) == nb, 'the number of issues does not match' # an assert to check that the number of paths parsed is equal nb\n \n \n filename = [typ+ticker.lower()+str(2020-i)+'.txt' for i in reversed(range(0,nb))] # loop to create list of filenames\n \n for p,n in zip(pathf,filename): # looping through files and filesname\n\n raw_html = open(p,encoding = 'utf-8') # opening the html given the path\n \n soup = bsoup(raw_html, 'lxml')# decoding the html\n\n ltables = soup.find_all('table') # locating the tables\n \n for z in ltables: # removing the table\n z.decompose()\n \n bla = open(n,\"w+\", encoding = 'utf-8') #opening a new .txt file with filename\n bla.write(soup.text) # writing the text of the report in the .txt file\n \n\n return None","def notest_file(text):\n if debug == 2:\n print(text)\n with open(\"info_file.txt\", \"a\", encoding=\"utf-8\", ) as f:\n f.write(text + \"\\n\")\n elif debug == 1:\n with open(\"info_file.txt\", \"a\", encoding=\"utf-8\", ) as f:\n f.write(text + \"\\n\")","def test_write_race_results_to_file():\n number = random.randint(1, 3)\n f1.write_race_results_to_file(number)\n with open(f\"results_for_race_{number}.txt\", encoding=\"utf-8\") as opened_file:\n list_of_lines = opened_file.readlines()\n assert len(list_of_lines) == 13","def save(self,f,open_and_close=False):\n\n if open_and_close:\n f = file(file_name,'w')\n for k in self.__dict__.keys():\n if k[0]!='_': #Exclude 'private' variables ('_dont_touch')\n f.write('# %s : %s \\n'%(k,self.__dict__[k]))\n f.close()\n\n else:\n for k in self.__dict__.keys():\n if k[0]!='_': #Exclude 'private' variables ('_dont_touch')\n f.write('# %s : %s \\n'%(k,self.__dict__[k]))\n return f","def save(self,f,open_and_close=False):\n\n if open_and_close:\n f = file(file_name,'w')\n for k in self.__dict__.keys():\n if k[0]!='_': #Exclude 'private' variables ('_dont_touch')\n f.write('# %s : %s \\n'%(k,self.__dict__[k]))\n f.close()\n\n else:\n for k in self.__dict__.keys():\n if k[0]!='_': #Exclude 'private' variables ('_dont_touch')\n f.write('# %s : %s \\n'%(k,self.__dict__[k]))\n return f","def save(self, fn: str) -> None:\n fout = open(fn, 'w')\n for t,x in zip(self.times,self.milestones):\n fout.write('%f\\t%d '%(t,len(x)))\n fout.write(' '.join([str(xi) for xi in x]))\n fout.write('\\n')\n fout.close()","def export(self):\n memento = self.create_memento()\n try:\n f = open(\"story.txt\", \"w\")\n try:\n f.write(memento.__str__())\n finally:\n f.close()\n except IOError:\n print 'IOError while exporting story!'","def save(self):\n \n fileName=self.characterName+\"_\"+self.race+\"_\"+self.classType+\"_lvl_\"+str(self.level)\n new_file = open(str(fileName)+\".txt\",\"w\")\n new_file.write(\"~~~~~~~~~~~ \"+self.characterName+\" the \"+self.race+\" \"+self.classType+\" ~~~~~~~~~~~\\n\\n\")\n new_file.write(\"Level: \"+str(self.level)+\" HP: \"+str(self.hp)+\" XP: \"+str(self.xp)+\" Hit Dice: \"+str(self.level)+str(self.hit_dice[self.classType])+\"\\n\")\n new_file.write(str(self.abilityScores()))\n new_file.write(\"\\n\\n~~~~~~~~~ Skills ~~~~~~~~~\\n\")\n for i in self.skills:\n new_file.write(\"\\n\"+i+\" \"+\"(\"+skills[i.lower()].upper()+\")\")\n new_file.write(\"\\n\\n~~~~~~~~~ Traits ~~~~~~~~~\\n\")\n for i in self.traits:\n new_file.write(\"\\n ~~\"+i+\"~~\\n \"+str(self.traits[i])+\"\\n\")\n new_file.write(\"\\n\\n~~~~~~~~~ Specialty: \"+self.specialty+\" ~~~~~~~~\\n\")\n new_file.write(\"\\n \"+self.specialtyStory+\"\\n\")\n new_file.write(\"\\n ~~~~ Feats ~~~~\\n\")\n for i in range(1,self.level+1):\n if i == 1 or i%3 == 0:\n new_file.write(\"\\n Level \"+str(i)+\": \"+self.feats[i]['name']+' '\\\n \"(\"+self.feats[i]['type']+\")\\n\"\\\n ' \"'+self.feats[i]['description']+'\"\\n\\n')\n if 'prereq' in self.feats[i]:\n new_file.write(\" Prerequisite: \"+self.feats[i]['prereq']+\"\\n\")\n if 'benefit' in self.feats[i]:\n new_file.write(\" Benefit: \"+self.feats[i]['benefit']+\"\\n\")\n if 'effect' in self.feats[i]:\n new_file(\" Effect: \"+self.feats[i]['effect']+\"\\n\")\n \n new_file.write(\"\\n\\n~~~~~~~~~ Background: \"+self.background+\" ~~~~~~~~\\n\")\n if self.backgroundProfession == '':\n pass\n else:\n new_file.write(\"Profession: \"+self.backgroundProfession)\n new_file.write(\"\\n \"+self.backgroundStory)\n \n new_file.close()\n print \"File \"+str(fileName)+\".txt saved.\"","def write_to_file(self, filepath, mode = \"a\"): \n if \"r\" in mode: \n print(\"Only accepts write and append modes\")\n return \n with open(filepath, mode) as f: \n f.write(\"{}\\n\".format(self.title))\n verified, seen, ratio = self.get_verified_ratio()\n f.write(\"Verified Names: {}\\n\".format(str(verified)))\n f.write(\"Names: {}\\n\".format(str(seen)))\n f.write(\"Ratio: {}\\n\".format(str(ratio)))","def saveLogFile(self, fname = \"data/status.txt\"):\n with open(fname, 'w') as f:\n f.write(\"<br>\\n\".join(self.logLines))\n self.log(\"wrote \"+fname)","def _toFile(self):\n pass","def exercise2(self):\n my_file = open('my_file.txt', 'w+')\n my_file.write(\"Look Ma! I'm a file\")\n my_file.close()\n content = my_file.read()\n print content","def save_to_file(self) -> None:\n abs_errors = 0\n art_errors = 0\n for i, doc in enumerate(tqdm(self.results[\"documents\"])):\n paper, abstract = self.__get_article_and_abstract(doc[\"eid\"])\n if abstract:\n abstract_file = open(self.out_dir + \"[{}] Abstract_{}.txt\".format(doc[\"date\"], doc[\"title\"]), \"w\")\n abstract_file.write(abstract)\n abstract_file.close()\n else:\n abs_errors += 1\n if paper:\n file = open(self.out_dir + \"[{}] {}.txt\".format(doc[\"date\"], doc[\"title\"]), \"w\")\n file.write(paper)\n file.close()\n else:\n art_errors += 1\n print(\"Not saved/found: {} abstracts, {} articles\".format(abs_errors, art_errors))","def do_the_issues(user_id, repo_id):\n with tempfile.TemporaryDirectory() as tmp:\n path = os.path.join(tmp, \"{}_{}_issues.txt\".format(repo_id, user_id))\n issues_initial_url = get_initial_url_issues(user_id, repo_id)\n resp_obj = requests.get(issues_initial_url, headers=headers)\n # prase the initial request. for Issue\n all_issues = json.loads(resp_obj.text)\n with open(path, \"w\") as out_stream:\n for an_issue in all_issues:\n print(an_issue, file=out_stream)\n print(\"the len of resp is {}\".format(len(all_issues)))\n LINK_HEADER = \"Link\"\n next_url = None\n if LINK_HEADER in resp_obj.headers:\n # parse next page (if present)\n next_url = parse_next_url(resp_obj.headers[LINK_HEADER])\n # subsequent page\n while next_url:\n resp_obj = requests.get(next_url, headers=headers)\n all_issues = json.loads(resp_obj.text)\n with open(path, \"a\") as out_stream:\n for an_issue in all_issues:\n print(an_issue, file=out_stream)\n if LINK_HEADER in resp_obj.headers:\n next_url = parse_next_url(resp_obj.headers[LINK_HEADER])\n print(next_url)\n else:\n next_url = None\n GsUpload.upload_blob(GS_BUCKET_NAME, path, basename(path))\n print(\"the issues path is \" + str(path))","def save(self):\n if PYTHON3:\n fileobj = open(self.filename, 'w', encoding=self.ENCODING, errors=\"replace\")\n else:\n fileobj = open(self.filename, 'w')\n self.save_to_fileobj(fileobj)\n fileobj.close()","def writing_get_game(file_name, title):\n result = str(reports.get_game(file_name, title))\n with open (\"report_for_judy_part2.txt\", \"+a\") as f:\n f.write(result)\n f.write(\"\\n\")","def write_to_file(info: List[str]) -> None:\n return","def save2txt(obj, file: str):\n with open(file, \"w\") as f:\n print(obj, file=f)","def saveProblem(num, name, content, url):\n # Only include first 5 words of name\n try:\n filename = name.split()[:4]\n except IndexError:\n filename = name.split()\n filename = '_'.join(filename)\n\n # Exclude special characters from filenames\n for x in ('\\\\', '/', '?', '\"', '\\'', '...', ',',\n '..', '$', '<', '>', '|', ':', ';'):\n filename = filename.replace(x, '')\n # Add number to start\n filename = f'{num!s:0>3.3}-{filename}'\n\n # Paths for the md file and solution file\n problemPath = OUTPUT_FOLDER / (filename + '.md')\n scriptPath = OUTPUT_FOLDER / f'../solutions/{filename}.py'\n exist = '' if scriptPath.exists() else ' - not been created.'\n\n # Create markdown text and write to file\n text = (f\"# Problem {num} - {name}\"\n f\"\\n\\n[Problem Page]({url})\"\n f\"\\n\\n[Solution Script]({scriptPath.relative_to(OUTPUT_FOLDER)}){exist}\"\n f\"\\n\\n{content}\")\n with open(problemPath, 'wb') as f:\n f.write(text.encode('utf8'))\n\n return","def save_file(self):\n f = open(self._file_name, \"w\")\n try:\n for rental in self.list:\n rental_str = self.obj_to_string(rental)\n f.write(rental_str)\n f.close()\n except Exception as e:\n raise e","def save_reason(reason):\n filename = 'tmp/reason.txt'\n\n with open(filename, 'a') as file_object:\n file_object.write(reason + '\\n')","def write_summary(self, text):\n summary = Path(self.path, self.folder + \"_summary.txt\")\n with open(summary, \"a\") as summary_file:\n summary_file.write(f\"{text}\\n\")\n shutil.copyfile(summary, summary.parent.parent / summary.name)","def write(self, path):\n\n with open(path, 'w') as file:\n file.write(f\"\\n{self.subject}\")\n file.write(8 * '\\n')\n file.write(\"0\")\n file.write(8 * '\\n')\n file.write(self.data.date[0].strftime(\"%d.%m.%Y\"))\n file.write(7 * '\\n')\n file.write(\"Unknown Line\")\n file.write(26 * '\\n')\n file.write(self.valid_measurements + \"\\n\")\n printing_df = self.data.drop(columns=['date', 'time'])\n printing_df['hours'] = self.data.time.map(lambda x: x.strftime(\"%H\"))\n printing_df['minutes'] = self.data.time.map(lambda x: x.strftime(\"%M\"))\n order = ['hours', 'minutes', 'SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']\n printing_df = printing_df[order]\n printing_df.fillna(-9999, inplace=True)\n printing_df.replace('EB', -9998, inplace=True)\n printing_df.replace('AB', -9997, inplace=True)\n printing_df[['SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']] = printing_df[\n ['SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']].astype(int).astype(str)\n printing_df.replace('-9999', '\"\"', inplace=True)\n printing_df.replace('-9998', '\"EB\"', inplace=True)\n printing_df.replace('-9997', '\"AB\"', inplace=True)\n printing_df.to_csv(file, header=None, index=None, quoting=csv.QUOTE_NONE, line_terminator='\\n')\n\n xml_node = ET.Element('XML')\n xml_node.extend(self._dict_to_etree(self.metadata))\n xml_line = ET.tostring(xml_node, encoding=\"unicode\")\n file.write(xml_line)","def to_file(self, f: str) -> None:\n with open(f, \"w\") as open_file:\n open_file.write(\"\\n\".join(self.itos) + \"\\n\")","def test_writing(self):\n with contextlib.closing(RiggedDailyLogFile(self.name, self.dir)) as log:\n log.write(\"123\")\n log.write(\"456\")\n log.flush()\n log.write(\"7890\")\n\n with open(self.path) as f:\n self.assertEqual(f.read(), \"1234567890\")","def save_to_file(name, elements, status, current_section):\n # status 3 = dealing with about section\n # status 4 = dealing with posts\n\n try:\n # dealing with About Section\n if status == 3:\n workEducation = elements[0].text.replace(\"\\n\", \"|\")\n influencerObject[\"Influencer\"][\"Description\"][\"Text\"] = workEducation\n\n # dealing with Posts\n # elif status == 4:\n # extract_and_write_posts(elements, name)\n # return\n\n except:\n print(\"Exception (save_to_file)\", \"Status =\",\n str(status), sys.exc_info()[0])\n\n return","def test_writing(self):\n with contextlib.closing(logfile.LogFile(self.name, self.dir)) as log:\n log.write(\"123\")\n log.write(\"456\")\n log.flush()\n log.write(\"7890\")\n\n with open(self.path) as f:\n self.assertEqual(f.read(), \"1234567890\")","def save_problem(problem, filepath):\n with open(filepath, \"w\") as out:\n print \"Vectors count: %s\" % (len(problem))\n for vector in problem:\n out.write(\"%s \" % (vector[0]))\n for index, value in sorted(vector[1].iteritems()):\n out.write(\"%s:%s \" % (index, value))\n out.write(\"\\n\")","def save_file():\n generic = pull_list()\n result = list()\n i = 0\n while True:\n try:\n if generic[i].startswith('CVE'):\n cve_pattern = \"^CVE-\\d+-\\d+|^CVE-\\d+-[X]+\"\n header = re.findall(cve_pattern, generic[i])[0]\n i += 1\n notes = list()\n while not generic[i].startswith('CVE'):\n commit_pattern = \"http[s]?:\\/\\/.+commit\\/[\\S]+\"\n if re.search(commit_pattern, generic[i]):\n link = re.findall(commit_pattern, generic[i])\n notes.append(link[0])\n i += 1\n if notes != list():\n result.append(Data(header, notes))\n except IndexError:\n print('Finished')\n break\n return result","def test_write_championship_to_file():\n f1.write_championship_to_file()\n with open(\"championship_results.txt\") as new_file:\n list_of_lines = new_file.readlines()\n assert len(list_of_lines) == 13","def save_todo_file(self):\n\n if os.path.exists('TODO.txt'):\n os.remove('TODO.txt')\n todo_fp = open('TODO.txt', 'w')\n todo_items = self.todo_scroll_cell.get_item_list()\n in_progress_items = self.in_progress_scroll_cell.get_item_list()\n done_items = self.done_scroll_cell.get_item_list()\n for item in todo_items:\n todo_fp.write(item + '\\n')\n todo_fp.write('__IN_PROGRESS__' + '\\n')\n for item in in_progress_items:\n todo_fp.write(item + '\\n')\n todo_fp.write('__DONE__' + '\\n')\n for item in done_items:\n todo_fp.write(item + '\\n')\n todo_fp.close()\n self.master.show_message_popup('Saved', 'Your TODO list has been saved!')","def print_to_file(start, stop, time_worked, work_text, work_log):\n today = datetime.date.today()\n\n record = ' || %.2f || %.2f || %.4f hours || %s\\n' % (start, stop, time_worked/3600, work_text)\n\n #if it is a new file you have the option to set a start time for the project\n # and how many hours a week you want to work\n if not os.path.isfile(work_log):\n while True:\n option = raw_input('\\nThis is a new log, would you like to specify a start date and a hours per week goal for the project? (y/n): ').lower()\n if option == 'y':\n date = raw_input('\\nplease enter the start date of the project (dd-mm-yyyy): ')\n hours_per_week = raw_input('\\nplease enter the number of hours you intend to work on the project per week: ')\n try:\n datetime.datetime.strptime(date, '%d-%m-%Y')\n if hours_per_week.isdigit():\n f = open(work_log, 'a')\n f.write('#! || ' + date + ':' + hours_per_week + '\\n')\n f.close()\n break\n else:\n print \"\\nPlease enter a valid number for hours to work!\\n\"\n except ValueError:\n print \"\\nPlease enter a valid date!\\n\"\n\n else:\n break\n\n\n f = open(work_log, 'a')\n print '\\n\\n' + today.strftime('%b-%d-%Y') + record\n f.write(today.strftime('%b-%d-%Y') + record)\n f.close()","def save_txt():\n # open file and append, if it doesn't exist then create it.\n with open('journal_entries.txt', 'a+') as f:\n # .get the input in text widget at the first line, '0th' character, then read until the end\n f.write(\"\\n\" + get_date_time())\n for i in range(len(entries)):\n string = entries[i].get('1.0', 'end-1c')\n if string:\n f.write(\"\\n\" + string)","def save_last_error_report(trace):\n try:\n rfile = os.path.join(get_file_dir(), 'last_report_error.txt')\n with open(rfile, 'w') as f:\n f.write(trace)\n except:\n log(\"Error writing error report file\")","def store_story(self):\n\n file_path = self.dir + '/' + self.filename + '.txt'\n file_path = self.check_path(file_path)\n with open(file_path, 'a') as f_out:\n f_out.write('Name: ' + self.name + '\\n')\n f_out.write('Age: ' + self.age + '\\n')\n f_out.write('Country of origin: ' + self.origin + '\\n')\n f_out.write('Company: ' + self.company + '\\n')\n f_out.write('First country of arrival: ' + self.route)\n if self.route.lower() != 'the netherlands':\n f_out.write(' >>> DUBLIN PROCEDURE\\n')\n else:\n f_out.write('\\n')\n f_out.write('Entrance: ' + self.entrance + '\\n')\n f_out.write('Documentation: ' + self.documentation + '\\n')\n f_out.write('Exclusion: ' + self.exclusion + '\\n')\n f_out.write('Conflict: ' + self.conflict + '\\n')\n f_out.write('Inhumanity: ' + self.inhumanity + '\\n')\n f_out.write('Family with a permit: ' + self.family + '\\n')\n return","def save_to_file(self):\n # Create a new file name based off date and time\n file_name = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S_RTI_CFG.txt\")\n file_path = os.path.expanduser(\"~\\\\Desktop\\\\\"+file_name)\n\n file = open(file_path, 'w')\n file.write(self.commandFileTextBrowser.toPlainText())\n file.close()\n\n self.parent.statusBar().showMessage('File saved to ' + file_path)","def _reportErrorMsg(self, ErrorMessage, outputFile):\n f=self.openFile(outputFile, \"a\") #open otuputFile for appending\n self._insertErrorMsg(ErrorMessage, f)\n f.close()","def write_to_files():\n\t# Create output files\n\toutput = [None, \\\n\t\t open(\"priority-1.txt\", \"w\"), \\\n\t\t open(\"priority-2.txt\", \"w\"), \\\n\t\t open(\"priority-3.txt\", \"w\"), \\\n\t\t open(\"priority-4.txt\", \"w\"), \\\n\t\t open(\"priority-5.txt\", \"w\"), ]\n\n\t# Loop over all fields and write them to the correct file\n\tfor field in sorted(reportlog.keys()):\n\t\tpriority = reportlog[field]['priority']\n\t\tlabel = reportlog[field]['label']\n\n\t\toutput[priority].write(\"intphas_%s\\t%s\\n\" % (field, label))\n\t\toutput[priority].flush()\n\n\t# Close files\n\tfor i in [1,2,3,4,5]:\n\t\toutput[i].close()","def save_failed_tests_info():\n global g_failed_tests_info_dict\n if len(g_failed_testnames) > 0: # found failed test\n if os.path.isfile(g_failed_tests_dict) and os.path.getsize(g_failed_tests_dict) > 10:\n try:\n g_failed_tests_info_dict=json.load(open(g_failed_tests_dict, 'r'))\n except:\n init_failed_tests_dict()\n\n # with open(g_failed_tests_dict, 'rb') as dict_file:\n # g_failed_tests_info_dict = pickle.load(dict_file)\n else: # file not found, create new dict\n init_failed_tests_dict()\n\n with open(g_summary_text_filename, 'a') as failed_file:\n with open(g_daily_failure_csv, 'w') as daily_failure:\n for index in range(len(g_failed_testnames)):\n\n testInfo = ','.join([g_timestring, g_job_name, str(g_build_id), g_git_hash, g_node_name,\n g_unit_test_type, g_failed_testnames[index]])\n failed_file.write(testInfo+'\\n')\n daily_failure.write(testInfo+'\\n')\n # update failed tests dictionary\n update_failed_test_info_dict(g_failed_testnames[index], g_failed_test_paths[index])\n json.dump(g_failed_tests_info_dict, open(g_failed_tests_dict, 'w'))","def count_corrects_tissue(input_file, output_file):\n\ttissues_dict = {}\n\twith open(input_file) as fl:\n\t\tfl.readline()\n\t\tfor line in fl:\n\t\t\tline = line.rstrip()\n\t\t\tdata = line.split('\\t')\n\t\t\tif not data[2] in tissues_dict:\n\t\t\t\ttissues_dict[data[2]] = {'correct':0,'incorrect':0}\n\t\t\tif data[2] == data[1]:\n\t\t\t\ttissues_dict[data[2]]['correct'] += 1\n\t\t\telse:\n\t\t\t\ttissues_dict[data[2]]['incorrect'] += 1\n\n\twith open(output_file,'w') as out:\n\t\tfirst = True\n\t\ttissue_order = []\n\t\t#print tissue\n\t\tfor tissue in tissues_dict:\n\t\t\tif first:\n\t\t\t\ttissue_order.append(tissue)\n\t\t\t\tout.write(tissue)\n\t\t\t\tfirst = False\n\t\t\telse:\n\t\t\t\tout.write(\"\\t%s\" % tissue)\n\t\t\t\ttissue_order.append(tissue)\n\t\tout.write(\"\\n\")\n\t\tfirst = True\n\t\t#prints corrects\n\t\tfor tissue in tissue_order:\n\t\t\tif first:\n\t\t\t\tout.write(str(tissues_dict[tissue]['correct']))\n\t\t\t\tfirst = False\n\t\t\telse:\n\t\t\t\tout.write(\"\\t%s\" % str(tissues_dict[tissue]['correct']))\n\n\t\tout.write(\"\\n\")\n\t\tfirst = True\n\t\t#prints incorrects\n\t\tfor tissue in tissue_order:\n\t\t\tif first:\n\t\t\t\tout.write(str(tissues_dict[tissue]['incorrect']))\n\t\t\t\tfirst = False\n\t\t\telse:\n\t\t\t\tout.write(\"\\t%s\" % str(tissues_dict[tissue]['incorrect']))","def to_file(self, file_path, smirnoff_data):\n pass","def save(*messages):\n data = Parser.parse_texts(*messages[1:])\n hour = time.strftime(\"_%H_%M_%S\")\n today = time.strftime(\"_%d_%m_%Y\")\n title = Parser.parse_text(messages[0])\n\n file = open(\"./logs/\"+threading.currentThread().getName()+today+\".log\",'a+')\n file.write(\"\\n==\"+title+hour+\"==\\n\")\n if type(data) is dict: #Dictionary with each value being a triplet. From get_all_items\n for key in data.keys():\n file.write(Parser.parse_text(key) + \" -> \"+ Parser.parse_text(str(data[key].x)) +\"\\n\")\n elif type(data) is list: #From get_item, market item, attribute listings\n for listing in data:\n file.write(str(listing.id)+\" - \"+str(listing.price/100)+\" euros\\n\")\n else: #plain text\n file.write(Parser.parse_text(data))\n file.write(\"=====================================\\n\")\n file.close()","def __save_article_to_file(self, content):\n with open(\"article.txt\", 'w') as out:\n out.write(content)","def save_review_to_file(lines, filename):\n data = '\\n'.join(lines)\n file = open(filename, 'w')\n file.write(data)\n file.close()","def write_error_report(self):\n\n with open('runReport.txt', 'a') as report:\n report.write(\"Number of Hits: \" + str(self.num_hits) + '\\n')\n report.write(\"Number of Requests: \" + str(self.num_requests) + '\\n')\n report.write(\"Hit Rate: \" + str((self.num_hits / self.num_requests)))\n report.write(\"Datafiles downloaded: \" + str(self.num_datafiles))\n now = datetime.now()\n dt_string = now.strftime(\"%H:%M %m/%d/%Y\")\n report.write(\"Run finished \" + dt_string)","def write_to_file(self):\n\t\tfile = open(\"states.txt\", \"w\")\n\t\t\n\t\tpointer = self.head\n\t\twhile pointer != None:\n\t\t\tfile.write(pointer.state + \"\\t\" + pointer.info)\t\n\t\t\tpointer = pointer.next\n\n\t\tfile.close()","def os_open_comm_log( self, ):\r\n AppGlobal.os_open_txt_file( self.parameters.comm_logging_fn )","def save_txt_file():\n global output_on_display\n if data_base == '':\n mistake_load_table()\n else:\n save_name = asksaveasfilename(title=\"Select file\", filetypes=((\"TXT\", \"*.txt\"), (\"all files\", \"*.*\")),\n defaultextension='.txt')\n if Path(save_name).suffix == '.txt':\n data_txt = output_on_display.get('1.0', 'end')\n f = open(save_name, 'w')\n f.write(data_txt)\n f.close()","def write_to_file(self, filename: str) -> None:","def report(db, openfile):\n pass","def save(self):\n\n # We need to set updated, even if it's the same as created,\n # so we have a consistent timestamp to sort issues by.\n self.updated = time.time()\n\n if not hasattr(self, 'id'):\n # IDs are generated from the JSON dump of the\n # issue. This includes the UTC-format timestamp, so \n # they can be considered pretty unique.\n self.created = self.updated \n self.id = get_hash(to_json(self.fields))\n # set the paths now that we have an id\n self._set_paths()\n \n # Make the parent directory if it doesn't exist.\n if not os.path.isdir(self.paths['root']):\n os.mkdir(self.paths['root'])\n # Make the comments dir if it doesn't exist.\n if not os.path.isdir(self.paths['comments']):\n os.mkdir(self.paths['comments'])\n # Save it in the db.\n self.tracker.db.insert(self)\n # Save it.\n return self.to_file(self.paths['issue'])","def write_txt_file(title, abstract, f_out):\n\n print(\n '*' * 40,\n '\\n',\n '[Title] {}'.format(title),\n '\\n',\n '[Abstract] {}'.format(abstract),\n file=f_out\n )","def write_to_file(content, filename):\n if not os.path.isfile(filename): # Checking if file already exists, don't append data if it does.\n for j in range(len(content)): # For each dialog in dialogues array.\n with open(filename, 'a') as file: # Open a text file in append mode and write data into it.\n for k in range(len(content[j][0])):\n file.write('{0} {1}\\n'.format(str(content[j][0][k]).lower().split(\"(\")[0],\n str(content[j][1][k])).lower())","def reset_used():\n with open(LOG_FILEPATH, 'w+') as logfile:\n pass","def save(self):\n filename = os.path.expanduser(\"~/\" + self.name)\n print(filename)\n np.savetxt(filename + \"_left.txt\", self.central)\n np.savetxt(filename + \"_right.txt\", self.boundaries)","def save_solution(self, solution_path, file_path):\n node_info = \"\"\n with open(file_path, \"w\") as file:\n file.write(\n \"Strategy: \"\n + str(self.strategy)\n + \"\\n Max Depth: \"\n + str(self.max_depth)\n + \"\\n Depth Increment: \"\n + str(self.depth_increment)\n + \"\\n Pruning: \"\n + str(self.pruning)\n + \"\\n ---SOLUTION---: \"\n )\n for node in solution_path:\n node_info = \"\\n\\n ID: \" + str(node.id)\n if node.last_action != None:\n node_info += \"\\n Action: \" + str(node.last_action)\n node_info += (\n \"\\n Cost: \"\n + str(node.cost)\n + \"\\n Depth: \"\n + str(node.node_depth)\n + \"\\n Heuristic: \"\n + str(node.state.entropy())\n + \"\\n F value: \"\n + str(node.f)\n + \"\\n Node: \"\n + str(node.state.create_md5())\n )\n file.write(node_info)\n node_info = \"\"\n\n file.write(\n \"\\n TOTAL COST: \" + str(solution_path[len(solution_path) - 1].cost)\n )","def write_file(self):\n if self._write_file == None:\n return\n\n try:\n out = file(self._write_file, \"w\")\n except IOError, e:\n print e\n sys.exit(1)\n out.writelines(\"A cases\") \n out.close()","def save_bugs(self, year, chunk, bugs, errors=None):\n directory = self.get_bugs_path(year)\n return self._save(directory, chunk, bugs, errors, switch='bugs')","def write_puzzle(to_file: str, solution: str):\n with open(to_file, \"w\") as file:\n file.write(solution)","def put_in_file(txt,fd,defined_symbols):\n\n\tif len(txt) > 0: fd.write(txt+'\\n')\n\treferenceKeys = sorted(defined_symbols.iterkeys())\n\tfor i in referenceKeys:\n\t\tdict_of_files = defined_symbols[i]\t\t\t\t\t\t# dict of files:list of tuples\n\t\ttry:\n\t\t\tfd.write('name='+i+'\\n')\n\t\t\tif len(dict_of_files) > 0:\n\t\t\t\tlist_of_files = sorted(dict_of_files.iterkeys()) \t# list of tuples (file , list of tuples)\n\t\t\t\tfor\tj in list_of_files:\n\t\t\t\t\tfd.write('file='+j+'\\n') \n\t\t\t\t\tfor k in dict_of_files[j]:\n\t\t\t\t\t\tif k[1] == DEFINED:\n\t\t\t\t\t\t\ttp = \"defin\"\n\t\t\t\t\t\telif k[1] == USED:\n\t\t\t\t\t\t\ttp = \"refer\"\n\t\t\t\t\t\telif k[1] == MACRO:\n\t\t\t\t\t\t\ttp = \"macro\"\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\ttp = \"both \"\n\t\t\t\t\t\ts = str(k[0])\n\t\t\t\t\t\tfor p in k[2]:\n\t\t\t\t\t\t\ts += ':'+str(p)\n\t\t\t\t\t\tfd.write(' line='+s+' type='+tp)\t# line ond type\n\t\t\t\t\tfd.write('\\n')\n\t\texcept :\n\t\t\tprint \"ERR - \" + i\n\t\t\tprint dict_of_files","def save_result(file_name, text):\n\n filename = os.path.join(fileDir, 'tools/results/{}.txt'.format(file_name))\n\n with open(filename, \"a+\") as file:\n file.write(text)","def saveToTextFile(self, file_name):\n with open(file_name, 'w') as file_obj:\n for item in self.items:\n print(item, file=file_obj)","def save_text_file(i):\n\n fn = i['text_file']\n\n s = i['string']\n\n try:\n s = s.replace('\\r', '')\n except Exception as e:\n pass\n\n try:\n s = s.replace(b'\\r', b'')\n except Exception as e:\n pass\n\n m = 'w'\n if i.get('append', '') == 'yes':\n m = 'a'\n\n try:\n s = s.encode('utf8')\n except Exception as e:\n pass\n\n try:\n # if sys.version_info[0]>2:\n # f=open(fn, m+'b')\n # f.write(s)\n # else:\n f = open(fn, m+'b')\n f.write(s)\n except Exception as e:\n return {'return': 1, 'error': 'problem writing text file='+fn+' ('+format(e)+')'}\n\n f.close()\n\n return {'return': 0}","def open_logfile(self):\r\n if self.output_option == 2:\r\n self.ER_file = open(self.result_filename, 'w')","def save(self,file):\n\n with open(file,\"w\") as f:\n f.write(self.to_string())","def write_polling_location_txt(self):\n\n plt = self.build_polling_location_txt()\n\n\n # Drop base_df columns.\n plt.drop(['index', 'office_name', 'official_name', 'phone', 'email', 'ocd_division', 'phone', 'location_name',\n 'address1', 'address2', 'city', 'state', 'zip_code', 'start_time', 'end_time', 'start_date',\n 'end_date', 'is_only_by_appointment', 'is_or_by_appointment', 'directions', 'notes'],\n inplace=True, axis=1)\n\n plt = self.dedupe(plt)\n plt = plt[plt.address_line.notnull()]\n\n plt.to_csv(config.output + 'polling_location.txt', index=False, encoding='utf-8') # send to txt file\n plt.to_csv(config.output + 'polling_location.csv', index=False, encoding='utf-8') # send to csv file","def test_output(self):\n work_logs = [WorkLog(\"MYB-7\", datetime(2020, 1, 20), 3600, \"René Doe\"),\n WorkLog(\"MYB-5\", datetime(2020, 1, 18), 3600, \"John Doe\"),\n WorkLog(\"MYB-5\", datetime(2020, 1, 18), 5400, \"John Doe\"),\n WorkLog(\"MYB-5\", datetime(2020, 1, 12), 3600, \"John Doe\")]\n\n issue_myb_5 = Issue(10005, \"MYB-5\", \"Summary of issue MYB-5\", \"MYB-3\", \"Summary of the parent issue of MYB-5\", 3600, 900, datetime(2020, 1, 15))\n issue_myb_5.issue_start_date = datetime(2020, 1, 10)\n issue_myb_7 = Issue(10007, \"MYB-7\", \"Summary of issue MYB-7\", None, None, None, None, None)\n\n issues = [issue_myb_5,\n issue_myb_7]\n\n stdout = sys.stdout\n with open('jira-time-report-console.txt', 'w') as sys.stdout:\n jiratimereport.process_work_logs(\"console\", issues, work_logs)\n sys.stdout = stdout\n self.assertTrue(filecmp.cmp('console_output.txt', 'jira-time-report-console.txt'))\n\n jiratimereport.process_work_logs(\"csv\", issues, work_logs)\n self.assertTrue(filecmp.cmp('csv_output.csv', 'jira-time-report.csv'))\n\n jiratimereport.process_work_logs(\"excel\", issues, work_logs)\n expected_excel = pd.read_excel('excel_output.xlsx')\n actual_excel = pd.read_excel('jira-time-report.xlsx')\n self.assertTrue(expected_excel.equals(actual_excel))","def flowdetail_save(fd):\n return IMPL.flowdetail_save(fd)","def dump2txt(cls, argv):\n try:\n for filename in argv[1:]:\n parser = cls(filename)\n invalid_str = r\"[\\\\/:*?\\\"<>|]\" # Not allowed to use filename\n # Remove invalid text\n subject = re.sub(invalid_str, \"\", parser.subject)\n # Remove local time \"+09:00\", \"-\"\n title_date = parser.date[:-len(\"+09:00\")].replace(\"-\", \"\")\n # Remove invalid strings\n date = re.sub(invalid_str, \"\", title_date)\n result = parser.get_attr_data()\n # Overwrite same date+subject eml\n with open(f'{date}_{subject}.txt', 'w',\n encoding='utf-8') as _f:\n _f.write(result)\n except BaseException as e:\n with open('eml2ext_error.txt', 'w', encoding='utf-8') as _f:\n print(f'error {e}')\n # _f.write(e)","def ledgerWrite(self):\n if(self.back == 'y'):\n open(\"ledger.txt\",'a').write(str(num_files) + ' ' + str(self.file_count) + ' ' + self.file_path + ' ' + \"y\" + '\\n')\n else:\n open(\"ledger.txt\",'a').write(str(num_files) + ' ' + str(self.file_count) + ' ' + self.file_path + ' ' + \"n\" + '\\n')\n dbUp.upload_file(\"ledger.txt\", '/ledger.txt')","def save_summary(model, model_name, stage_no):\n stringlist = []\n model.summary(print_fn=lambda x: stringlist.append(x))\n short_model_summary = \"\\n\".join(stringlist)\n \n with open(eval_path+\"{}_model_summary_stage_{}.txt\".format(model_name, stage_no), \"w\") as text_file:\n print(short_model_summary, file=text_file)","def save_percentages_to_txt(self):\n #initialise a dictionary with the list of NEs taken from \n #https://spacy.io/api/annotation\n if self.lang == 'en':\n named_entity_divided_per_type = {'PERSON':0, 'NORP':0, 'FAC':0, \n 'ORG':0, 'GPE':0, 'LOC':0, \n 'PRODUCT':0, 'EVENT':0, \n 'WORK_OF_ART':0, 'LAW':0, \n 'LANGUAGE':0, 'DATE':0, 'TIME':0, \n 'PERCENT':0, 'MONEY':0, \n 'QUANTITY':0, 'ORDINAL':0, \n 'CARDINAL':0}\n if self.lang == 'de' or self.lang == 'fr':\n named_entity_divided_per_type = {'PER':0, 'ORG':0, 'LOC':0, \n 'MISC':0}\n #define counter for total amount of NE\n amount_ne_total = 0\n #update the dictionary depending on the type of NE and update the \n #counter\n for sublist in self.named_entity_list_total:\n for entry in sublist:\n old_amount = (named_entity_divided_per_type.get(entry[3]))\n named_entity_divided_per_type[entry[3]] = old_amount + 1\n amount_ne_total += 1\n #get percentages of the various NE types over the amount of nouns and \n #numerals\n percentages = {}\n for i in named_entity_divided_per_type:\n percentages[i] = round(100*named_entity_divided_per_type.get(i)/self.amount_nouns_and_num_total,1)\n #write the output\n outfile = open(('percentages_' + self.lang + '_' + self.method + \n '.txt'), 'w')\n outfile.write('Method used: ' + self.method + '\\n')\n outfile.write('----------------------------------------------\\n')\n outfile.write('Amount NEs: '+ str(amount_ne_total) + '\\n')\n outfile.write('Amount nouns and numerals: '+ \n str(self.amount_nouns_and_num_total) + '\\n')\n outfile.write('----------------------------------------------\\n')\n outfile.write('Percentages:\\n')\n for i in percentages:\n outfile.write(i+'\\t'+str(percentages.get(i)) + '%\\n')\n outfile.close()\n #build the plot\n if self.lang == 'en':\n plt.figure(figsize=(12,6))\n plt.ylabel('#NE / #(NOUN,PROPN,NUM) [%]', fontsize=14)\n elif self.lang == 'de' or self.lang == 'fr':\n plt.figure(figsize=(2.5,6))\n plt.ylabel('#NE / #(NOUN,PROPN) [%]', fontsize=14)\n plt.bar(range(len(percentages)), list(percentages.values()), \n align='center')\n plt.xticks(range(len(percentages)), list(percentages.keys()), \n fontsize=14, rotation='vertical')\n plt.title('Method: '+self.method+'\\nLanguage: '+self.lang, fontsize=20)\n plt.savefig('percentages_'+self.lang+'_'+self.method, \n bbox_inches='tight')\n return","def save_exception(exc):\n LOG.error(\"Error - %s\", str(exc))\n hour = time.strftime(\"_%H_%M_%S\")\n today = time.strftime(\"_%d_%m_%Y\")\n data = (str(exc)+traceback.format_exc())\n\n file = open(\"./logs/ERROR_\"+threading.currentThread().getName()+today+\".log\",'a+') #Replace to fix OSError\n file.write(\"\\n==\"+hour+\"==\\n\")\n file.write(Parser.parse_text(data))\n file.write(\"=====================================\\n\")\n file.close()","def fopenhelp(self):","def log_to_file(text, status='INFO'):\n outfile = open(LogName, 'a')\n outfile.write(timestamp()+' - '+status+' - '+str(text)+'\\n')\n outfile.close()","def store_name(author):\n with open(\"ref.txt\",\"a\") as fp:\n fp.write(str(reference)+\"\"+'\\n')","def _save_target_info(self):\n \n #make sure the file exists\n path = self.communicator.image_store.project_path + \\\n self.target_file_name\n fout = open(path, 'w')\n\n print str(1)\n print str(len(self.target_list)-1)\n for i in range(1, len(self.target_list)):\n fout.write(self.target_list[i].format_info())\n fout.write(\"\\n\\n\")\n fout.close()","def writeText2File(loginfo, mfile):\n with open(mfile,'a') as f:\n f.writelines('%s\\n' %loginfo)\n f.close()","def _log_save_errors(self, ex_errs: Iterable[str]) -> None:\n # Check for the root of save problems\n new_errs = [e for e in self.aug.match(\"/augeas//error\") if e not in ex_errs]\n\n for err in new_errs:\n logger.debug(\n \"Error %s saving %s: %s\", self.aug.get(err), err[13:len(err) - 6],\n self.aug.get(f\"{err}/message\"))\n logger.error(\n \"Unable to save files: %s.%s\", \", \".join(err[13:len(err) - 6] for err in new_errs),\n f\" Save Notes: {self.configurator.save_notes}\" if self.configurator.save_notes else \"\")","def save_highscores(self, contents):\n\t\ttry:\n\t\t\twith open(self.filename, 'w') as f_obj:\n\t\t\t\tf_obj.write(json.dumps(contents)) #save as json\n\t\texcept FileNotFoundError:\n\t\t\tprint('File for highscores not found! Call 016 741 6243 for assistance.')","def save_data_to_file(self, html_detail_tab, name_category):\n name_saved_data = name_category + \".html\"\n with open(os.path.join(self.current_path, name_saved_data), 'a+') as f:\n try:\n f.write(html_detail_tab)\n except IOError as io:\n logger.error(\n \"caught an io exception while writing to the file\",\n io)","def save_to_file(urls):\n try:\n with open('url.txt', 'w') as file:\n for url in urls:\n file.write(url + \"\\n\")\n except:\n print(\"ERROR SAVING FILE\")","def open_read_write(file_to_open=PROBLEM_FILE):\n\n with open(file_to_open, 'r') as working_file:\n # turns the opened file into a list using a list comprehension\n working_file = [sentence.strip() for sentence in working_file.readlines()]\n\n # iterates through the enumerated file displaying a tuple\n # of (index, string)\n for sentence in enumerate(working_file): #iterates through the file\n if sentence[0] % 2: #Checks to see if the index is divisible by 2\n print(sentence[1]) #prints the string","def write_to_file(info, mode='w', file=\"output4.txt\"):\n with open(file, mode, encoding='utf-8') as f:\n for line in info:\n f.write(' '.join(map(str, line)) + '\\n')","def write(self, fname):\n pass","def write_info_to_file(self):\n\n self.info.write_mission_info()\n\n self.logger.info(\"Mission instance write succeeded.\")","def save_to_file(self, string):\n with open(self.output_path, \"w\") as text_file:\n text_file.write(string)\n print \"Saved to file \" + self.output_path","def open(self) -> None:","def open(self) -> None:","def open(self) -> None:","def save_log(file_path=None, stat=\"\"):\n fp = open(file_path, mode=\"a+\")\n fp.write(stat + \"\\n\")\n fp.close()","def getReport():\n\tpending = len(open(\"todo.txt\").readlines())\n\tdone = len(open(\"done.txt\").readlines())\n\tprint(\n\t \"{} Pending : {} Completed : {}\"\n\t .format(datetime.today().strftime(\"%Y-%m-%d\"),\n\t pending,\n\t done\n\t ))"],"string":"[\n \"def issue_text_dump(issue):\\n file_path = os.path.join(BIO_REQUESTS_DIR,\\n str(issue.id),\\n str(issue.id) + '_' + str(issue.subject) + '_redmine_details.txt')\\n with open(file_path, 'w+') as file:\\n for attr in dir(issue):\\n file.write('{}: {}\\\\n\\\\n'.format(attr, getattr(issue, attr)))\\n return file_path\",\n \"def save_changes(self):\\n with open(base_dir + str(self.name) + \\\".txt\\\", mode=\\\"w\\\") as f:\\n f.writelines([str(self.initial_amount)+'\\\\n', str(self.spent)+'\\\\n', \\\" \\\".join(self.allocs)+'\\\\n'])\\n f.write(\\\" \\\".join([str(k) for k in self.alloc_amounts]) + \\\"\\\\n\\\")\\n f.write(\\\" \\\".join([str(k) for k in self.alloc_spent]) + \\\"\\\\n\\\")\\n f.writelines(self.expenditures)\\n self.report()\",\n \"def cleaning_file():\\n f = open (\\\"report_for_judy_part2.txt\\\", \\\"w\\\")\\n f.close()\",\n \"def saveToFile(html):\\n #print(\\\"Saving to file.\\\")\\n html += \\\"\\\\n\\\"\\n #open necessary files to save\\n logFile = open(\\\"postLog_{0}_{1}.txt\\\".format(os.path.splitext(path)[0], dateTimeNow), \\\"a\\\")\\n logFile.write(html)\\n logFile.close()\\n #print(\\\"Check Point.\\\")\",\n \"def cmd_wq(self):\\n out_file = open(\\\"DoubleLinkedList.txt\\\", \\\"w\\\")\\n out_file.write(str(self.get_text()))\\n out_file.close()\\n print(\\\"File saved to DoubleLinkedList.txt\\\")\",\n \"def WriteErrorsToFile():\\n if(not __errorsTracked__ is None):\\n if(len(__errorsTracked__)>0):\\n formattedLogName = '_'.join[\\\"ErrorLog\\\",\\\"GarageChecker\\\",datetime.date,datetime.time]\\n WriteToFile(formattedLogName,__errorsTracked__)\\n __errorsTracked__ = []\",\n \"def write_to_txt(self):\\r\\n file = open(self.output_path, 'w')\\r\\n for question_id in self.question_ids:\\r\\n file.write(self.questions[question_id].question_string+str(self.questions[question_id].answer)+'\\\\n')\\r\\n file.close()\",\n \"def saveUsage(self, filePath):\\n message = time.strftime('%c') + ' : '\\n for spot in self.getParkingSpots():\\n message += str(spot.id) + ', ' + spot.status + '; '\\n with open(filePath, 'a+') as outfile:\\n outfile.write(message + '\\\\n')\\n pass\",\n \"def _write_data_out(solutions, unable_to_resolve, unresolvables):\\n print('')\\n print('------------------------')\\n print('--- Progress So Far: ---')\\n print('Solved: ' + str(len(solutions)))\\n print('Error while resolving: ' + str(len(unable_to_resolve)))\\n print('Unresolvable conflicts: ' + str(len(unresolvables)))\\n print('Saving progress to json.')\\n print('------------------------')\\n print('')\\n json.dump(solutions, open(fname_solutions, 'w'))\\n json.dump(unable_to_resolve, open(fname_errors, 'w'))\\n json.dump(unresolvables, open(fname_unresolvables, 'w'))\",\n \"def writingtxt(pathf,ticker,nb,typ):\\n\\n assert len(pathf) == nb, 'the number of issues does not match' # an assert to check that the number of paths parsed is equal nb\\n \\n \\n filename = [typ+ticker.lower()+str(2020-i)+'.txt' for i in reversed(range(0,nb))] # loop to create list of filenames\\n \\n for p,n in zip(pathf,filename): # looping through files and filesname\\n\\n raw_html = open(p,encoding = 'utf-8') # opening the html given the path\\n \\n soup = bsoup(raw_html, 'lxml')# decoding the html\\n\\n ltables = soup.find_all('table') # locating the tables\\n \\n for z in ltables: # removing the table\\n z.decompose()\\n \\n bla = open(n,\\\"w+\\\", encoding = 'utf-8') #opening a new .txt file with filename\\n bla.write(soup.text) # writing the text of the report in the .txt file\\n \\n\\n return None\",\n \"def notest_file(text):\\n if debug == 2:\\n print(text)\\n with open(\\\"info_file.txt\\\", \\\"a\\\", encoding=\\\"utf-8\\\", ) as f:\\n f.write(text + \\\"\\\\n\\\")\\n elif debug == 1:\\n with open(\\\"info_file.txt\\\", \\\"a\\\", encoding=\\\"utf-8\\\", ) as f:\\n f.write(text + \\\"\\\\n\\\")\",\n \"def test_write_race_results_to_file():\\n number = random.randint(1, 3)\\n f1.write_race_results_to_file(number)\\n with open(f\\\"results_for_race_{number}.txt\\\", encoding=\\\"utf-8\\\") as opened_file:\\n list_of_lines = opened_file.readlines()\\n assert len(list_of_lines) == 13\",\n \"def save(self,f,open_and_close=False):\\n\\n if open_and_close:\\n f = file(file_name,'w')\\n for k in self.__dict__.keys():\\n if k[0]!='_': #Exclude 'private' variables ('_dont_touch')\\n f.write('# %s : %s \\\\n'%(k,self.__dict__[k]))\\n f.close()\\n\\n else:\\n for k in self.__dict__.keys():\\n if k[0]!='_': #Exclude 'private' variables ('_dont_touch')\\n f.write('# %s : %s \\\\n'%(k,self.__dict__[k]))\\n return f\",\n \"def save(self,f,open_and_close=False):\\n\\n if open_and_close:\\n f = file(file_name,'w')\\n for k in self.__dict__.keys():\\n if k[0]!='_': #Exclude 'private' variables ('_dont_touch')\\n f.write('# %s : %s \\\\n'%(k,self.__dict__[k]))\\n f.close()\\n\\n else:\\n for k in self.__dict__.keys():\\n if k[0]!='_': #Exclude 'private' variables ('_dont_touch')\\n f.write('# %s : %s \\\\n'%(k,self.__dict__[k]))\\n return f\",\n \"def save(self, fn: str) -> None:\\n fout = open(fn, 'w')\\n for t,x in zip(self.times,self.milestones):\\n fout.write('%f\\\\t%d '%(t,len(x)))\\n fout.write(' '.join([str(xi) for xi in x]))\\n fout.write('\\\\n')\\n fout.close()\",\n \"def export(self):\\n memento = self.create_memento()\\n try:\\n f = open(\\\"story.txt\\\", \\\"w\\\")\\n try:\\n f.write(memento.__str__())\\n finally:\\n f.close()\\n except IOError:\\n print 'IOError while exporting story!'\",\n \"def save(self):\\n \\n fileName=self.characterName+\\\"_\\\"+self.race+\\\"_\\\"+self.classType+\\\"_lvl_\\\"+str(self.level)\\n new_file = open(str(fileName)+\\\".txt\\\",\\\"w\\\")\\n new_file.write(\\\"~~~~~~~~~~~ \\\"+self.characterName+\\\" the \\\"+self.race+\\\" \\\"+self.classType+\\\" ~~~~~~~~~~~\\\\n\\\\n\\\")\\n new_file.write(\\\"Level: \\\"+str(self.level)+\\\" HP: \\\"+str(self.hp)+\\\" XP: \\\"+str(self.xp)+\\\" Hit Dice: \\\"+str(self.level)+str(self.hit_dice[self.classType])+\\\"\\\\n\\\")\\n new_file.write(str(self.abilityScores()))\\n new_file.write(\\\"\\\\n\\\\n~~~~~~~~~ Skills ~~~~~~~~~\\\\n\\\")\\n for i in self.skills:\\n new_file.write(\\\"\\\\n\\\"+i+\\\" \\\"+\\\"(\\\"+skills[i.lower()].upper()+\\\")\\\")\\n new_file.write(\\\"\\\\n\\\\n~~~~~~~~~ Traits ~~~~~~~~~\\\\n\\\")\\n for i in self.traits:\\n new_file.write(\\\"\\\\n ~~\\\"+i+\\\"~~\\\\n \\\"+str(self.traits[i])+\\\"\\\\n\\\")\\n new_file.write(\\\"\\\\n\\\\n~~~~~~~~~ Specialty: \\\"+self.specialty+\\\" ~~~~~~~~\\\\n\\\")\\n new_file.write(\\\"\\\\n \\\"+self.specialtyStory+\\\"\\\\n\\\")\\n new_file.write(\\\"\\\\n ~~~~ Feats ~~~~\\\\n\\\")\\n for i in range(1,self.level+1):\\n if i == 1 or i%3 == 0:\\n new_file.write(\\\"\\\\n Level \\\"+str(i)+\\\": \\\"+self.feats[i]['name']+' '\\\\\\n \\\"(\\\"+self.feats[i]['type']+\\\")\\\\n\\\"\\\\\\n ' \\\"'+self.feats[i]['description']+'\\\"\\\\n\\\\n')\\n if 'prereq' in self.feats[i]:\\n new_file.write(\\\" Prerequisite: \\\"+self.feats[i]['prereq']+\\\"\\\\n\\\")\\n if 'benefit' in self.feats[i]:\\n new_file.write(\\\" Benefit: \\\"+self.feats[i]['benefit']+\\\"\\\\n\\\")\\n if 'effect' in self.feats[i]:\\n new_file(\\\" Effect: \\\"+self.feats[i]['effect']+\\\"\\\\n\\\")\\n \\n new_file.write(\\\"\\\\n\\\\n~~~~~~~~~ Background: \\\"+self.background+\\\" ~~~~~~~~\\\\n\\\")\\n if self.backgroundProfession == '':\\n pass\\n else:\\n new_file.write(\\\"Profession: \\\"+self.backgroundProfession)\\n new_file.write(\\\"\\\\n \\\"+self.backgroundStory)\\n \\n new_file.close()\\n print \\\"File \\\"+str(fileName)+\\\".txt saved.\\\"\",\n \"def write_to_file(self, filepath, mode = \\\"a\\\"): \\n if \\\"r\\\" in mode: \\n print(\\\"Only accepts write and append modes\\\")\\n return \\n with open(filepath, mode) as f: \\n f.write(\\\"{}\\\\n\\\".format(self.title))\\n verified, seen, ratio = self.get_verified_ratio()\\n f.write(\\\"Verified Names: {}\\\\n\\\".format(str(verified)))\\n f.write(\\\"Names: {}\\\\n\\\".format(str(seen)))\\n f.write(\\\"Ratio: {}\\\\n\\\".format(str(ratio)))\",\n \"def saveLogFile(self, fname = \\\"data/status.txt\\\"):\\n with open(fname, 'w') as f:\\n f.write(\\\"<br>\\\\n\\\".join(self.logLines))\\n self.log(\\\"wrote \\\"+fname)\",\n \"def _toFile(self):\\n pass\",\n \"def exercise2(self):\\n my_file = open('my_file.txt', 'w+')\\n my_file.write(\\\"Look Ma! I'm a file\\\")\\n my_file.close()\\n content = my_file.read()\\n print content\",\n \"def save_to_file(self) -> None:\\n abs_errors = 0\\n art_errors = 0\\n for i, doc in enumerate(tqdm(self.results[\\\"documents\\\"])):\\n paper, abstract = self.__get_article_and_abstract(doc[\\\"eid\\\"])\\n if abstract:\\n abstract_file = open(self.out_dir + \\\"[{}] Abstract_{}.txt\\\".format(doc[\\\"date\\\"], doc[\\\"title\\\"]), \\\"w\\\")\\n abstract_file.write(abstract)\\n abstract_file.close()\\n else:\\n abs_errors += 1\\n if paper:\\n file = open(self.out_dir + \\\"[{}] {}.txt\\\".format(doc[\\\"date\\\"], doc[\\\"title\\\"]), \\\"w\\\")\\n file.write(paper)\\n file.close()\\n else:\\n art_errors += 1\\n print(\\\"Not saved/found: {} abstracts, {} articles\\\".format(abs_errors, art_errors))\",\n \"def do_the_issues(user_id, repo_id):\\n with tempfile.TemporaryDirectory() as tmp:\\n path = os.path.join(tmp, \\\"{}_{}_issues.txt\\\".format(repo_id, user_id))\\n issues_initial_url = get_initial_url_issues(user_id, repo_id)\\n resp_obj = requests.get(issues_initial_url, headers=headers)\\n # prase the initial request. for Issue\\n all_issues = json.loads(resp_obj.text)\\n with open(path, \\\"w\\\") as out_stream:\\n for an_issue in all_issues:\\n print(an_issue, file=out_stream)\\n print(\\\"the len of resp is {}\\\".format(len(all_issues)))\\n LINK_HEADER = \\\"Link\\\"\\n next_url = None\\n if LINK_HEADER in resp_obj.headers:\\n # parse next page (if present)\\n next_url = parse_next_url(resp_obj.headers[LINK_HEADER])\\n # subsequent page\\n while next_url:\\n resp_obj = requests.get(next_url, headers=headers)\\n all_issues = json.loads(resp_obj.text)\\n with open(path, \\\"a\\\") as out_stream:\\n for an_issue in all_issues:\\n print(an_issue, file=out_stream)\\n if LINK_HEADER in resp_obj.headers:\\n next_url = parse_next_url(resp_obj.headers[LINK_HEADER])\\n print(next_url)\\n else:\\n next_url = None\\n GsUpload.upload_blob(GS_BUCKET_NAME, path, basename(path))\\n print(\\\"the issues path is \\\" + str(path))\",\n \"def save(self):\\n if PYTHON3:\\n fileobj = open(self.filename, 'w', encoding=self.ENCODING, errors=\\\"replace\\\")\\n else:\\n fileobj = open(self.filename, 'w')\\n self.save_to_fileobj(fileobj)\\n fileobj.close()\",\n \"def writing_get_game(file_name, title):\\n result = str(reports.get_game(file_name, title))\\n with open (\\\"report_for_judy_part2.txt\\\", \\\"+a\\\") as f:\\n f.write(result)\\n f.write(\\\"\\\\n\\\")\",\n \"def write_to_file(info: List[str]) -> None:\\n return\",\n \"def save2txt(obj, file: str):\\n with open(file, \\\"w\\\") as f:\\n print(obj, file=f)\",\n \"def saveProblem(num, name, content, url):\\n # Only include first 5 words of name\\n try:\\n filename = name.split()[:4]\\n except IndexError:\\n filename = name.split()\\n filename = '_'.join(filename)\\n\\n # Exclude special characters from filenames\\n for x in ('\\\\\\\\', '/', '?', '\\\"', '\\\\'', '...', ',',\\n '..', '$', '<', '>', '|', ':', ';'):\\n filename = filename.replace(x, '')\\n # Add number to start\\n filename = f'{num!s:0>3.3}-{filename}'\\n\\n # Paths for the md file and solution file\\n problemPath = OUTPUT_FOLDER / (filename + '.md')\\n scriptPath = OUTPUT_FOLDER / f'../solutions/{filename}.py'\\n exist = '' if scriptPath.exists() else ' - not been created.'\\n\\n # Create markdown text and write to file\\n text = (f\\\"# Problem {num} - {name}\\\"\\n f\\\"\\\\n\\\\n[Problem Page]({url})\\\"\\n f\\\"\\\\n\\\\n[Solution Script]({scriptPath.relative_to(OUTPUT_FOLDER)}){exist}\\\"\\n f\\\"\\\\n\\\\n{content}\\\")\\n with open(problemPath, 'wb') as f:\\n f.write(text.encode('utf8'))\\n\\n return\",\n \"def save_file(self):\\n f = open(self._file_name, \\\"w\\\")\\n try:\\n for rental in self.list:\\n rental_str = self.obj_to_string(rental)\\n f.write(rental_str)\\n f.close()\\n except Exception as e:\\n raise e\",\n \"def save_reason(reason):\\n filename = 'tmp/reason.txt'\\n\\n with open(filename, 'a') as file_object:\\n file_object.write(reason + '\\\\n')\",\n \"def write_summary(self, text):\\n summary = Path(self.path, self.folder + \\\"_summary.txt\\\")\\n with open(summary, \\\"a\\\") as summary_file:\\n summary_file.write(f\\\"{text}\\\\n\\\")\\n shutil.copyfile(summary, summary.parent.parent / summary.name)\",\n \"def write(self, path):\\n\\n with open(path, 'w') as file:\\n file.write(f\\\"\\\\n{self.subject}\\\")\\n file.write(8 * '\\\\n')\\n file.write(\\\"0\\\")\\n file.write(8 * '\\\\n')\\n file.write(self.data.date[0].strftime(\\\"%d.%m.%Y\\\"))\\n file.write(7 * '\\\\n')\\n file.write(\\\"Unknown Line\\\")\\n file.write(26 * '\\\\n')\\n file.write(self.valid_measurements + \\\"\\\\n\\\")\\n printing_df = self.data.drop(columns=['date', 'time'])\\n printing_df['hours'] = self.data.time.map(lambda x: x.strftime(\\\"%H\\\"))\\n printing_df['minutes'] = self.data.time.map(lambda x: x.strftime(\\\"%M\\\"))\\n order = ['hours', 'minutes', 'SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']\\n printing_df = printing_df[order]\\n printing_df.fillna(-9999, inplace=True)\\n printing_df.replace('EB', -9998, inplace=True)\\n printing_df.replace('AB', -9997, inplace=True)\\n printing_df[['SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']] = printing_df[\\n ['SYS(mmHg)', 'DIA(mmHg)', 'UNKNOW_1', 'UNKNOW_2', 'CODE', 'UNKNOW_3']].astype(int).astype(str)\\n printing_df.replace('-9999', '\\\"\\\"', inplace=True)\\n printing_df.replace('-9998', '\\\"EB\\\"', inplace=True)\\n printing_df.replace('-9997', '\\\"AB\\\"', inplace=True)\\n printing_df.to_csv(file, header=None, index=None, quoting=csv.QUOTE_NONE, line_terminator='\\\\n')\\n\\n xml_node = ET.Element('XML')\\n xml_node.extend(self._dict_to_etree(self.metadata))\\n xml_line = ET.tostring(xml_node, encoding=\\\"unicode\\\")\\n file.write(xml_line)\",\n \"def to_file(self, f: str) -> None:\\n with open(f, \\\"w\\\") as open_file:\\n open_file.write(\\\"\\\\n\\\".join(self.itos) + \\\"\\\\n\\\")\",\n \"def test_writing(self):\\n with contextlib.closing(RiggedDailyLogFile(self.name, self.dir)) as log:\\n log.write(\\\"123\\\")\\n log.write(\\\"456\\\")\\n log.flush()\\n log.write(\\\"7890\\\")\\n\\n with open(self.path) as f:\\n self.assertEqual(f.read(), \\\"1234567890\\\")\",\n \"def save_to_file(name, elements, status, current_section):\\n # status 3 = dealing with about section\\n # status 4 = dealing with posts\\n\\n try:\\n # dealing with About Section\\n if status == 3:\\n workEducation = elements[0].text.replace(\\\"\\\\n\\\", \\\"|\\\")\\n influencerObject[\\\"Influencer\\\"][\\\"Description\\\"][\\\"Text\\\"] = workEducation\\n\\n # dealing with Posts\\n # elif status == 4:\\n # extract_and_write_posts(elements, name)\\n # return\\n\\n except:\\n print(\\\"Exception (save_to_file)\\\", \\\"Status =\\\",\\n str(status), sys.exc_info()[0])\\n\\n return\",\n \"def test_writing(self):\\n with contextlib.closing(logfile.LogFile(self.name, self.dir)) as log:\\n log.write(\\\"123\\\")\\n log.write(\\\"456\\\")\\n log.flush()\\n log.write(\\\"7890\\\")\\n\\n with open(self.path) as f:\\n self.assertEqual(f.read(), \\\"1234567890\\\")\",\n \"def save_problem(problem, filepath):\\n with open(filepath, \\\"w\\\") as out:\\n print \\\"Vectors count: %s\\\" % (len(problem))\\n for vector in problem:\\n out.write(\\\"%s \\\" % (vector[0]))\\n for index, value in sorted(vector[1].iteritems()):\\n out.write(\\\"%s:%s \\\" % (index, value))\\n out.write(\\\"\\\\n\\\")\",\n \"def save_file():\\n generic = pull_list()\\n result = list()\\n i = 0\\n while True:\\n try:\\n if generic[i].startswith('CVE'):\\n cve_pattern = \\\"^CVE-\\\\d+-\\\\d+|^CVE-\\\\d+-[X]+\\\"\\n header = re.findall(cve_pattern, generic[i])[0]\\n i += 1\\n notes = list()\\n while not generic[i].startswith('CVE'):\\n commit_pattern = \\\"http[s]?:\\\\/\\\\/.+commit\\\\/[\\\\S]+\\\"\\n if re.search(commit_pattern, generic[i]):\\n link = re.findall(commit_pattern, generic[i])\\n notes.append(link[0])\\n i += 1\\n if notes != list():\\n result.append(Data(header, notes))\\n except IndexError:\\n print('Finished')\\n break\\n return result\",\n \"def test_write_championship_to_file():\\n f1.write_championship_to_file()\\n with open(\\\"championship_results.txt\\\") as new_file:\\n list_of_lines = new_file.readlines()\\n assert len(list_of_lines) == 13\",\n \"def save_todo_file(self):\\n\\n if os.path.exists('TODO.txt'):\\n os.remove('TODO.txt')\\n todo_fp = open('TODO.txt', 'w')\\n todo_items = self.todo_scroll_cell.get_item_list()\\n in_progress_items = self.in_progress_scroll_cell.get_item_list()\\n done_items = self.done_scroll_cell.get_item_list()\\n for item in todo_items:\\n todo_fp.write(item + '\\\\n')\\n todo_fp.write('__IN_PROGRESS__' + '\\\\n')\\n for item in in_progress_items:\\n todo_fp.write(item + '\\\\n')\\n todo_fp.write('__DONE__' + '\\\\n')\\n for item in done_items:\\n todo_fp.write(item + '\\\\n')\\n todo_fp.close()\\n self.master.show_message_popup('Saved', 'Your TODO list has been saved!')\",\n \"def print_to_file(start, stop, time_worked, work_text, work_log):\\n today = datetime.date.today()\\n\\n record = ' || %.2f || %.2f || %.4f hours || %s\\\\n' % (start, stop, time_worked/3600, work_text)\\n\\n #if it is a new file you have the option to set a start time for the project\\n # and how many hours a week you want to work\\n if not os.path.isfile(work_log):\\n while True:\\n option = raw_input('\\\\nThis is a new log, would you like to specify a start date and a hours per week goal for the project? (y/n): ').lower()\\n if option == 'y':\\n date = raw_input('\\\\nplease enter the start date of the project (dd-mm-yyyy): ')\\n hours_per_week = raw_input('\\\\nplease enter the number of hours you intend to work on the project per week: ')\\n try:\\n datetime.datetime.strptime(date, '%d-%m-%Y')\\n if hours_per_week.isdigit():\\n f = open(work_log, 'a')\\n f.write('#! || ' + date + ':' + hours_per_week + '\\\\n')\\n f.close()\\n break\\n else:\\n print \\\"\\\\nPlease enter a valid number for hours to work!\\\\n\\\"\\n except ValueError:\\n print \\\"\\\\nPlease enter a valid date!\\\\n\\\"\\n\\n else:\\n break\\n\\n\\n f = open(work_log, 'a')\\n print '\\\\n\\\\n' + today.strftime('%b-%d-%Y') + record\\n f.write(today.strftime('%b-%d-%Y') + record)\\n f.close()\",\n \"def save_txt():\\n # open file and append, if it doesn't exist then create it.\\n with open('journal_entries.txt', 'a+') as f:\\n # .get the input in text widget at the first line, '0th' character, then read until the end\\n f.write(\\\"\\\\n\\\" + get_date_time())\\n for i in range(len(entries)):\\n string = entries[i].get('1.0', 'end-1c')\\n if string:\\n f.write(\\\"\\\\n\\\" + string)\",\n \"def save_last_error_report(trace):\\n try:\\n rfile = os.path.join(get_file_dir(), 'last_report_error.txt')\\n with open(rfile, 'w') as f:\\n f.write(trace)\\n except:\\n log(\\\"Error writing error report file\\\")\",\n \"def store_story(self):\\n\\n file_path = self.dir + '/' + self.filename + '.txt'\\n file_path = self.check_path(file_path)\\n with open(file_path, 'a') as f_out:\\n f_out.write('Name: ' + self.name + '\\\\n')\\n f_out.write('Age: ' + self.age + '\\\\n')\\n f_out.write('Country of origin: ' + self.origin + '\\\\n')\\n f_out.write('Company: ' + self.company + '\\\\n')\\n f_out.write('First country of arrival: ' + self.route)\\n if self.route.lower() != 'the netherlands':\\n f_out.write(' >>> DUBLIN PROCEDURE\\\\n')\\n else:\\n f_out.write('\\\\n')\\n f_out.write('Entrance: ' + self.entrance + '\\\\n')\\n f_out.write('Documentation: ' + self.documentation + '\\\\n')\\n f_out.write('Exclusion: ' + self.exclusion + '\\\\n')\\n f_out.write('Conflict: ' + self.conflict + '\\\\n')\\n f_out.write('Inhumanity: ' + self.inhumanity + '\\\\n')\\n f_out.write('Family with a permit: ' + self.family + '\\\\n')\\n return\",\n \"def save_to_file(self):\\n # Create a new file name based off date and time\\n file_name = datetime.datetime.now().strftime(\\\"%Y%m%d%H%M%S_RTI_CFG.txt\\\")\\n file_path = os.path.expanduser(\\\"~\\\\\\\\Desktop\\\\\\\\\\\"+file_name)\\n\\n file = open(file_path, 'w')\\n file.write(self.commandFileTextBrowser.toPlainText())\\n file.close()\\n\\n self.parent.statusBar().showMessage('File saved to ' + file_path)\",\n \"def _reportErrorMsg(self, ErrorMessage, outputFile):\\n f=self.openFile(outputFile, \\\"a\\\") #open otuputFile for appending\\n self._insertErrorMsg(ErrorMessage, f)\\n f.close()\",\n \"def write_to_files():\\n\\t# Create output files\\n\\toutput = [None, \\\\\\n\\t\\t open(\\\"priority-1.txt\\\", \\\"w\\\"), \\\\\\n\\t\\t open(\\\"priority-2.txt\\\", \\\"w\\\"), \\\\\\n\\t\\t open(\\\"priority-3.txt\\\", \\\"w\\\"), \\\\\\n\\t\\t open(\\\"priority-4.txt\\\", \\\"w\\\"), \\\\\\n\\t\\t open(\\\"priority-5.txt\\\", \\\"w\\\"), ]\\n\\n\\t# Loop over all fields and write them to the correct file\\n\\tfor field in sorted(reportlog.keys()):\\n\\t\\tpriority = reportlog[field]['priority']\\n\\t\\tlabel = reportlog[field]['label']\\n\\n\\t\\toutput[priority].write(\\\"intphas_%s\\\\t%s\\\\n\\\" % (field, label))\\n\\t\\toutput[priority].flush()\\n\\n\\t# Close files\\n\\tfor i in [1,2,3,4,5]:\\n\\t\\toutput[i].close()\",\n \"def save_failed_tests_info():\\n global g_failed_tests_info_dict\\n if len(g_failed_testnames) > 0: # found failed test\\n if os.path.isfile(g_failed_tests_dict) and os.path.getsize(g_failed_tests_dict) > 10:\\n try:\\n g_failed_tests_info_dict=json.load(open(g_failed_tests_dict, 'r'))\\n except:\\n init_failed_tests_dict()\\n\\n # with open(g_failed_tests_dict, 'rb') as dict_file:\\n # g_failed_tests_info_dict = pickle.load(dict_file)\\n else: # file not found, create new dict\\n init_failed_tests_dict()\\n\\n with open(g_summary_text_filename, 'a') as failed_file:\\n with open(g_daily_failure_csv, 'w') as daily_failure:\\n for index in range(len(g_failed_testnames)):\\n\\n testInfo = ','.join([g_timestring, g_job_name, str(g_build_id), g_git_hash, g_node_name,\\n g_unit_test_type, g_failed_testnames[index]])\\n failed_file.write(testInfo+'\\\\n')\\n daily_failure.write(testInfo+'\\\\n')\\n # update failed tests dictionary\\n update_failed_test_info_dict(g_failed_testnames[index], g_failed_test_paths[index])\\n json.dump(g_failed_tests_info_dict, open(g_failed_tests_dict, 'w'))\",\n \"def count_corrects_tissue(input_file, output_file):\\n\\ttissues_dict = {}\\n\\twith open(input_file) as fl:\\n\\t\\tfl.readline()\\n\\t\\tfor line in fl:\\n\\t\\t\\tline = line.rstrip()\\n\\t\\t\\tdata = line.split('\\\\t')\\n\\t\\t\\tif not data[2] in tissues_dict:\\n\\t\\t\\t\\ttissues_dict[data[2]] = {'correct':0,'incorrect':0}\\n\\t\\t\\tif data[2] == data[1]:\\n\\t\\t\\t\\ttissues_dict[data[2]]['correct'] += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttissues_dict[data[2]]['incorrect'] += 1\\n\\n\\twith open(output_file,'w') as out:\\n\\t\\tfirst = True\\n\\t\\ttissue_order = []\\n\\t\\t#print tissue\\n\\t\\tfor tissue in tissues_dict:\\n\\t\\t\\tif first:\\n\\t\\t\\t\\ttissue_order.append(tissue)\\n\\t\\t\\t\\tout.write(tissue)\\n\\t\\t\\t\\tfirst = False\\n\\t\\t\\telse:\\n\\t\\t\\t\\tout.write(\\\"\\\\t%s\\\" % tissue)\\n\\t\\t\\t\\ttissue_order.append(tissue)\\n\\t\\tout.write(\\\"\\\\n\\\")\\n\\t\\tfirst = True\\n\\t\\t#prints corrects\\n\\t\\tfor tissue in tissue_order:\\n\\t\\t\\tif first:\\n\\t\\t\\t\\tout.write(str(tissues_dict[tissue]['correct']))\\n\\t\\t\\t\\tfirst = False\\n\\t\\t\\telse:\\n\\t\\t\\t\\tout.write(\\\"\\\\t%s\\\" % str(tissues_dict[tissue]['correct']))\\n\\n\\t\\tout.write(\\\"\\\\n\\\")\\n\\t\\tfirst = True\\n\\t\\t#prints incorrects\\n\\t\\tfor tissue in tissue_order:\\n\\t\\t\\tif first:\\n\\t\\t\\t\\tout.write(str(tissues_dict[tissue]['incorrect']))\\n\\t\\t\\t\\tfirst = False\\n\\t\\t\\telse:\\n\\t\\t\\t\\tout.write(\\\"\\\\t%s\\\" % str(tissues_dict[tissue]['incorrect']))\",\n \"def to_file(self, file_path, smirnoff_data):\\n pass\",\n \"def save(*messages):\\n data = Parser.parse_texts(*messages[1:])\\n hour = time.strftime(\\\"_%H_%M_%S\\\")\\n today = time.strftime(\\\"_%d_%m_%Y\\\")\\n title = Parser.parse_text(messages[0])\\n\\n file = open(\\\"./logs/\\\"+threading.currentThread().getName()+today+\\\".log\\\",'a+')\\n file.write(\\\"\\\\n==\\\"+title+hour+\\\"==\\\\n\\\")\\n if type(data) is dict: #Dictionary with each value being a triplet. From get_all_items\\n for key in data.keys():\\n file.write(Parser.parse_text(key) + \\\" -> \\\"+ Parser.parse_text(str(data[key].x)) +\\\"\\\\n\\\")\\n elif type(data) is list: #From get_item, market item, attribute listings\\n for listing in data:\\n file.write(str(listing.id)+\\\" - \\\"+str(listing.price/100)+\\\" euros\\\\n\\\")\\n else: #plain text\\n file.write(Parser.parse_text(data))\\n file.write(\\\"=====================================\\\\n\\\")\\n file.close()\",\n \"def __save_article_to_file(self, content):\\n with open(\\\"article.txt\\\", 'w') as out:\\n out.write(content)\",\n \"def save_review_to_file(lines, filename):\\n data = '\\\\n'.join(lines)\\n file = open(filename, 'w')\\n file.write(data)\\n file.close()\",\n \"def write_error_report(self):\\n\\n with open('runReport.txt', 'a') as report:\\n report.write(\\\"Number of Hits: \\\" + str(self.num_hits) + '\\\\n')\\n report.write(\\\"Number of Requests: \\\" + str(self.num_requests) + '\\\\n')\\n report.write(\\\"Hit Rate: \\\" + str((self.num_hits / self.num_requests)))\\n report.write(\\\"Datafiles downloaded: \\\" + str(self.num_datafiles))\\n now = datetime.now()\\n dt_string = now.strftime(\\\"%H:%M %m/%d/%Y\\\")\\n report.write(\\\"Run finished \\\" + dt_string)\",\n \"def write_to_file(self):\\n\\t\\tfile = open(\\\"states.txt\\\", \\\"w\\\")\\n\\t\\t\\n\\t\\tpointer = self.head\\n\\t\\twhile pointer != None:\\n\\t\\t\\tfile.write(pointer.state + \\\"\\\\t\\\" + pointer.info)\\t\\n\\t\\t\\tpointer = pointer.next\\n\\n\\t\\tfile.close()\",\n \"def os_open_comm_log( self, ):\\r\\n AppGlobal.os_open_txt_file( self.parameters.comm_logging_fn )\",\n \"def save_txt_file():\\n global output_on_display\\n if data_base == '':\\n mistake_load_table()\\n else:\\n save_name = asksaveasfilename(title=\\\"Select file\\\", filetypes=((\\\"TXT\\\", \\\"*.txt\\\"), (\\\"all files\\\", \\\"*.*\\\")),\\n defaultextension='.txt')\\n if Path(save_name).suffix == '.txt':\\n data_txt = output_on_display.get('1.0', 'end')\\n f = open(save_name, 'w')\\n f.write(data_txt)\\n f.close()\",\n \"def write_to_file(self, filename: str) -> None:\",\n \"def report(db, openfile):\\n pass\",\n \"def save(self):\\n\\n # We need to set updated, even if it's the same as created,\\n # so we have a consistent timestamp to sort issues by.\\n self.updated = time.time()\\n\\n if not hasattr(self, 'id'):\\n # IDs are generated from the JSON dump of the\\n # issue. This includes the UTC-format timestamp, so \\n # they can be considered pretty unique.\\n self.created = self.updated \\n self.id = get_hash(to_json(self.fields))\\n # set the paths now that we have an id\\n self._set_paths()\\n \\n # Make the parent directory if it doesn't exist.\\n if not os.path.isdir(self.paths['root']):\\n os.mkdir(self.paths['root'])\\n # Make the comments dir if it doesn't exist.\\n if not os.path.isdir(self.paths['comments']):\\n os.mkdir(self.paths['comments'])\\n # Save it in the db.\\n self.tracker.db.insert(self)\\n # Save it.\\n return self.to_file(self.paths['issue'])\",\n \"def write_txt_file(title, abstract, f_out):\\n\\n print(\\n '*' * 40,\\n '\\\\n',\\n '[Title] {}'.format(title),\\n '\\\\n',\\n '[Abstract] {}'.format(abstract),\\n file=f_out\\n )\",\n \"def write_to_file(content, filename):\\n if not os.path.isfile(filename): # Checking if file already exists, don't append data if it does.\\n for j in range(len(content)): # For each dialog in dialogues array.\\n with open(filename, 'a') as file: # Open a text file in append mode and write data into it.\\n for k in range(len(content[j][0])):\\n file.write('{0} {1}\\\\n'.format(str(content[j][0][k]).lower().split(\\\"(\\\")[0],\\n str(content[j][1][k])).lower())\",\n \"def reset_used():\\n with open(LOG_FILEPATH, 'w+') as logfile:\\n pass\",\n \"def save(self):\\n filename = os.path.expanduser(\\\"~/\\\" + self.name)\\n print(filename)\\n np.savetxt(filename + \\\"_left.txt\\\", self.central)\\n np.savetxt(filename + \\\"_right.txt\\\", self.boundaries)\",\n \"def save_solution(self, solution_path, file_path):\\n node_info = \\\"\\\"\\n with open(file_path, \\\"w\\\") as file:\\n file.write(\\n \\\"Strategy: \\\"\\n + str(self.strategy)\\n + \\\"\\\\n Max Depth: \\\"\\n + str(self.max_depth)\\n + \\\"\\\\n Depth Increment: \\\"\\n + str(self.depth_increment)\\n + \\\"\\\\n Pruning: \\\"\\n + str(self.pruning)\\n + \\\"\\\\n ---SOLUTION---: \\\"\\n )\\n for node in solution_path:\\n node_info = \\\"\\\\n\\\\n ID: \\\" + str(node.id)\\n if node.last_action != None:\\n node_info += \\\"\\\\n Action: \\\" + str(node.last_action)\\n node_info += (\\n \\\"\\\\n Cost: \\\"\\n + str(node.cost)\\n + \\\"\\\\n Depth: \\\"\\n + str(node.node_depth)\\n + \\\"\\\\n Heuristic: \\\"\\n + str(node.state.entropy())\\n + \\\"\\\\n F value: \\\"\\n + str(node.f)\\n + \\\"\\\\n Node: \\\"\\n + str(node.state.create_md5())\\n )\\n file.write(node_info)\\n node_info = \\\"\\\"\\n\\n file.write(\\n \\\"\\\\n TOTAL COST: \\\" + str(solution_path[len(solution_path) - 1].cost)\\n )\",\n \"def write_file(self):\\n if self._write_file == None:\\n return\\n\\n try:\\n out = file(self._write_file, \\\"w\\\")\\n except IOError, e:\\n print e\\n sys.exit(1)\\n out.writelines(\\\"A cases\\\") \\n out.close()\",\n \"def save_bugs(self, year, chunk, bugs, errors=None):\\n directory = self.get_bugs_path(year)\\n return self._save(directory, chunk, bugs, errors, switch='bugs')\",\n \"def write_puzzle(to_file: str, solution: str):\\n with open(to_file, \\\"w\\\") as file:\\n file.write(solution)\",\n \"def put_in_file(txt,fd,defined_symbols):\\n\\n\\tif len(txt) > 0: fd.write(txt+'\\\\n')\\n\\treferenceKeys = sorted(defined_symbols.iterkeys())\\n\\tfor i in referenceKeys:\\n\\t\\tdict_of_files = defined_symbols[i]\\t\\t\\t\\t\\t\\t# dict of files:list of tuples\\n\\t\\ttry:\\n\\t\\t\\tfd.write('name='+i+'\\\\n')\\n\\t\\t\\tif len(dict_of_files) > 0:\\n\\t\\t\\t\\tlist_of_files = sorted(dict_of_files.iterkeys()) \\t# list of tuples (file , list of tuples)\\n\\t\\t\\t\\tfor\\tj in list_of_files:\\n\\t\\t\\t\\t\\tfd.write('file='+j+'\\\\n') \\n\\t\\t\\t\\t\\tfor k in dict_of_files[j]:\\n\\t\\t\\t\\t\\t\\tif k[1] == DEFINED:\\n\\t\\t\\t\\t\\t\\t\\ttp = \\\"defin\\\"\\n\\t\\t\\t\\t\\t\\telif k[1] == USED:\\n\\t\\t\\t\\t\\t\\t\\ttp = \\\"refer\\\"\\n\\t\\t\\t\\t\\t\\telif k[1] == MACRO:\\n\\t\\t\\t\\t\\t\\t\\ttp = \\\"macro\\\"\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\ttp = \\\"both \\\"\\n\\t\\t\\t\\t\\t\\ts = str(k[0])\\n\\t\\t\\t\\t\\t\\tfor p in k[2]:\\n\\t\\t\\t\\t\\t\\t\\ts += ':'+str(p)\\n\\t\\t\\t\\t\\t\\tfd.write(' line='+s+' type='+tp)\\t# line ond type\\n\\t\\t\\t\\t\\tfd.write('\\\\n')\\n\\t\\texcept :\\n\\t\\t\\tprint \\\"ERR - \\\" + i\\n\\t\\t\\tprint dict_of_files\",\n \"def save_result(file_name, text):\\n\\n filename = os.path.join(fileDir, 'tools/results/{}.txt'.format(file_name))\\n\\n with open(filename, \\\"a+\\\") as file:\\n file.write(text)\",\n \"def saveToTextFile(self, file_name):\\n with open(file_name, 'w') as file_obj:\\n for item in self.items:\\n print(item, file=file_obj)\",\n \"def save_text_file(i):\\n\\n fn = i['text_file']\\n\\n s = i['string']\\n\\n try:\\n s = s.replace('\\\\r', '')\\n except Exception as e:\\n pass\\n\\n try:\\n s = s.replace(b'\\\\r', b'')\\n except Exception as e:\\n pass\\n\\n m = 'w'\\n if i.get('append', '') == 'yes':\\n m = 'a'\\n\\n try:\\n s = s.encode('utf8')\\n except Exception as e:\\n pass\\n\\n try:\\n # if sys.version_info[0]>2:\\n # f=open(fn, m+'b')\\n # f.write(s)\\n # else:\\n f = open(fn, m+'b')\\n f.write(s)\\n except Exception as e:\\n return {'return': 1, 'error': 'problem writing text file='+fn+' ('+format(e)+')'}\\n\\n f.close()\\n\\n return {'return': 0}\",\n \"def open_logfile(self):\\r\\n if self.output_option == 2:\\r\\n self.ER_file = open(self.result_filename, 'w')\",\n \"def save(self,file):\\n\\n with open(file,\\\"w\\\") as f:\\n f.write(self.to_string())\",\n \"def write_polling_location_txt(self):\\n\\n plt = self.build_polling_location_txt()\\n\\n\\n # Drop base_df columns.\\n plt.drop(['index', 'office_name', 'official_name', 'phone', 'email', 'ocd_division', 'phone', 'location_name',\\n 'address1', 'address2', 'city', 'state', 'zip_code', 'start_time', 'end_time', 'start_date',\\n 'end_date', 'is_only_by_appointment', 'is_or_by_appointment', 'directions', 'notes'],\\n inplace=True, axis=1)\\n\\n plt = self.dedupe(plt)\\n plt = plt[plt.address_line.notnull()]\\n\\n plt.to_csv(config.output + 'polling_location.txt', index=False, encoding='utf-8') # send to txt file\\n plt.to_csv(config.output + 'polling_location.csv', index=False, encoding='utf-8') # send to csv file\",\n \"def test_output(self):\\n work_logs = [WorkLog(\\\"MYB-7\\\", datetime(2020, 1, 20), 3600, \\\"René Doe\\\"),\\n WorkLog(\\\"MYB-5\\\", datetime(2020, 1, 18), 3600, \\\"John Doe\\\"),\\n WorkLog(\\\"MYB-5\\\", datetime(2020, 1, 18), 5400, \\\"John Doe\\\"),\\n WorkLog(\\\"MYB-5\\\", datetime(2020, 1, 12), 3600, \\\"John Doe\\\")]\\n\\n issue_myb_5 = Issue(10005, \\\"MYB-5\\\", \\\"Summary of issue MYB-5\\\", \\\"MYB-3\\\", \\\"Summary of the parent issue of MYB-5\\\", 3600, 900, datetime(2020, 1, 15))\\n issue_myb_5.issue_start_date = datetime(2020, 1, 10)\\n issue_myb_7 = Issue(10007, \\\"MYB-7\\\", \\\"Summary of issue MYB-7\\\", None, None, None, None, None)\\n\\n issues = [issue_myb_5,\\n issue_myb_7]\\n\\n stdout = sys.stdout\\n with open('jira-time-report-console.txt', 'w') as sys.stdout:\\n jiratimereport.process_work_logs(\\\"console\\\", issues, work_logs)\\n sys.stdout = stdout\\n self.assertTrue(filecmp.cmp('console_output.txt', 'jira-time-report-console.txt'))\\n\\n jiratimereport.process_work_logs(\\\"csv\\\", issues, work_logs)\\n self.assertTrue(filecmp.cmp('csv_output.csv', 'jira-time-report.csv'))\\n\\n jiratimereport.process_work_logs(\\\"excel\\\", issues, work_logs)\\n expected_excel = pd.read_excel('excel_output.xlsx')\\n actual_excel = pd.read_excel('jira-time-report.xlsx')\\n self.assertTrue(expected_excel.equals(actual_excel))\",\n \"def flowdetail_save(fd):\\n return IMPL.flowdetail_save(fd)\",\n \"def dump2txt(cls, argv):\\n try:\\n for filename in argv[1:]:\\n parser = cls(filename)\\n invalid_str = r\\\"[\\\\\\\\/:*?\\\\\\\"<>|]\\\" # Not allowed to use filename\\n # Remove invalid text\\n subject = re.sub(invalid_str, \\\"\\\", parser.subject)\\n # Remove local time \\\"+09:00\\\", \\\"-\\\"\\n title_date = parser.date[:-len(\\\"+09:00\\\")].replace(\\\"-\\\", \\\"\\\")\\n # Remove invalid strings\\n date = re.sub(invalid_str, \\\"\\\", title_date)\\n result = parser.get_attr_data()\\n # Overwrite same date+subject eml\\n with open(f'{date}_{subject}.txt', 'w',\\n encoding='utf-8') as _f:\\n _f.write(result)\\n except BaseException as e:\\n with open('eml2ext_error.txt', 'w', encoding='utf-8') as _f:\\n print(f'error {e}')\\n # _f.write(e)\",\n \"def ledgerWrite(self):\\n if(self.back == 'y'):\\n open(\\\"ledger.txt\\\",'a').write(str(num_files) + ' ' + str(self.file_count) + ' ' + self.file_path + ' ' + \\\"y\\\" + '\\\\n')\\n else:\\n open(\\\"ledger.txt\\\",'a').write(str(num_files) + ' ' + str(self.file_count) + ' ' + self.file_path + ' ' + \\\"n\\\" + '\\\\n')\\n dbUp.upload_file(\\\"ledger.txt\\\", '/ledger.txt')\",\n \"def save_summary(model, model_name, stage_no):\\n stringlist = []\\n model.summary(print_fn=lambda x: stringlist.append(x))\\n short_model_summary = \\\"\\\\n\\\".join(stringlist)\\n \\n with open(eval_path+\\\"{}_model_summary_stage_{}.txt\\\".format(model_name, stage_no), \\\"w\\\") as text_file:\\n print(short_model_summary, file=text_file)\",\n \"def save_percentages_to_txt(self):\\n #initialise a dictionary with the list of NEs taken from \\n #https://spacy.io/api/annotation\\n if self.lang == 'en':\\n named_entity_divided_per_type = {'PERSON':0, 'NORP':0, 'FAC':0, \\n 'ORG':0, 'GPE':0, 'LOC':0, \\n 'PRODUCT':0, 'EVENT':0, \\n 'WORK_OF_ART':0, 'LAW':0, \\n 'LANGUAGE':0, 'DATE':0, 'TIME':0, \\n 'PERCENT':0, 'MONEY':0, \\n 'QUANTITY':0, 'ORDINAL':0, \\n 'CARDINAL':0}\\n if self.lang == 'de' or self.lang == 'fr':\\n named_entity_divided_per_type = {'PER':0, 'ORG':0, 'LOC':0, \\n 'MISC':0}\\n #define counter for total amount of NE\\n amount_ne_total = 0\\n #update the dictionary depending on the type of NE and update the \\n #counter\\n for sublist in self.named_entity_list_total:\\n for entry in sublist:\\n old_amount = (named_entity_divided_per_type.get(entry[3]))\\n named_entity_divided_per_type[entry[3]] = old_amount + 1\\n amount_ne_total += 1\\n #get percentages of the various NE types over the amount of nouns and \\n #numerals\\n percentages = {}\\n for i in named_entity_divided_per_type:\\n percentages[i] = round(100*named_entity_divided_per_type.get(i)/self.amount_nouns_and_num_total,1)\\n #write the output\\n outfile = open(('percentages_' + self.lang + '_' + self.method + \\n '.txt'), 'w')\\n outfile.write('Method used: ' + self.method + '\\\\n')\\n outfile.write('----------------------------------------------\\\\n')\\n outfile.write('Amount NEs: '+ str(amount_ne_total) + '\\\\n')\\n outfile.write('Amount nouns and numerals: '+ \\n str(self.amount_nouns_and_num_total) + '\\\\n')\\n outfile.write('----------------------------------------------\\\\n')\\n outfile.write('Percentages:\\\\n')\\n for i in percentages:\\n outfile.write(i+'\\\\t'+str(percentages.get(i)) + '%\\\\n')\\n outfile.close()\\n #build the plot\\n if self.lang == 'en':\\n plt.figure(figsize=(12,6))\\n plt.ylabel('#NE / #(NOUN,PROPN,NUM) [%]', fontsize=14)\\n elif self.lang == 'de' or self.lang == 'fr':\\n plt.figure(figsize=(2.5,6))\\n plt.ylabel('#NE / #(NOUN,PROPN) [%]', fontsize=14)\\n plt.bar(range(len(percentages)), list(percentages.values()), \\n align='center')\\n plt.xticks(range(len(percentages)), list(percentages.keys()), \\n fontsize=14, rotation='vertical')\\n plt.title('Method: '+self.method+'\\\\nLanguage: '+self.lang, fontsize=20)\\n plt.savefig('percentages_'+self.lang+'_'+self.method, \\n bbox_inches='tight')\\n return\",\n \"def save_exception(exc):\\n LOG.error(\\\"Error - %s\\\", str(exc))\\n hour = time.strftime(\\\"_%H_%M_%S\\\")\\n today = time.strftime(\\\"_%d_%m_%Y\\\")\\n data = (str(exc)+traceback.format_exc())\\n\\n file = open(\\\"./logs/ERROR_\\\"+threading.currentThread().getName()+today+\\\".log\\\",'a+') #Replace to fix OSError\\n file.write(\\\"\\\\n==\\\"+hour+\\\"==\\\\n\\\")\\n file.write(Parser.parse_text(data))\\n file.write(\\\"=====================================\\\\n\\\")\\n file.close()\",\n \"def fopenhelp(self):\",\n \"def log_to_file(text, status='INFO'):\\n outfile = open(LogName, 'a')\\n outfile.write(timestamp()+' - '+status+' - '+str(text)+'\\\\n')\\n outfile.close()\",\n \"def store_name(author):\\n with open(\\\"ref.txt\\\",\\\"a\\\") as fp:\\n fp.write(str(reference)+\\\"\\\"+'\\\\n')\",\n \"def _save_target_info(self):\\n \\n #make sure the file exists\\n path = self.communicator.image_store.project_path + \\\\\\n self.target_file_name\\n fout = open(path, 'w')\\n\\n print str(1)\\n print str(len(self.target_list)-1)\\n for i in range(1, len(self.target_list)):\\n fout.write(self.target_list[i].format_info())\\n fout.write(\\\"\\\\n\\\\n\\\")\\n fout.close()\",\n \"def writeText2File(loginfo, mfile):\\n with open(mfile,'a') as f:\\n f.writelines('%s\\\\n' %loginfo)\\n f.close()\",\n \"def _log_save_errors(self, ex_errs: Iterable[str]) -> None:\\n # Check for the root of save problems\\n new_errs = [e for e in self.aug.match(\\\"/augeas//error\\\") if e not in ex_errs]\\n\\n for err in new_errs:\\n logger.debug(\\n \\\"Error %s saving %s: %s\\\", self.aug.get(err), err[13:len(err) - 6],\\n self.aug.get(f\\\"{err}/message\\\"))\\n logger.error(\\n \\\"Unable to save files: %s.%s\\\", \\\", \\\".join(err[13:len(err) - 6] for err in new_errs),\\n f\\\" Save Notes: {self.configurator.save_notes}\\\" if self.configurator.save_notes else \\\"\\\")\",\n \"def save_highscores(self, contents):\\n\\t\\ttry:\\n\\t\\t\\twith open(self.filename, 'w') as f_obj:\\n\\t\\t\\t\\tf_obj.write(json.dumps(contents)) #save as json\\n\\t\\texcept FileNotFoundError:\\n\\t\\t\\tprint('File for highscores not found! Call 016 741 6243 for assistance.')\",\n \"def save_data_to_file(self, html_detail_tab, name_category):\\n name_saved_data = name_category + \\\".html\\\"\\n with open(os.path.join(self.current_path, name_saved_data), 'a+') as f:\\n try:\\n f.write(html_detail_tab)\\n except IOError as io:\\n logger.error(\\n \\\"caught an io exception while writing to the file\\\",\\n io)\",\n \"def save_to_file(urls):\\n try:\\n with open('url.txt', 'w') as file:\\n for url in urls:\\n file.write(url + \\\"\\\\n\\\")\\n except:\\n print(\\\"ERROR SAVING FILE\\\")\",\n \"def open_read_write(file_to_open=PROBLEM_FILE):\\n\\n with open(file_to_open, 'r') as working_file:\\n # turns the opened file into a list using a list comprehension\\n working_file = [sentence.strip() for sentence in working_file.readlines()]\\n\\n # iterates through the enumerated file displaying a tuple\\n # of (index, string)\\n for sentence in enumerate(working_file): #iterates through the file\\n if sentence[0] % 2: #Checks to see if the index is divisible by 2\\n print(sentence[1]) #prints the string\",\n \"def write_to_file(info, mode='w', file=\\\"output4.txt\\\"):\\n with open(file, mode, encoding='utf-8') as f:\\n for line in info:\\n f.write(' '.join(map(str, line)) + '\\\\n')\",\n \"def write(self, fname):\\n pass\",\n \"def write_info_to_file(self):\\n\\n self.info.write_mission_info()\\n\\n self.logger.info(\\\"Mission instance write succeeded.\\\")\",\n \"def save_to_file(self, string):\\n with open(self.output_path, \\\"w\\\") as text_file:\\n text_file.write(string)\\n print \\\"Saved to file \\\" + self.output_path\",\n \"def open(self) -> None:\",\n \"def open(self) -> None:\",\n \"def open(self) -> None:\",\n \"def save_log(file_path=None, stat=\\\"\\\"):\\n fp = open(file_path, mode=\\\"a+\\\")\\n fp.write(stat + \\\"\\\\n\\\")\\n fp.close()\",\n \"def getReport():\\n\\tpending = len(open(\\\"todo.txt\\\").readlines())\\n\\tdone = len(open(\\\"done.txt\\\").readlines())\\n\\tprint(\\n\\t \\\"{} Pending : {} Completed : {}\\\"\\n\\t .format(datetime.today().strftime(\\\"%Y-%m-%d\\\"),\\n\\t pending,\\n\\t done\\n\\t ))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67435944","0.59890133","0.58801615","0.58345985","0.5740403","0.5711492","0.5682778","0.56753564","0.5620525","0.55917394","0.55783993","0.55623657","0.5539575","0.5539575","0.5538157","0.55351144","0.5503311","0.55032104","0.5501654","0.5491713","0.54906255","0.54890096","0.5458524","0.5452088","0.5449945","0.544957","0.5449516","0.5433033","0.5397366","0.53936195","0.5384758","0.53360975","0.5327447","0.5318214","0.5301019","0.5298558","0.52900785","0.5260476","0.52547693","0.52492195","0.52490497","0.5233929","0.52271116","0.5221689","0.52215856","0.52193266","0.5215849","0.5203959","0.5199449","0.5198839","0.51901716","0.5188824","0.51874274","0.5181735","0.51784253","0.51712745","0.5170231","0.5162323","0.5154138","0.5148142","0.5144794","0.514277","0.51267993","0.51149994","0.51041996","0.5088343","0.5087364","0.50842065","0.50822127","0.50803727","0.50729537","0.5068292","0.50517017","0.504908","0.5044105","0.50368845","0.50365835","0.5029801","0.5029228","0.50277704","0.502772","0.50208235","0.5020438","0.50186974","0.50168306","0.5016703","0.5016228","0.5015595","0.50116014","0.4990313","0.49883494","0.4986193","0.49800557","0.4975238","0.49728897","0.49717343","0.49703133","0.49703133","0.49703133","0.49694505","0.49653947"],"string":"[\n \"0.67435944\",\n \"0.59890133\",\n \"0.58801615\",\n \"0.58345985\",\n \"0.5740403\",\n \"0.5711492\",\n \"0.5682778\",\n \"0.56753564\",\n \"0.5620525\",\n \"0.55917394\",\n \"0.55783993\",\n \"0.55623657\",\n \"0.5539575\",\n \"0.5539575\",\n \"0.5538157\",\n \"0.55351144\",\n \"0.5503311\",\n \"0.55032104\",\n \"0.5501654\",\n \"0.5491713\",\n \"0.54906255\",\n \"0.54890096\",\n \"0.5458524\",\n \"0.5452088\",\n \"0.5449945\",\n \"0.544957\",\n \"0.5449516\",\n \"0.5433033\",\n \"0.5397366\",\n \"0.53936195\",\n \"0.5384758\",\n \"0.53360975\",\n \"0.5327447\",\n \"0.5318214\",\n \"0.5301019\",\n \"0.5298558\",\n \"0.52900785\",\n \"0.5260476\",\n \"0.52547693\",\n \"0.52492195\",\n \"0.52490497\",\n \"0.5233929\",\n \"0.52271116\",\n \"0.5221689\",\n \"0.52215856\",\n \"0.52193266\",\n \"0.5215849\",\n \"0.5203959\",\n \"0.5199449\",\n \"0.5198839\",\n \"0.51901716\",\n \"0.5188824\",\n \"0.51874274\",\n \"0.5181735\",\n \"0.51784253\",\n \"0.51712745\",\n \"0.5170231\",\n \"0.5162323\",\n \"0.5154138\",\n \"0.5148142\",\n \"0.5144794\",\n \"0.514277\",\n \"0.51267993\",\n \"0.51149994\",\n \"0.51041996\",\n \"0.5088343\",\n \"0.5087364\",\n \"0.50842065\",\n \"0.50822127\",\n \"0.50803727\",\n \"0.50729537\",\n \"0.5068292\",\n \"0.50517017\",\n \"0.504908\",\n \"0.5044105\",\n \"0.50368845\",\n \"0.50365835\",\n \"0.5029801\",\n \"0.5029228\",\n \"0.50277704\",\n \"0.502772\",\n \"0.50208235\",\n \"0.5020438\",\n \"0.50186974\",\n \"0.50168306\",\n \"0.5016703\",\n \"0.5016228\",\n \"0.5015595\",\n \"0.50116014\",\n \"0.4990313\",\n \"0.49883494\",\n \"0.4986193\",\n \"0.49800557\",\n \"0.4975238\",\n \"0.49728897\",\n \"0.49717343\",\n \"0.49703133\",\n \"0.49703133\",\n \"0.49703133\",\n \"0.49694505\",\n \"0.49653947\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":74593,"cells":{"query":{"kind":"string","value":"Release the translating issues that have been accepted for more than specified delay days (at least 3 days)"},"document":{"kind":"string","value":"def find_delay_issues(self, msg, delay_days, unassign):\n self._asset_bind(msg)\n yield (\"Processing....\")\n trans = self._translation_util(msg)\n client = self._github_operator(msg)\n issues = find_delay_issues(trans, client, delay_days)\n if unassign == 0:\n yield (\"\\n\".join(limit_result([\"{}: {}, {}, {}\".format(issues[i][0].number, issues[i][0].title, issues[i][2], issues[i][3]) for i in issues])))\n else:\n success = unassign_issues(trans, issues)\n yield \"relassed {} issues that have been accepted for more than {} days\".format(success, delay_days)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def maintain_leases(self) -> None:\n while not self._stop_event.is_set():\n # Determine the appropriate duration for the lease. This is\n # based off of how long previous messages have taken to ack, with\n # a sensible default and within the ranges allowed by Pub/Sub.\n # Also update the deadline currently used if enough new ACK data has been\n # gathered since the last deadline update.\n deadline = self._manager._obtain_ack_deadline(maybe_update=True)\n _LOGGER.debug(\"The current deadline value is %d seconds.\", deadline)\n\n # Make a copy of the leased messages. This is needed because it's\n # possible for another thread to modify the dictionary while\n # we're iterating over it.\n leased_messages = copy.copy(self._leased_messages)\n\n # Drop any leases that are beyond the max lease time. This ensures\n # that in the event of a badly behaving actor, we can drop messages\n # and allow the Pub/Sub server to resend them.\n cutoff = time.time() - self._manager.flow_control.max_lease_duration\n to_drop = [\n requests.DropRequest(ack_id, item.size, item.ordering_key)\n for ack_id, item in leased_messages.items()\n if item.sent_time < cutoff\n ]\n\n if to_drop:\n _LOGGER.warning(\n \"Dropping %s items because they were leased too long.\", len(to_drop)\n )\n assert self._manager.dispatcher is not None\n self._manager.dispatcher.drop(to_drop)\n\n # Remove dropped items from our copy of the leased messages (they\n # have already been removed from the real one by\n # self._manager.drop(), which calls self.remove()).\n for item in to_drop:\n leased_messages.pop(item.ack_id)\n\n # Create a modack request.\n # We do not actually call `modify_ack_deadline` over and over\n # because it is more efficient to make a single request.\n ack_ids = leased_messages.keys()\n expired_ack_ids = set()\n if ack_ids:\n _LOGGER.debug(\"Renewing lease for %d ack IDs.\", len(ack_ids))\n\n # NOTE: This may not work as expected if ``consumer.active``\n # has changed since we checked it. An implementation\n # without any sort of race condition would require a\n # way for ``send_request`` to fail when the consumer\n # is inactive.\n assert self._manager.dispatcher is not None\n ack_id_gen = (ack_id for ack_id in ack_ids)\n expired_ack_ids = self._manager._send_lease_modacks(\n ack_id_gen, deadline\n )\n\n start_time = time.time()\n # If exactly once delivery is enabled, we should drop all expired ack_ids from lease management.\n if self._manager._exactly_once_delivery_enabled() and len(expired_ack_ids):\n assert self._manager.dispatcher is not None\n self._manager.dispatcher.drop(\n [\n requests.DropRequest(\n ack_id,\n leased_messages.get(ack_id).size, # type: ignore\n leased_messages.get(ack_id).ordering_key, # type: ignore\n )\n for ack_id in expired_ack_ids\n if ack_id in leased_messages\n ]\n )\n # Now wait an appropriate period of time and do this again.\n #\n # We determine the appropriate period of time based on a random\n # period between:\n # minimum: MAX_BATCH_LATENCY (to prevent duplicate modacks being created in one batch)\n # maximum: 90% of the deadline\n # This maximum time attempts to prevent ack expiration before new lease modacks arrive at the server.\n # This use of jitter (http://bit.ly/2s2ekL7) helps decrease contention in cases\n # where there are many clients.\n # If we spent any time iterating over expired acks, we should subtract this from the deadline.\n snooze = random.uniform(\n _MAX_BATCH_LATENCY, (deadline * 0.9 - (time.time() - start_time))\n )\n _LOGGER.debug(\"Snoozing lease management for %f seconds.\", snooze)\n self._stop_event.wait(timeout=snooze)\n\n _LOGGER.debug(\"%s exiting.\", _LEASE_WORKER_NAME)","def __cleanup(self, ttl_in_sec):\n ttl_in_ms = ttl_in_sec * 1000\n while True:\n logging.debug(\"cleanup action...\")\n current_ts = self.__current_timestamp_in_ms()\n self.lock.acquire()\n for key, value in self.orderedDict.items():\n if value[1] > current_ts - ttl_in_ms:\n break\n else:\n self.orderedDict.pop(key, None)\n self.lock.release()\n time.sleep(ttl_in_sec)","def remove_expired(cls):\n max_trailers = 10\n current_trailers = cls.get_all(collection='approved_trailers')\n current_trailers.reverse()\n queued_trailers = cls.get_all(collection='queued_trailers')\n\n if len(current_trailers) >= max_trailers and len(queued_trailers) > 0:\n for trailer in current_trailers:\n time_active = trailer.date.timetuple().tm_yday - datetime.now().timetuple().tm_yday\n if time_active >= 14 and len(queued_trailers) > 0:\n cls.move(trailer, 'approved_trailers', 'archived_trailers')\n cls.move(queued_trailers[0], 'queued_trailers', 'approved_trailers')","def trigger_expiration_notices(at_time=None, nb_days=15, dry_run=False):\n\n def _handle_organization_notices(organization):\n if organization.processor_card_key:\n card = organization.retrieve_card()\n try:\n exp_month, exp_year = card['exp_date'].split('/')\n exp_date = datetime(year=int(exp_year),\n month=int(exp_month), day=1, tzinfo=at_time.tzinfo)\n if lower >= exp_date:\n LOGGER.info(\"payment method expires soon for %s\",\n organization)\n if not dry_run:\n signals.card_expires_soon.send(\n sender=__name__, organization=organization,\n nb_days=nb_days)\n except (KeyError, ValueError):\n # exp info is missing or the format is incorrect\n pass\n else:\n LOGGER.info(\"%s doesn't have a payment method attached\",\n organization)\n if not dry_run:\n signals.payment_method_absent.send(sender=__name__,\n organization=organization)\n\n at_time = datetime_or_now(at_time)\n lower = at_time + relativedelta(days=nb_days)\n upper = at_time + relativedelta(days=nb_days + 1)\n LOGGER.info(\n \"trigger notifications for subscription expiring within [%s,%s[ ...\",\n lower, upper)\n prev_organization = None\n subscription = None\n for subscription in Subscription.objects.valid_for(ends_at__gte=lower,\n ends_at__lt=upper).order_by('organization'):\n org = subscription.organization\n plan = subscription.plan\n\n try:\n if subscription.auto_renew:\n if plan.renewal_type == plan.AUTO_RENEW:\n if org.id != prev_organization:\n _handle_organization_notices(org)\n\n prev_organization = org.id\n else:\n if plan.renewal_type == plan.ONE_TIME:\n LOGGER.info(\"trigger upgrade soon for %s\", subscription)\n if not dry_run:\n signals.subscription_upgrade.send(sender=__name__,\n subscription=subscription, nb_days=nb_days)\n\n elif plan.renewal_type == plan.REPEAT:\n LOGGER.info(\"trigger expires soon for %s\", subscription)\n if not dry_run:\n signals.expires_soon.send(sender=__name__,\n subscription=subscription, nb_days=nb_days)\n\n except Exception as err: #pylint:disable=broad-except\n # We use `Exception` because the email server might be\n # unavailable but ConnectionRefusedError is not a subclass\n # of RuntimeError.\n LOGGER.exception(\"error: %s\", err)\n\n # flushing the last organization\n if subscription and subscription.organization.id != prev_organization:\n if subscription.auto_renew:\n if subscription.plan.renewal_type == subscription.plan.AUTO_RENEW:\n _handle_organization_notices(subscription.organization)","def _reconsile_hits(self, broker, ftd_msgs, release_hits):\n for msg in ftd_msgs:\n found = False\n for hit in release_hits:\n if str(msg.id) in hit:\n release_hits.remove(hit)\n #print \"Found %s in %s\" % (msg.id, broker.log)\n found = True\n break\n if not found:\n self.assert_(False, \"Unable to locate released message %s in log %s\" % (msg.id, broker.log))\n if len(release_hits) > 0:\n err = \"Messages were unexpectedly released in log %s:\\n\" % broker.log\n for hit in release_hits:\n err += \" %s\\n\" % hit\n self.assert_(False, err)","def test_penalty_is_made_on_delay(self):\n twenty_days_ago = timezone.now() - timezone.timedelta(days=20)\n borrow = Borrow.objects.create(\n book_id=1,\n student=self.students[0],\n requested_at=twenty_days_ago,\n borrowed_at=twenty_days_ago,\n duration=10,\n )\n client = APIClient()\n client.login(username=self.manager.username, password=\"salam*123\")\n client.post(\"/borrows/1/terminate/\", data={\"duration\": 5})\n borrow.refresh_from_db()\n self.assertIsNotNone(borrow.returned_at)\n self.assertIsNotNone(borrow.delaypenalty)\n self.assertEqual(borrow.delaypenalty.amount, 11 * 1000)","def revise_agreements_expirations_planned(self):\n for agreement in self.search([('prolong', '=', 'unlimited')]):\n if agreement.next_expiration_date <= fields.Date.today():\n # force recalculate next_expiration_date\n agreement.write({'prolong': 'unlimited'})\n return True","def prune_out_of_date_queuing_delays(self, global_clock_sec):\n # Replacement deque\n new_queuing_delays = deque()\n for exit_time, queuing_delay in list(self.queuing_delays):\n time_since_exit = global_clock_sec - exit_time\n assert time_since_exit >= 0.0\n\n if time_since_exit <= LinkBuffer.MAX_DATA_AGE_SEC:\n new_queuing_delays.append((exit_time, queuing_delay))\n\n self.queuing_delays = new_queuing_delays","def cleanup_expired_dev_entries(**kwargs):\n logging.debug(\"In the cleanup_expired_dev_entries() method.\")\n logging.info(\"Checking for expired Developer Objects.\")\n\n # Get all rules for this ACP.\n\n all_acp_rules = ACPRule(fmc=fmc1, acp_name=acp_name)\n all_rules = all_acp_rules.get()\n if all_rules.get('items', '') is '':\n logging.warning(\"No rules found for Access Control Policy: {}.\".format(kwargs['acp_name']))\n else:\n for item in all_rules['items']:\n if 'Dev-' in item['name']:\n namesplit = item['name'].split('-')\n if int(namesplit[2]) < kwargs['threshold_time']:\n logging.info(\"Deleting {} rule from {}.\".format(item['name'], kwargs['acp_name']))\n tmp_rule = None\n tmp_rule = ACPRule(fmc=fmc1, acp_name=acp_name)\n tmp_rule.get(name=item['name'])\n tmp_rule.delete()\n # Now Delete any expired Host objects.\n all_ips = IPAddresses(fmc=fmc1)\n all_hosts = all_ips.get()\n for item in all_hosts['items']:\n if 'Dev-' in item['name']:\n namesplit = item['name'].split('-')\n if int(namesplit[2]) < kwargs['threshold_time']:\n logging.info(\"Deleting {} host object.\".format(item['name']))\n tmp_rule = None\n tmp_rule = IPHost(fmc=fmc1)\n tmp_rule.get(name=item['name'])\n tmp_rule.delete()\n # Finally Delete any expired Port objects.\n all_ports = ProtocolPort(fmc=fmc1)\n response = all_ports.get()\n for item in response['items']:\n if 'Dev-' in item['name']:\n namesplit = item['name'].split('-')\n if int(namesplit[2]) < kwargs['threshold_time']:\n logging.info(\"Deleting {} port object.\".format(item['name']))\n tmp_rule = None\n tmp_rule = ProtocolPort(fmc=fmc1)\n tmp_rule.get(name=item['name'])\n tmp_rule.delete()","def status_check(self):\n from coordinator.tasks import cancel_release\n # Check if we hit the time limit\n last_update = self.events.order_by('-created_at')\\\n .first().created_at\n diff = datetime.datetime.utcnow() - last_update.replace(tzinfo=None)\n\n if diff.total_seconds() > settings.RELEASE_TIMEOUT:\n if self.state == 'canceling':\n return\n logger.error(f'canceling release {self.kf_id} for time out.')\n self.cancel()\n self.save()\n django_rq.enqueue(cancel_release, self.kf_id)\n return\n\n # Check if any contained tasks have failed/canceled\n for task in self.tasks.all():\n if task.state in ['failed', 'canceled', 'rejected']:\n if self.state == 'canceling':\n return\n logger.error(f'canceling release: {self.kf_id} task is ' +\n f'{task.state}')\n self.cancel()\n self.save()\n django_rq.enqueue(cancel_release, self.kf_id)\n return","def DissolutionAfterReinstatement(obj):\n if (obj.dissolution_date is None) or (obj.reinstatement_date is None): \n return\n if obj.dissolution_date > obj.reinstatement_date:\n raise interface.Invalid(\n _(\"A committee must be disolved before it can be reinstated\"), \n \"dissolution_date\", \n \"reinstatement_date\")","def test_debt_target_not_expired_when_new_issue_added(self):\n measurement = self.measurement(\n self.metric(accept_debt=True, debt_target=\"100\", issue_ids=[\"FOO-41\", \"FOO-42\"]),\n count={\"debt_target\": \"100\"},\n issue_status=[{\"status_category\": \"done\", \"issue_id\": \"FOO-41\"}],\n )\n self.assertFalse(measurement.debt_target_expired())","def test_order_ejection_4_hours_extended_24(self):\n manager = EjectionManager(self.user)\n curr_time = self.curr_time\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=61))\n self.assertEqual(self.order.number_of_ejection_notices, 1)\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=121))\n self.assertEqual(self.order.number_of_ejection_notices, 2)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=121))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=181))\n self.assertEqual(self.order.number_of_ejection_notices, 3)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=181))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=211))\n self.assertEqual(self.order.number_of_ejection_notices, 4)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=211))\n\n OrderUpdate.objects.create(\n order=self.order,\n creator=self.user,\n time_limit_delta=timedelta(hours=24))\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=211, seconds=1))\n\n curr_time = curr_time + timedelta(minutes=211, seconds=1)\n # Now that we've extended the Order by 24 hours left to go, we should\n # start getting periodic notifications\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=31))\n self.assertEqual(self.order.number_of_ejection_notices, 5)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=31))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=511))\n self.assertEqual(self.order.number_of_ejection_notices, 6)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=511))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=991))\n self.assertEqual(self.order.number_of_ejection_notices, 7)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=991))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=1231))\n self.assertEqual(self.order.number_of_ejection_notices, 8)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=1231))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=1351))\n self.assertEqual(self.order.number_of_ejection_notices, 9)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=1351))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=1411))\n self.assertEqual(self.order.number_of_ejection_notices, 10)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=1411))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=1441))\n self.assertEqual(self.order.number_of_ejection_notices, 11)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=1441))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=1456))\n self.assertEqual(self.order.number_of_ejection_notices, 12)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=1456))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=1471))\n self.assertEqual(self.order.number_of_ejection_notices, 12)\n self.assertEqual(self.order.status, Order.STATUS_CLOSED)","def check_msg_release(self, broker, ftd_msgs):\n hits = self._get_hits(broker, re.compile(\"debug Message id=\\\"[0-9a-f-]{36}\\\"; pid=0x[0-9a-f]+: \"\n \"Content released$\", re.MULTILINE))\n self._reconsile_hits(broker, ftd_msgs, hits)","def ssl_expires_in(entity, serial_number, remaining, buffer_days=14):\n # if the cert expires in less than two weeks, we should reissue it\n if remaining < datetime.timedelta(days=0):\n # cert has already expired - uhoh!\n print(\"Cert %s issued to '%s' expired %s days ago!\"\n % (serial_number, entity, remaining.days))\n elif remaining < datetime.timedelta(days=buffer_days):\n # expires sooner than the buffer\n print(\"Cert %s issued to '%s' is nearly expired - %s more days\"\n % (serial_number, entity, remaining.days))\n else:\n # everything is fine\n print(\"Cert %s issued to '%s' is valid for %s more days\"\n % (serial_number, entity, remaining.days))","def __removing_loop(self) -> None:\r\n\r\n # repeat until stop flag is set\r\n while not self.__stopper.wait(self.CLEANUP_EXPIRED_INTERVAL):\r\n now = int(datetime.now(self.__tz).timestamp())\r\n log.debug('Removing...')\r\n\r\n # iterate through database and remove expired encounters\r\n for enc_id, despawn_time in self.__pokes_db.copy().items():\r\n if despawn_time - now < 5:\r\n del self.__pokes_db[enc_id]","def _cleanupDownCounts(self):\n now = datetime.now()\n timeout = timedelta(minutes=DOWN_COUNT_TIMEOUT_MINUTES)\n for taskName, (down_count, last_time) in self._down_counts.iteritems():\n if now - last_time > timeout:\n del self._down_counts[taskName]","def release_budget(self, plan_id, amount):\n budget_line = self.search([('id', '=', plan_id)])\n if float_compare(amount, budget_line.planned_amount, 2) == 1:\n raise ValidationError(\n _('Release amount exceed planned amount!'))\n budget_line.write({'released_amount': amount})\n budget_line.project_id._trigger_auto_sync()\n return True","def test_maxttl_greater_doc_expiry(self):\n for bucket in self.buckets:\n self._load_json(bucket, self.num_items, exp=int(self.maxttl)-100)\n self.sleep(int(self.maxttl-100), \"waiting for all docs to expire per maxTTL rule...\")\n self.expire_pager(self.servers)\n self.sleep(20, \"waiting for item count to come down...\")\n for bucket in self.buckets:\n items = RestConnection(self.master).get_active_key_count(bucket)\n self.log.info(\"Doc expiry set to = {0}s, maxTTL = {1}s, after {2}s, item count = {3}\".format(\n int(self.maxttl) - 100,\n self.maxttl-100,\n self.maxttl-100,\n items))\n if items == 0:\n self.log.info(\"SUCCESS: Docs with lesser expiry deleted\")\n else:\n self.fail(\"FAIL: Doc with lesser expiry still present past ttl\")","def renew_close_to_expiration(self, margin_in_seconds=A_DAY):\n subscriptions = self.storage.close_to_expiration(margin_in_seconds)\n for subscription in subscriptions:\n try:\n self.subscribe_impl(**subscription)\n except SubscriberError as e:\n warn(RENEW_FAILURE % (subscription['topic_url'],\n subscription['callback_id']), e)","def test_debt_target_expired(self):\n measurement = self.measurement(\n self.metric(accept_debt=True, debt_target=\"100\", issue_ids=[\"FOO-40\"]),\n count={\"debt_target\": \"100\"},\n issue_status=[{\"status_category\": \"done\", \"issue_id\": \"FOO-40\"}],\n )\n self.assertTrue(measurement.debt_target_expired())","def update_validity(sender, instance, **kwargs):\n\tinstance.up_to_date = True\n\n\tfor sol in instance.post_set.all():\n\t\tsol.up_to_date = False\n\t\tsol.save()","def check_for_canceled(self, index):\n if 0 <= index < len(self.stops):\n stop = self.stops[index]\n if stop.status == StopStatuses.planned or stop.status == StopStatuses.announced:\n self.issue_time += timedelta(seconds=config.INTERVAL_BETWEEN_UPDATE_MSG)\n self.tasks.append(self.instruction_task(stop.station_url, 'prio', self.issue_time))","def check_due(self, objects, time=None):\n time = time or datetime.utcnow()\n recurrences = self.recurrenceforobject_set.prefetch_related('content_object').all()\n all_scheduled_objs = set(r.content_object for r in recurrences)\n past_due = set(r.content_object for r in recurrences.filter(next_scheduled__lt=time))\n\n due = []\n for obj in objects:\n if obj in all_scheduled_objs and obj in past_due:\n due.append(obj)\n elif obj not in all_scheduled_objs:\n due.append(obj)\n self._sub_recurrence(obj)\n return due","def clean_reports():\n logger.info('Cleaning reports older than %d days' % lgr_settings.report_expiration_delay)\n # set the last run in the settings data\n lgr_settings.report_expiration_last_run = timezone.now()\n lgr_settings.save()\n nbr, __ = LGRReport.objects.filter(\n created_at__lt=datetime.datetime.now() - datetime.timedelta(days=lgr_settings.report_expiration_delay)\n ).delete()\n logger.info('%d reports removed' % nbr)","def release_unclaimed_goods():\n print('releasing unclaimed goods...')\n released = 0\n goods = db.session.query(Good).filter(Good.tx_hash == None).filter(Good.order_id != None).all()\n for good in goods:\n from .order import has_expired # don't move me to prevent cyclical deps\n if has_expired(good.order_id):\n release_good(good.order_id)\n released = released + 1\n\n log.info('released %s goods' % released)\n return released","def reap_stale_submissions():\n\n print(\"Reaping stale submissions\")\n\n # Find and update stale submissions\n Submission.query.filter(\n Submission.last_updated < datetime.now() - timedelta(minutes=60),\n Submission.processed == False,\n Submission.state != 'regrading',\n ).update({\n 'processed': True,\n 'state': \"Reaped after timeout\",\n }, False)\n\n # Commit any changes\n db.session.commit()","def adjustableDelay(delay, ants) :\n antlist = helpers.makeList(ants)\n s.adjustableDelay(delay, antlist)","def remind_all_finally_to_update_release_notes(self):\n names = read_from_file(self.pr.config.releaseItemsFileMergedBy)\n LOG.debug(\"final names list =\" + names)\n if names:\n time.sleep(10)\n\n msg = RELEASE_NOTES_REMINDER.format(msg=names, release_notes_link=self.pr.config.release_notes_link,\n qa_team=self.pr.config.qaTeamMembers)\n self.slack.postToSlack(channel=self.pr.config.alertChannelName, msg=msg)\n self.clean_up_for_next_cycle()","def start_lease_expiry_timer(self, ack_ids: Iterable[str]) -> None:\n with self._add_remove_lock:\n for ack_id in ack_ids:\n lease_info = self._leased_messages.get(ack_id)\n # Lease info might not exist for this ack_id because it has already\n # been removed by remove().\n if lease_info:\n self._leased_messages[ack_id] = lease_info._replace(\n sent_time=time.time()\n )","def do(self):\n self.message += 'Searching for unpayed loans \\n'\n today = dt.now()\n tomorrow = today + timedelta(days=1)\n\n expired = Loan.objects.filter(due_date=today)\n almost = Loan.objects.filter(due_date=tomorrow)\n\n self.charge(expired)\n self.notify(almost)\n\n self.log_success()","def check_auto_reject(self):\r\n for pr in self:\r\n if not pr.line_ids.filtered(lambda l: l.cancelled is False):\r\n pr.write({'state': 'rejected'})","def test_form_invalid_date_more_4_weeks(self):\n login = self.client.login(\n username='testuser2',\n password='2HJ1vRV0Z&3iD')\n future_date = datetime.date.today()\\\n + datetime.timedelta(weeks=4, days=1)\n response = self.client.post(\n reverse('librarian-renew-book',\n kwargs={'pk': self.test_bookinstance1.pk}),\n {'due_back': future_date})\n self.assertEqual(response.status_code, 200)\n self.assertFormError(response,\n 'form',\n 'due_back',\n 'Invalid date - renewal more than 4 weeks a ahead')","async def process(self, timeout=60):\n\n previous_date = self.previous_date()\n new_date = previous_date\n last_sent_message_date = previous_date\n now = pendulum.now('UTC')\n\n self.log.info(\"Begining processing feed %s, previous date %s\",\n self.name, previous_date)\n\n for entry in await self.fetch_and_parse(timeout):\n\n pubdate = dateutil.parser.parse(entry.published, tzinfos=rssalertbot.BOGUS_TIMEZONES)\n entry.published = pendulum.from_timestamp(pubdate.timestamp())\n # also save a prettified string format\n entry.datestring = self.format_timestamp_local(entry.published)\n\n # skip anything that's stale\n if entry.published <= previous_date:\n continue\n\n event_id = md5((entry.title + entry.description).encode()).hexdigest()\n last_sent = self.storage.load_event(self.feed, event_id)\n re_alert = self.cfg.get('re_alert', rssalertbot.RE_ALERT_DEFAULT)\n should_delete_message = False\n\n if entry.published > now:\n if last_sent and now < last_sent.add(hours=re_alert):\n continue\n self.storage.save_event(self.feed, event_id, now)\n else:\n if entry.published > new_date:\n new_date = entry.published\n should_delete_message = last_sent\n\n self.log.debug(\"Found new entry %s\", entry.published)\n\n # alert on it\n await self.alert(entry)\n if new_date > last_sent_message_date:\n self.storage.save_date(self.feed, new_date)\n last_sent_message_date = new_date\n\n if should_delete_message:\n self.log.debug(f\"Deleting stored date for message {event_id}\")\n self.storage.delete_event(self.feed, event_id)\n\n self.log.info(\"End processing feed %s, previous date %s\", self.name, new_date)","def _deactivate_campaigns(self):\n now = timezone.now()\n for campaign in self.campaigns.active():\n campaign.until = now\n campaign.save(update_fields=['until'])","def killQueueOrder(self):\n # CHECK ALL QUEUE ORDERS AND CANCEL ORDER IF GREATER THAN TWO HOURS OLD\n queue_orders = self.queue.find(\n {\"Trader\": self.user[\"Name\"], \"Asset_Type\": self.asset_type, \"Account_ID\": self.account_id})\n\n dt = datetime.now(tz=pytz.UTC).replace(microsecond=0)\n\n dt_central = dt.astimezone(pytz.timezone('US/Central'))\n\n two_hours_ago = datetime.strptime(datetime.strftime(\n dt_central - timedelta(hours=2), \"%Y-%m-%d %H:%M:%S\"), \"%Y-%m-%d %H:%M:%S\")\n\n ten_minutes_ago = datetime.strptime(datetime.strftime(\n dt_central - timedelta(minutes=10), \"%Y-%m-%d %H:%M:%S\"), \"%Y-%m-%d %H:%M:%S\")\n\n for order in queue_orders:\n\n order_date = order[\"Date\"]\n\n order_type = order[\"Order_Type\"]\n\n id = order[\"Order_ID\"]\n\n forbidden = [\"REJECTED\", \"CANCELED\", \"FILLED\"]\n\n if two_hours_ago > order_date and (order_type == \"BUY\" or order_type == \"BUY_TO_OPEN\") and id != None and order[\"Order_Status\"] not in forbidden:\n\n # FIRST CANCEL ORDER\n resp = self.tdameritrade.cancelOrder(id)\n\n if resp.status_code == 200 or resp.status_code == 201:\n\n other = {\n \"Symbol\": order[\"Symbol\"],\n \"Order_Type\": order[\"Order_Type\"],\n \"Order_Status\": \"CANCELED\",\n \"Strategy\": order[\"Strategy\"],\n \"Account_ID\": self.account_id,\n \"Aggregation\": order[\"Aggregation\"],\n \"Trader\": self.user[\"Name\"],\n \"Date\": getDatetime()\n }\n\n if self.asset_type == \"OPTION\":\n\n other[\"Pre_Symbol\"] = order[\"Pre_Symbol\"]\n\n other[\"Exp_Date\"] = order[\"Exp_Date\"]\n\n self.other.insert_one(other)\n\n self.queue.delete_one(\n {\"Trader\": self.user[\"Name\"], \"Symbol\": order[\"Symbol\"], \"Strategy\": order[\"Strategy\"], \"Asset_Type\": self.asset_type})\n\n self.logger.INFO(\n f\"CANCELED ORDER FOR {order['Symbol']} - TRADER: {self.user['Name']}\", True)\n\n # IF QUEUE ORDER DATE GREATER THAN 10 MINUTES OLD AND ORDER ID EQUALS NONE, SEND ALERT\n if ten_minutes_ago > order_date and order[\"Order_ID\"] == None and order[\"Account_ID\"] == self.account_id:\n\n if order[\"Symbol\"] not in self.no_ids_list:\n\n self.logger.ERROR(\n \"QUEUE ORDER ID ERROR\", f\"ORDER ID FOR {order['Symbol']} NOT FOUND - TRADER: {self.user['Name']} - ACCOUNT ID: {self.account_id}\")\n\n self.no_ids_list.append(order[\"Symbol\"])\n\n else:\n\n if order[\"Symbol\"] in self.no_ids_list:\n\n self.no_ids_list.remove(order[\"Symbol\"])","def test_expiration_banner_with_expired_upgrade_deadline(self):\n past = datetime(2010, 1, 1, tzinfo=UTC)\n CourseDurationLimitConfig.objects.create(enabled=True, enabled_as_of=past)\n course = CourseFactory.create(start=now() - timedelta(days=10))\n CourseModeFactory.create(course_id=course.id, mode_slug=CourseMode.AUDIT)\n CourseModeFactory.create(course_id=course.id, mode_slug=CourseMode.VERIFIED, expiration_datetime=past)\n user = UserFactory(password=self.TEST_PASSWORD)\n self.client.login(username=user.username, password=self.TEST_PASSWORD)\n CourseEnrollment.enroll(user, course.id, mode=CourseMode.AUDIT)\n\n url = course_home_url(course)\n response = self.client.get(url)\n bannerText = get_expiration_banner_text(user, course)\n self.assertContains(response, bannerText, html=True)\n self.assertContains(response, TEST_BANNER_CLASS)","def _check_timeouts(self):\n\n expired_tokens = []\n for token in self._capability_timeouts:\n interval = datetime.utcnow() - self._capability_timeouts[token]\n if interval.total_seconds() >= 10:\n expired_tokens.append(token)\n\n for token in expired_tokens:\n cap_withdraw = mplane.model.Withdrawal(capability=self._capabilities[token])\n self.handle_message(cap_withdraw, self.identity_for(token))","def test_notify_delay_not_elapsed(self):\n # self.client.force_authenticate(user=self.admin)\n\n self.wait_queue_notif = WaitQueueNotification.objects.create(\n user=self.user2,\n retreat=self.retreat,\n )\n\n response = self.client.get(\n '/'.join([\n reverse('retreat:waitqueuenotification-list'),\n 'notify',\n ])\n )\n\n self.assertEqual(\n response.status_code,\n status.HTTP_200_OK,\n response.content,\n )\n\n response_data = json.loads(response.content)\n\n content = {\n 'detail': 'Last notification was sent less than 24h ago.'\n }\n\n self.assertEqual(response_data, content)","def test_revoke_stops(self):\n # Load sample data:\n TSStation.update_stations('TestTAMission.data/stations.xml')\n TASeries.import_xml('TestTAMission.data/series.xml')\n\n mission = TAMission.get('nl.3046')\n mission.activate_mission(mark_cet(datetime(2013, 2, 18, 2)))\n self.assertEqual(len(mission.stops), 5)\n self.assertEqual(mission.origin_id, 'nl.ah')\n self.assertEqual(mission.destination_id, 'nl.asd')\n mission.put()\n\n # STEP 10a revoke Ede\n self.post_stops_from_file('TestTAMission.data/step_10_10a.json')\n mission = TAMission.get('nl.3046')\n self.assertEqual(len(mission.stops), 4)\n self.assertEqual(mission.origin_id, 'nl.ah')\n self.assertEqual(mission.destination_id, 'nl.asd')\n\n # ...and set a delay for Arnhem - this must not cause an error when revoking Arnhem\n self.assertEqual(mission.first_stop.station_id, 'nl.ah')\n self.assertEqual(mission.first_stop.delay_dep, 15.0)\n\n # STEP 10b revoke Arnhem\n self.post_stops_from_file('TestTAMission.data/step_10_10b.json')\n mission = TAMission.get('nl.3046')\n self.assertEqual(len(mission.stops), 3)\n self.assertEqual(mission.origin_id, 'nl.klp')\n\n # STEP 10c revoke Utrecht and Veenendaal De Klomp\n self.post_stops_from_file('TestTAMission.data/step_10_10c.json')\n mission = TAMission.get('nl.3046')\n self.assertEqual(len(mission.stops), 0)\n self.assertEqual(mission.origin_id, None)\n self.assertEqual(mission.destination_id, None)\n\n mission = TAMission.get('nl.3046')\n mission.activate_mission(mark_cet(datetime(2013, 2, 19, 2)))\n self.assertEqual(len(mission.stops), 8)\n self.assertEqual(mission.origin_id, 'nl.ah')\n self.assertEqual(mission.destination_id, 'nl.amr')\n mission.put()\n\n # STEP 10d reset Arnhem\n self.post_stops_from_file('TestTAMission.data/step_10_10d.json')\n mission = TAMission.get('nl.3046')\n self.assertEqual(len(mission.stops), 5)\n self.assertEqual(mission.origin_id, 'nl.ah')\n self.assertEqual(mission.destination_id, 'nl.asd')\n self.assertEqual(mission.stops[4].status, StopStatuses.finalDestination)","def test_email_warnings_only_sent_for_unpaid(self, mock_tz):\n mock_tz.now.return_value = datetime(\n 2015, 2, 11, 10, 0, tzinfo=dt_timezone.utc\n )\n\n # payment_due_date 2015/2/11 23:59 (within 24hrs - warnings sent)\n ticketed_event = baker.make_recipe(\n 'booking.ticketed_event_max10',\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\n payment_open=True,\n ticket_cost=10,\n payment_due_date=datetime(2015, 2, 11, tzinfo=dt_timezone.utc),\n )\n\n baker.make(\n TicketBooking, ticketed_event=ticketed_event, paid=False,\n date_booked=datetime(2015, 2, 1, 0, 0, tzinfo=dt_timezone.utc),\n _quantity=5,\n )\n baker.make(\n TicketBooking, ticketed_event=ticketed_event, paid=True,\n date_booked=datetime(2015, 2, 1, 0, 0, tzinfo=dt_timezone.utc),\n _quantity=5,\n )\n _add_user_email_addresses(TicketBooking)\n for ticket_booking in TicketBooking.objects.all():\n baker.make(Ticket, ticket_booking=ticket_booking)\n\n management.call_command('email_ticket_booking_warnings')\n self.assertEqual(len(mail.outbox), 5)\n for ticket_booking in TicketBooking.objects.filter(paid=False):\n self.assertTrue(ticket_booking.warning_sent)\n for ticket_booking in TicketBooking.objects.filter(paid=True):\n self.assertFalse(ticket_booking.warning_sent)","def test_dont_cancel_bookings_within_cancellation_period_without_warning_sent(self, mock_tz):\n mock_tz.now.return_value = datetime(2015, 2, 10, 18, 0, tzinfo=dt_timezone.utc)\n # reset warning flags\n self.unpaid.warning_sent = False\n self.unpaid.date_warning_sent = None\n self.unpaid.save()\n\n self.assertEqual(self.unpaid.status, 'OPEN')\n self.assertFalse(self.unpaid.warning_sent)\n self.assertIsNone(self.unpaid.date_warning_sent)\n management.call_command('cancel_unpaid_bookings')\n self.unpaid.refresh_from_db()\n # still open\n self.assertEqual(self.unpaid.status, 'OPEN')\n\n # set the warning sent flag to < 2hrs ago\n self.unpaid.warning_sent = True\n self.unpaid.date_warning_sent = datetime(2015, 2, 10, 17, 0, tzinfo=dt_timezone.utc)\n self.unpaid.save()\n management.call_command('cancel_unpaid_bookings')\n self.unpaid.refresh_from_db()\n # still open\n self.assertEqual(self.unpaid.status, 'OPEN')\n\n # set the warning sent flag to > 2hrs ago\n self.unpaid.warning_sent = True\n self.unpaid.date_warning_sent = datetime(2015, 2, 10, 15, 0, tzinfo=dt_timezone.utc)\n self.unpaid.save()\n management.call_command('cancel_unpaid_bookings')\n self.unpaid.refresh_from_db()\n # now cancelled\n self.assertEqual(self.unpaid.status, 'CANCELLED')","def test_maxttl_lesser_doc_expiry(self):\n for bucket in self.buckets:\n self._load_json(bucket, self.num_items, exp=int(self.maxttl)+500)\n self.sleep(int(self.maxttl), \"waiting for all docs to expire per maxTTL rule...\")\n self.expire_pager(self.servers)\n self.sleep(20, \"waiting for item count to come down...\")\n for bucket in self.buckets:\n items = RestConnection(self.master).get_active_key_count(bucket)\n RestConnection(self.master).get_active_key_count(bucket)\n self.log.info(\"Doc expiry set to = {0}s, maxTTL = {1}s, after {2}s, item count = {3}\".format(\n int(self.maxttl) + 500,\n self.maxttl,\n self.maxttl,\n items))\n if items > 0:\n self.fail(\"Bucket maxTTL of {0} is not honored\".format(self.maxttl))\n else:\n self.log.info(\"SUCCESS: Doc expiry set to = {0}s, maxTTL = {1}s, after {2}s, item count = {3}\".format(\n int(self.maxttl) + 500,\n self.maxttl,\n self.maxttl,\n items))","def check_msg_release_on_recover(self, broker, ftd_msgs):\n hits = self._get_hits(broker, re.compile(\"debug Message id=\\\"[0-9a-f-]{36}\\\"; pid=0x[0-9a-f]+: \"\n \"Content released after recovery$\", re.MULTILINE))\n self._reconsile_hits(broker, ftd_msgs, hits)","def test_pypi_py2py3_expired_major_grace_period(self):\n obsolete_dep_info = dict(UP_TO_DATE_DEPS)\n obsolete_dep_info['google-auth'] = {\n 'current_time': datetime.datetime(2019, 3, 23, 0, 0, 0),\n 'installed_version': '0.9.9',\n 'installed_version_time': datetime.datetime(\n 2019, 2, 19, 21, 15, 56),\n 'is_latest': False,\n 'latest_version': '1.0.0',\n 'latest_version_time': datetime.datetime(2019, 2, 19, 21, 15, 56)\n }\n\n obsolete_dep_compat_results = list(RECENT_SUCCESS_DATA)\n obsolete_dep_compat_results.remove(GOOGLE_API_CORE_RECENT_SUCCESS_2)\n obsolete_dep_compat_results.remove(GOOGLE_API_CORE_RECENT_SUCCESS_3)\n obsolete_dep_compat_results.append(\n GOOGLE_API_CORE_RECENT_SUCCESS_2.with_updated_dependency_info(\n obsolete_dep_info))\n obsolete_dep_compat_results.append(\n GOOGLE_API_CORE_RECENT_SUCCESS_3.with_updated_dependency_info(\n obsolete_dep_info))\n\n self.fake_store.save_compatibility_statuses(\n obsolete_dep_compat_results)\n package_name = 'google-api-core'\n self.assertImageResponsePyPI(package_name)\n self.assertTargetResponse(\n package_name, self.expired_major_grace_period_expected_details)","def invalidate_artifacts(buf0, t_reject=T_REJECT,\n n_reject=N_REJECT, verbose=True):\n ti_all = [(b['timestamp'], i) for i, b in enumerate(buf0)]\n ti_all = sorted(ti_all)\n t_all = np.array([t[0] for t in ti_all])\n i_all = [t[1] for t in ti_all]\n\n nb = len(buf0)\n ri = range(nb)\n i = 0\n while i < nb - 1:\n ii = []\n t0 = t_all[i]\n for j in xrange(i + 1, nb):\n if t_all[j] < t0 + t_reject:\n ii.append(j)\n else:\n break\n i = j\n\n if len(ii) < n_reject:\n continue\n for ix in ii:\n try:\n ri.remove(i_all[ix])\n except ValueError:\n pass\n\n buf = [buf0[i] for i in ri]\n if verbose and len(buf) != nb:\n print '* Rejecting', nb - len(buf), 'spikes.'\n return buf","def test_order_ejection_4_hours(self):\n manager = EjectionManager(self.user)\n curr_time = self.curr_time\n\n manager.process_order_time_limit(self.order, curr_time)\n self.assertEqual(self.order.number_of_ejection_notices, 1)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time)\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=61))\n self.assertEqual(self.order.number_of_ejection_notices, 1)\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=121))\n self.assertEqual(self.order.number_of_ejection_notices, 2)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=121))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=181))\n self.assertEqual(self.order.number_of_ejection_notices, 3)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=181))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=211))\n self.assertEqual(self.order.number_of_ejection_notices, 4)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=211))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=226))\n self.assertEqual(self.order.number_of_ejection_notices, 5)\n self.set_latest_order_update_time_created(\n self.order,\n curr_time + timedelta(minutes=226))\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=230))\n self.assertEqual(self.order.number_of_ejection_notices, 5)\n\n manager.process_order_time_limit(self.order,\n curr_time + timedelta(minutes=241))\n self.assertEqual(self.order.number_of_ejection_notices, 5)\n self.assertEqual(self.order.status, Order.STATUS_CLOSED)","def test_dont_cancel_rebookings_within_cancellation_period_without_warning_sent(self, mock_tz):\n mock_tz.now.return_value = datetime(2015, 2, 10, 18, 0, tzinfo=dt_timezone.utc)\n # cancel booking to reset warning flags\n self.unpaid.status = \"CANCELLED\"\n self.unpaid.save()\n # rebook\n self.unpaid.status = \"OPEN\"\n self.unpaid.date_rebooked = datetime(2015, 2, 10, 12, 30, tzinfo=dt_timezone.utc)\n self.unpaid.save()\n\n self.assertEqual(self.unpaid.status, 'OPEN')\n self.assertFalse(self.unpaid.warning_sent)\n self.assertIsNone(self.unpaid.date_warning_sent)\n management.call_command('cancel_unpaid_bookings')\n self.unpaid.refresh_from_db()\n # still open\n self.assertEqual(self.unpaid.status, 'OPEN')\n\n # set the warning sent flag to < 2hrs ago\n self.unpaid.warning_sent = True\n self.unpaid.date_warning_sent = datetime(2015, 2, 10, 17, 0, tzinfo=dt_timezone.utc)\n self.unpaid.save()\n management.call_command('cancel_unpaid_bookings')\n self.unpaid.refresh_from_db()\n # still open\n self.assertEqual(self.unpaid.status, 'OPEN')\n\n # set the warning sent flag to > 2hrs ago\n self.unpaid.warning_sent = True\n self.unpaid.date_warning_sent = datetime(2015, 2, 10, 15, 0, tzinfo=dt_timezone.utc)\n self.unpaid.save()\n management.call_command('cancel_unpaid_bookings')\n self.unpaid.refresh_from_db()\n # now cancelled\n self.assertEqual(self.unpaid.status, 'CANCELLED')","def _periodically_cleanup_candidates(self):\n while True:\n yield 5 * 60.0\n\n now = time()\n for key, candidate in [(key, candidate) for key, candidate in self._candidates.iteritems() if candidate.is_all_obsolete(now)]:\n if __debug__: dprint(\"removing obsolete candidate \", candidate)\n del self._candidates[key]\n self.wan_address_unvote(candidate)","def solve_collisions(self, t2):\n self.t_start, self.t_end = self.t1, t2\n self.current_dt = self.t_end-self.t_start\n\n # Loop until an acceptable state is found.\n while True:\n\n # Check if any events have occurred.\n impact_surfaces = detect_impacts(self)\n release_surfaces = detect_releases(self)\n\n # Case 1: no events, return with the current state.\n if not any((release_surfaces, impact_surfaces)):\n return \n\n # Case 2: impact but no releases, solve for impacts.\n elif (impact_surfaces and (not release_surfaces)):\n surfaces, impact_state = find_first_impact(self,\n impact_surfaces, self._impact_solve_method)\n self.impact_update(surfaces, impact_state) \n self._state1_impacts = surfaces\n #print '\\tImpact', impact_state['t'], surfaces\n \n # Case 3: releases but no impacts, solve for releases. \n elif (release_surfaces and (not impact_surfaces)):\n surfaces, release_state = find_first_release(self,\n release_surfaces, self._release_solve_method)\n self.release_update(surfaces, release_state) \n self._state1_releases = []\n #print '\\tRelease', release_state['t'], surfaces\n\n # Case 4: both impacts and releases, find first event.\n else:\n impact_surfaces, impact_state = find_first_impact(self,\n impact_surfaces, self._impact_solve_method)\n release_surfaces, release_state = find_first_release(self,\n release_surfaces, self._release_solve_method)\n\n # Times are very close to one another - transfer of contact.\n if abs(impact_state['t']-release_state['t'])<1e-3:\n self.constraint_transition_update(impact_surfaces,\n release_surfaces, impact_state)\n #print '\\tTransition of constraints'\n\n # Impact occurred first.\n elif impact_state['t'] < release_state['t']:\n self.impact_update(impact_surfaces, impact_state)\n self._state1_impacts = impact_surfaces\n\n # Release occurred first.\n else:\n self.release_update(release_surfaces, release_state)","def decrement_notice(self):\n\t\tassert not any(self.feat_time_left==0)\n\t\tself.feat_time_left[self.feat_time_left>0]-=1\n\t\tbool_feats_to_remove=(self.feat_time_left==0)\n\t\tself.remove_feats(bool_feats_to_remove)","async def afterHoursAutoPurge(self, ctx: Context):","def _repair_crossed_asks(self, ask):\r\n while len(self.asks) and self.asks[0].price < ask:\r\n volume = self.asks[0].volume\r\n self._update_total_ask(-volume)\r\n self.asks.pop(0)\r\n self._valid_ask_cache = -1\r\n #self.debug(\"### repaired ask\")\r","async def afterHoursAutoPurgeInactiveDuration(self, ctx: Context, *, duration: Optional[str]):\n\n guildConfig = self.config.guild(ctx.guild)\n autoPurgeConfig = guildConfig.get_attr(KEY_AUTO_PURGE)\n autoPurgeInactiveDurationConfig = autoPurgeConfig.get_attr(KEY_INACTIVE_DURATION)\n\n if duration:\n if duration == \"0\":\n await autoPurgeInactiveDurationConfig.set(0)\n await ctx.send(\"Disabled auto-purge on inactive duration.\")\n return\n else:\n match = re.match(\n r\"^\\s*\"\n r\"(?:(\\d{1,5})\\s*y(?:(?:ea)?rs?)?)?\" # years\n r\"\\s*\"\n r\"(?:(\\d{1,5})\\s*mo(?:nth)?s?)?\" # months\n r\"\\s*\"\n r\"(?:(\\d{1,5})\\s*w(?:(?:ee)?ks?)?)?\" # weeks\n r\"\\s*\"\n r\"(?:(\\d{1,5})\\s*d(?:ays?)?)?\" # days\n r\"\\s*\"\n r\"(?:(\\d{1,5})\\s*h(?:(?:ou)?rs?)?)?\" # hours\n r\"\\s*\"\n r\"(?:(\\d{1,5})\\s*m(?:in(?:ute)?s?)?)?\" # minutes\n r\"\\s*\"\n r\"(?:(\\d{1,5})\\s*s(?:ec(?:ond)?s?)?)?\" # seconds\n r\"\\s*$\",\n duration,\n re.IGNORECASE,\n )\n if match is None:\n await ctx.send(\"Invalid duration.\")\n return\n else:\n groups = match.groups()\n years, months, weeks, days, hours, minutes, seconds = [\n int(x) if x else 0 for x in groups\n ]\n\n totalSeconds = (\n years * 365 * 24 * 60 * 60\n + months * 30 * 24 * 60 * 60\n + weeks * 7 * 24 * 60 * 60\n + days * 24 * 60 * 60\n + hours * 60 * 60\n + minutes * 60\n + seconds\n )\n\n await autoPurgeInactiveDurationConfig.set(totalSeconds)\n\n if totalSeconds == 0:\n await ctx.send(\"Disabled auto-purge on inactive duration.\")\n else:\n await ctx.send(\n f\"Set the inactive duration for auto-purge to {humanize_timedelta(seconds=totalSeconds)}.\"\n )\n else:\n totalSeconds: int = await autoPurgeInactiveDurationConfig()\n if totalSeconds == 0:\n await ctx.send(\"The inactive duration for auto-purge is currently not set.\")\n else:\n await ctx.send(\n f\"The inactive duration for auto-purge is currently set to {humanize_timedelta(seconds=totalSeconds)}.\"\n )","def test_email_warnings(self, mock_tz):\n mock_tz.now.return_value = datetime(\n 2015, 2, 10, 10, 0, tzinfo=dt_timezone.utc\n )\n\n # cancellation period starts 2015/2/14 17:00\n # payment_due_date 2015/2/11 23:59\n event = baker.make_recipe(\n 'booking.future_EV',\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\n payment_open=True,\n cost=10,\n payment_due_date=datetime(2015, 2, 11, tzinfo=dt_timezone.utc),\n cancellation_period=1)\n # cancellation period starts 2015/2/14 17:00\n # payment_due_date 2015/2/12 23:59\n event1 = baker.make_recipe(\n 'booking.future_EV',\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\n payment_open=True,\n cost=10,\n payment_due_date=datetime(2015, 2, 12, tzinfo=dt_timezone.utc),\n cancellation_period=1)\n # no cost, no warnings sent\n event2 = baker.make_recipe(\n 'booking.future_EV',\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\n payment_open=True,\n cost=0,\n cancellation_period=1)\n baker.make_recipe(\n 'booking.booking', event=event, paid=False,\n payment_confirmed=False,\n date_booked=datetime(2015, 2, 9, 19, 30, tzinfo=dt_timezone.utc),\n _quantity=5,\n )\n baker.make_recipe(\n 'booking.booking', event=event1, paid=False,\n payment_confirmed=False,\n date_booked=datetime(2015, 2, 9, 19, 30, tzinfo=dt_timezone.utc),\n _quantity=5,\n )\n baker.make_recipe(\n 'booking.booking', event=event2, paid=False,\n payment_confirmed=False,\n date_booked=datetime(2015, 2, 9, 21, 00, tzinfo=dt_timezone.utc),\n _quantity=5,\n )\n _add_user_email_addresses(Booking)\n management.call_command('email_warnings')\n self.assertEqual(len(mail.outbox), 10)","def release(self, delay=None):\n the_tuple = self.deque.release(self.tube, self.task_id, delay=delay)\n\n self.update_from_tuple(the_tuple)\n\n if delay is None:\n return bool(self.state == 1)\n else:\n return bool(self.state == 0)","def test_sms_campaign_expire_check(self):\n result = sms_campaign_expire_check.delay()\n self.assertEqual(result.successful(), True)","def unsend_scheduled_messages_after(self, time_cutoff):\n for user_id in self.user_id_to_scheduled_message_ts:\n for scheduled_ts in list(self.user_id_to_scheduled_message_ts[user_id]):\n if scheduled_ts >= time_cutoff:\n # The below if statement is likley redundant\n if scheduled_ts in self.user_id_to_scheduled_message_ts[user_id]:\n self.user_id_to_scheduled_message_ts[user_id].remove(scheduled_ts)","def _clean_outdated(self):\n now = _now()\n outdated = []\n for request_no, request_info in self._current_requests.items():\n if now - request_info.start_time > self._force_clean_after:\n outdated.append(request_no)\n if outdated:\n logging.error(\"There are {} requests which were started but haven't \"\n \"been finished in more than {}s.\"\n .format(len(outdated), self._force_clean_after))\n for request_no in outdated:\n del self._current_requests[request_no]\n self._last_autoclean_time = now","def notify_overdue(self, **kwargs):\n return self.notify(\"notify_overdue\", **kwargs)","def test_dont_cancel_bookings_in_cancellation_period_if_warning_not_sent(self, mock_tz):\n mock_tz.now.return_value = datetime(2015, 2, 11, 12, 0, tzinfo=dt_timezone.utc)\n\n # self.ticketed_event payment due date 2015/2/11 23:59\n\n unpaid_no_warning = baker.make(\n TicketBooking,\n ticketed_event=self.ticketed_event,\n paid=False,\n date_booked=datetime(2015, 2, 10, 5, 30, tzinfo=dt_timezone.utc),\n warning_sent=False\n )\n unpaid_warning_within_2_hrs = baker.make(\n TicketBooking,\n ticketed_event=self.ticketed_event,\n paid=False,\n date_booked=datetime(2015, 2, 10, 5, 30, tzinfo=dt_timezone.utc),\n warning_sent=True,\n date_warning_sent=datetime(2015, 2, 11, 10, 30, tzinfo=dt_timezone.utc),\n )\n unpaid_warning_more_than_2_hrs_ago = baker.make(\n TicketBooking,\n ticketed_event=self.ticketed_event,\n paid=False,\n date_booked=datetime(2015, 2, 10, 5, 30, tzinfo=dt_timezone.utc),\n warning_sent=True,\n date_warning_sent=datetime(2015, 2, 11, 9, 30, tzinfo=dt_timezone.utc),\n )\n\n self.assertFalse(unpaid_no_warning.cancelled)\n self.assertFalse(unpaid_warning_within_2_hrs.cancelled)\n self.assertFalse(unpaid_warning_more_than_2_hrs_ago.cancelled)\n\n management.call_command('cancel_unpaid_ticket_bookings')\n unpaid_no_warning.refresh_from_db()\n unpaid_warning_within_2_hrs.refresh_from_db()\n unpaid_warning_more_than_2_hrs_ago.refresh_from_db()\n self.assertFalse(unpaid_no_warning.cancelled)\n self.assertFalse(unpaid_warning_within_2_hrs.cancelled)\n self.assertTrue(unpaid_warning_more_than_2_hrs_ago.cancelled)","def _cleanUp(self):\r\n limit = datetime.now() - timedelta(seconds=self._timeout)\r\n\r\n toClean = [msg for msg in self._incompleteMsgs if msg.older(limit)]\r\n\r\n if toClean:\r\n for msg in toClean:\r\n self._incompleteMsgs.remove(msg)\r\n\r\n log.msg('{0} incomplete messages have been dropped '\r\n 'from assembler.'.format(len(toClean)))\r\n\r\n toClean = [uri for uri, (_, timestamp) in self._binaries.iteritems()\r\n if timestamp < limit]\r\n\r\n if toClean:\r\n for uri in toClean:\r\n del self._binaries[uri]\r\n\r\n log.msg('{0} unused binaries have been dropped '\r\n 'from assembler.'.format(len(toClean)))","def test_redeem_error_after_delay(\n self, get_config: GetConfig, clock: Clock, voucher: bytes\n ) -> None:\n datetime_now = clock_to_now(clock)\n store = self.useFixture(\n TemporaryVoucherStore(\n datetime_now,\n get_config,\n ),\n ).store\n controller = PaymentController(\n clock,\n store,\n UnpaidRedeemer(),\n default_token_count=100,\n allowed_public_keys=set(),\n )\n self.assertThat(\n Deferred.fromCoroutine(controller.redeem(voucher)),\n succeeded(Always()),\n )\n # It fails this time.\n self.assertThat(\n controller.get_voucher(voucher).state,\n MatchesAll(\n IsInstance(model_Unpaid),\n MatchesStructure(\n finished=Equals(datetime_now()),\n ),\n ),\n )\n\n # Some time passes.\n interval = timedelta(hours=1)\n clock.advance(interval.total_seconds())\n\n # It failed again.\n self.assertThat(\n controller.get_voucher(voucher).state,\n MatchesAll(\n IsInstance(model_Unpaid),\n MatchesStructure(\n # At the new time, demonstrating the retry was performed.\n finished=Equals(datetime_now()),\n ),\n ),\n )","def put_busy(duration, headers):\n debug = os.environ.get(\"DREPIN_DEBUG\")\n message = \"DREPIN_DEBUG is not defined. Delay duration: 0 sec.\"\n if debug:\n message = \"Delay duration: {} sec.\".format(duration)\n now_sec = time.time()\n end_sec = now_sec + duration\n while now_sec < end_sec:\n now_sec = time.time()\n return message","def test_pypi_py2py3_expired_default_grace_period(self):\n obsolete_dep_info = dict(UP_TO_DATE_DEPS)\n obsolete_dep_info['google-auth'] = {\n 'current_time': datetime.datetime(2019, 8, 23, 0, 0, 0),\n 'installed_version': '1.3.0',\n 'installed_version_time': datetime.datetime(\n 2019, 2, 19, 21, 15, 56),\n 'is_latest': False,\n 'latest_version': '1.0.0',\n 'latest_version_time': datetime.datetime(2019, 2, 19, 21, 15, 56)\n }\n\n obsolete_dep_compat_results = list(RECENT_SUCCESS_DATA)\n obsolete_dep_compat_results.remove(GOOGLE_API_CORE_RECENT_SUCCESS_2)\n obsolete_dep_compat_results.remove(GOOGLE_API_CORE_RECENT_SUCCESS_3)\n obsolete_dep_compat_results.append(\n GOOGLE_API_CORE_RECENT_SUCCESS_2.with_updated_dependency_info(\n obsolete_dep_info))\n obsolete_dep_compat_results.append(\n GOOGLE_API_CORE_RECENT_SUCCESS_3.with_updated_dependency_info(\n obsolete_dep_info))\n\n self.fake_store.save_compatibility_statuses(\n obsolete_dep_compat_results)\n package_name = 'google-api-core'\n self.assertImageResponsePyPI(package_name)\n self.assertTargetResponse(\n package_name, self.expired_default_grace_period_expected_details)","def releaseWave(t):\n for w in sorted(waveTimes.keys(), reverse=True):\n wt = waveTimes[w]\n if t - p >= wt:\n return w\n raise Exception('Tiempo de liberación de solicitud infactible.')","def delay_completion(mission):\n if mission.ut() - mission.current_step[\"start_ut\"] > 5:\n vessel = mission.conn.space_center.active_vessel\n vessel.auto_pilot.disengage()\n mission.next()","def action_payslip_done(self):\n for recd in self.overtime_ids:\n recd.payslip_paid = True\n return super(PayslipOverTime, self).action_payslip_done()","def test_crash_on_impossible_fixity(self):\n campaign, propositions, categories, user_campaigns = self.generate_batch_wishes(\n 2, 5\n )\n\n campaign.manager = User.objects.get(pk=\"17bocquet\")\n campaign.save()\n\n categories_uc = {}\n for uc in user_campaigns:\n categories_uc[uc.category.id] = categories_uc.get(uc.category.id, []) + [uc]\n\n for i in range(3):\n uc = categories_uc[categories[0].id][i]\n uc.fixed_to = propositions[0]\n uc.save()\n\n with self.assertRaises(ValueError):\n self.login(\"17bocquet\")\n self.patch(\n \"/repartitions/campaigns/{}/\".format(campaign.id),\n data={\"status\": \"RESULTS\"},\n )","def test_delete_on_background_response_descriptor_projects_release_release_resource(self):\n pass","def reap_recent_assignments():\n from anubis.config import config\n\n recent_assignments = Assignment.query.filter(\n Assignment.release_date > datetime.now(),\n Assignment.due_date > datetime.now() - config.STATS_REAP_DURATION,\n ).all()\n\n print(json.dumps({\n 'reaping assignments:': [assignment.data for assignment in recent_assignments]\n }, indent=2))\n\n for assignment in recent_assignments:\n for submission in Submission.query.filter(\n Submission.assignment_id == assignment.id,\n Submission.build == None,\n ).all():\n if submission.build is None:\n init_submission(submission)\n enqueue_autograde_pipeline(submission.id)\n\n for assignment in recent_assignments:\n bulk_autograde(assignment.id)","def check_for_timeouts(self) -> None:\n\n if self.id != self._leader_id:\n return\n\n for instance in self._undecided_instances:\n if (time.time() - self._last_prepare_time[instance]) > self._round_timeouts[instance]:\n #print('\\033[93m' + \"Instance {0} round {1} exceeded timeout!\".format(instance, self._round_id[instance]) + \"\\033[0m\")\n self._round_timeouts[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\n self._enable_phase1_optimization = False\n self.prepare_phase_parallel(instance)\n # Handle a single time out per loop to improve responsiveness to input messages\n break\n\n for instance in self._decided_values.keys():\n if not self._acked_decided_values[instance]:\n if (time.time() - self._last_decide_time[instance]) > self._learners_decide_timeout[instance]:\n # Timed out waiting the learner ACK: send the decided value to the learners and increase the timeout\n self._learners_decide_timeout[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\n decide_message: Decide = Decide(sender=self,\n receiver_role=Role.LEARNER,\n payload=DecidePayload((self._decided_values[instance], instance))\n )\n self.send(decide_message)\n\n # Handle a single time out per loop to improve responsiveness to input messages\n break","def test_remove_recurring_schedule(self):\n pass","def _thresholdAlarm(self, project_usage, proj_name, old_used_space_perc):\n\n message = \"\"\n threshold_soft = 95\n threshold_hard = 100\n if (proj_name in self.conf.mirrored_projects):\n threshold_soft = 95*2\n threshold_hard = 100*2\n if ((project_usage['used_space_perc'] > threshold_soft) and \n (project_usage['used_space_perc'] > old_used_space_perc)):\n message = \"project \" + proj_name + \" is reaching its quota limit \" \\\n + \"(used space > 95%): \" + str(project_usage['used_space'])\\\n + \" \" + self.conf.storage_space_unity\n if (project_usage['used_space_perc'] >= threshold_hard):\n message = \"project \" + proj_name + \" reached its quota limit \" \\\n + \"(used space > 100%): \" + str(project_usage['used_space'])\\\n + \" \" + self.conf.storage_space_unity\n if (len(message) > 0):\n mailsnd = MailSender()\n mailsnd.send(message, self.conf.notification_sender, \n self.conf.notification_receiver)\n logger.debug(\"sent alert for quota over limit related to project: \"\n + proj_name)","def test_escalate_questions_cron(self, submit_ticket):\n\n questions_to_escalate = [\n # Questions over 24 hours old without an answer.\n question(\n created=datetime.now() - timedelta(hours=24, minutes=10),\n save=True),\n question(\n created=datetime.now() - timedelta(hours=24, minutes=50),\n save=True),\n ]\n\n # Question about Firefox OS\n fxos = product(slug='firefox-os', save=True)\n q = question(\n created=datetime.now() - timedelta(hours=24, minutes=10),\n product=fxos,\n save=True)\n questions_to_escalate.append(q)\n\n questions_not_to_escalate = [\n # Questions newer than 24 hours without an answer.\n question(save=True),\n question(created=datetime.now() - timedelta(hours=11), save=True),\n question(created=datetime.now() - timedelta(hours=21), save=True),\n ]\n\n # Question older than 24 hours with a recent answer.\n q = question(\n created=datetime.now() - timedelta(hours=24, minutes=10),\n save=True)\n answer(created=datetime.now() - timedelta(hours=10), question=q,\n save=True)\n answer(created=datetime.now() - timedelta(hours=1), creator=q.creator,\n question=q, save=True)\n questions_not_to_escalate.append(q)\n\n # Question older than 24 hours with a recent answer by the asker.\n q = question(\n created=datetime.now() - timedelta(hours=24, minutes=10),\n save=True)\n answer(\n created=datetime.now() - timedelta(hours=15), creator=q.creator,\n question=q, save=True)\n questions_not_to_escalate.append(q)\n\n # Question older than 24 hours without an answer already escalated.\n q = question(\n created=datetime.now() - timedelta(hours=24, minutes=10),\n save=True)\n q.tags.add(config.ESCALATE_TAG_NAME)\n questions_not_to_escalate.append(q)\n\n # Question with an inactive user.\n q = question(\n created=datetime.now() - timedelta(hours=24, minutes=10),\n save=True)\n q.creator.is_active = False\n q.creator.save()\n questions_not_to_escalate.append(q)\n\n # Question about Thunderbird, which is one of the products we exclude.\n tb = product(slug='thunderbird', save=True)\n q = question(\n created=datetime.now() - timedelta(hours=24, minutes=10),\n product=tb,\n save=True)\n questions_not_to_escalate.append(q)\n\n # Run the cron job and verify only 3 questions were escalated.\n eq_(len(questions_to_escalate), escalate_questions())","def _notify_exceeded_budget(self, budget: Budget) -> None:\n print(f'[NOTIFICATION] You have exceeded the {budget.name} budget.')","def test_issue_edit_issue_deadline(self):\n pass","def test_email_warnings_not_sent_within_2_hrs_of_booking(self, mock_tz):\n mock_tz.now.return_value = datetime(\n 2015, 2, 11, 10, 0, tzinfo=dt_timezone.utc\n )\n\n # payment_due_date 2015/2/11 23:59 (within 24hrs - warnings sent)\n ticketed_event = baker.make_recipe(\n 'booking.ticketed_event_max10',\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\n payment_open=True,\n ticket_cost=10,\n payment_due_date=datetime(2015, 2, 11, tzinfo=dt_timezone.utc),\n )\n booking1 = baker.make(\n TicketBooking, ticketed_event=ticketed_event, paid=False,\n date_booked=datetime(2015, 2, 1, 0, 0, tzinfo=dt_timezone.utc),\n )\n booking2 = baker.make(\n TicketBooking, ticketed_event=ticketed_event, paid=False,\n date_booked=datetime(2015, 2, 11, 8, 5, tzinfo=dt_timezone.utc),\n )\n _add_user_email_addresses(TicketBooking)\n for ticket_booking in TicketBooking.objects.all():\n baker.make(Ticket, ticket_booking=ticket_booking)\n\n management.call_command('email_ticket_booking_warnings')\n self.assertEqual(len(mail.outbox), 1)\n booking1.refresh_from_db()\n booking2.refresh_from_db()\n self.assertTrue(booking1.warning_sent)\n self.assertFalse(booking2.warning_sent)","def admin_bankruptcy_forced_cancellation(self, name, with_report):\n discards = list()\n for delay in self.DELAYS:\n write_keys = self._get_sample_owners(name=name, delay=delay)\n leaderboard = self.get_leaderboard(granularity=LeaderboardGranularity.name_and_delay, count=10000,\n name=name, delay=delay, readable=False)\n losers = [key for key in write_keys if\n (self.shash(key) in leaderboard) and (leaderboard[self.shash(key)] < -1.0)]\n\n for write_key in losers:\n if self.bankrupt(write_key=write_key):\n code = self.shash(write_key)\n memo = Memo(activity=Activity.cancel, context=ActivityContext.bankruptcy, name=name, code=code,\n write_key=write_key, message='Initiating cancellation of submissions due to bankruptcy')\n self.add_memo_as_owner_confirm(memo=memo)\n self.cancel(name=name, write_key=write_key, delay=delay)\n discards.append((name, write_key))\n report_data = dict(discards)\n report_memo = Memo(activity=Activity.daemon, context=ActivityContext.bankruptcy, count=len(report_data), data=report_data)\n self.add_memo_as_system_confirm(memo=report_memo,private_actor=PrivateActor.bankruptcy_daemon)\n return len(discards) if not with_report else report_memo.as_dict()","def killQueueOrder(self):\n # CHECK ALL QUEUE ORDERS AND CANCEL ORDER IF GREATER THAN TWO HOURS OLD\n queue_orders = self.queue.find(\n {\"Trader\": self.user[\"Name\"], \"Account_ID\": self.account_id})\n\n dt = datetime.now(tz=pytz.UTC).replace(microsecond=0)\n\n dt_central = dt.astimezone(pytz.timezone('US/Central'))\n\n two_hours_ago = datetime.strptime(datetime.strftime(\n dt_central - timedelta(hours=2), \"%Y-%m-%d %H:%M:%S\"), \"%Y-%m-%d %H:%M:%S\")\n\n ten_minutes_ago = datetime.strptime(datetime.strftime(\n dt_central - timedelta(minutes=10), \"%Y-%m-%d %H:%M:%S\"), \"%Y-%m-%d %H:%M:%S\")\n\n for order in queue_orders:\n\n order_date = order[\"Date\"]\n\n order_type = order[\"Order_Type\"]\n\n id = order[\"Order_ID\"]\n\n forbidden = [\"REJECTED\", \"CANCELED\", \"FILLED\"]\n\n if two_hours_ago > order_date and (order_type == \"BUY\" or order_type == \"BUY_TO_OPEN\") and id != None and order[\"Order_Status\"] not in forbidden:\n\n # FIRST CANCEL ORDER\n resp = self.tdameritrade.cancelOrder(id)\n\n if resp.status_code == 200 or resp.status_code == 201:\n\n other = {\n \"Symbol\": order[\"Symbol\"],\n \"Order_Type\": order[\"Order_Type\"],\n \"Order_Status\": \"CANCELED\",\n \"Strategy\": order[\"Strategy\"],\n \"Account_ID\": self.account_id,\n \"Trader\": self.user[\"Name\"],\n \"Date\": getDatetime()\n }\n\n self.other.insert_one(other)\n\n self.queue.delete_one(\n {\"Trader\": self.user[\"Name\"], \"Symbol\": order[\"Symbol\"], \"Strategy\": order[\"Strategy\"]})\n\n self.logger.INFO(\n f\"CANCELED ORDER FOR {order['Symbol']} - TRADER: {self.user['Name']}\", True)\n\n # IF QUEUE ORDER DATE GREATER THAN 10 MINUTES OLD AND ORDER ID EQUALS NONE, SEND ALERT\n if ten_minutes_ago > order_date and order[\"Order_ID\"] == None and order[\"Account_ID\"] == self.account_id:\n\n if order[\"Symbol\"] not in self.no_ids_list:\n\n self.logger.ERROR(\n \"QUEUE ORDER ID ERROR\", f\"ORDER ID FOR {order['Symbol']} NOT FOUND - TRADER: {self.user['Name']} - ACCOUNT ID: {self.account_id}\")\n\n self.no_ids_list.append(order[\"Symbol\"])\n\n else:\n\n if order[\"Symbol\"] in self.no_ids_list:\n\n self.no_ids_list.remove(order[\"Symbol\"])","def renew(self):\n remaining=self.time_left()\n if ( (remaining !=-1) and (self.update_frequency!=-1) and \n (remaining<self.update_frequency) ): \n self.create()","def test_update_maxttl(self):\n for bucket in self.buckets:\n self._load_json(bucket, self.num_items, exp=100)\n self._update_bucket_maxTTL(maxttl=40)\n\n self.sleep(40, \"waiting before running expiry pager...\")\n self.expire_pager(self.servers)\n self.sleep(20, \"waiting for item count to come down...\")\n for bucket in self.buckets:\n items = RestConnection(self.master).get_active_key_count(bucket)\n self.log.info(\"Doc expiry set to = 100s, maxTTL at the time of doc creation = 200s\"\n \" updated maxttl = 40s, after 40s item count = {0}\".format(items))\n if items != self.num_items:\n self.fail(\"FAIL: Updated ttl affects docs with larger expiry before updation!\")\n\n self.sleep(60, \"waiting before running expiry pager...\")\n self.expire_pager(self.servers)\n self.sleep(20, \"waiting for item count to come down...\")\n for bucket in self.buckets:\n items = RestConnection(self.master).get_active_key_count(bucket)\n self.log.info(\"Doc expiry set to = 100s, maxTTL at the time of doc creation = 200s\"\n \" updated maxttl = 40s, after 100s item count = {0}\".format(items))\n if items != 0:\n self.fail(\"FAIL: Docs with 100s as expiry before maxTTL updation still alive!\")","def _putMailInPast(self, mail, days):\n doc = mail.getEditableContent()\n fid = self.archiver.date_field_id\n doc.edit({fid: doc.getDataModel()[fid] - days}, mail)","def cleanup(keep_num=5):\n\n keep_num = int(keep_num)\n assert keep_num > 0, \"[ERROR] keep_num must be > 0; refusing to proceed.\"\n\n with cd(\"%(path)s/packages\" % env):\n package_files = sorted(run(\"ls -1\").split())\n package_files = [_.replace(\".tar.gz\", \"\") for _ in package_files]\n\n with cd(\"%(path)s/releases\" % env):\n release_files = sorted(run(\"ls -1\").split())\n release_files.remove('current')\n\n diff = set(package_files).symmetric_difference(set(release_files))\n\n if diff:\n raise Exception(\"[ERROR]: Package and release directories are out of sync;\"\n \" refusing to proceed. Please fix this difference manually: %s\" % diff)\n\n package_files = package_files[:-keep_num]\n release_files = release_files[:-keep_num]\n\n with cd(\"%(path)s/packages\" % env):\n [sudo(\"rm %s.tar.gz\" % _) for _ in package_files]\n\n with cd(\"%(path)s/releases\" % env):\n [sudo(\"rm -r %s\" % _) for _ in release_files]","def check_cycle_durations(\n request: Request, policy: RequestPolicy, logger: Logger\n) -> None:\n if not policy.check_cycle_length:\n logger.warning(\n \"KSR-BUNDLE-CYCLE-DURATION: Disabled by policy (check_cycle_length)\"\n )\n return\n\n if not request.bundles:\n logger.warning(\"KSR-BUNDLE-CYCLE-DURATION: No bundles - can't check anything\")\n return\n\n cycle_inception_length = (\n request.bundles[-1].inception - request.bundles[0].inception\n )\n _inc_len_str = fmt_timedelta(cycle_inception_length)\n _min_inc_str = fmt_timedelta(policy.min_cycle_inception_length)\n _max_inc_str = fmt_timedelta(policy.max_cycle_inception_length)\n\n logger.debug(\n f\"Verifying that first bundle inception to last bundle inception ({_inc_len_str}) \"\n f\"is between {_min_inc_str} and {_max_inc_str}\"\n )\n\n if cycle_inception_length < policy.min_cycle_inception_length:\n raise KSR_BUNDLE_CYCLE_DURATION_Violation(\n f\"Cycle inception length ({_inc_len_str}) \"\n f\"less than minimum acceptable length {_min_inc_str}\"\n )\n if cycle_inception_length > policy.max_cycle_inception_length:\n raise KSR_BUNDLE_CYCLE_DURATION_Violation(\n f\"Cycle inception length ({_inc_len_str}) \"\n f\"greater than maximum acceptable length {_max_inc_str}\"\n )\n\n logger.info(\n \"KSR-BUNDLE-CYCLE-DURATION: The cycle length is in accordance with the KSK operator policy\"\n )","async def release(ctx, pkmn_id: int):\n res = database.release_from_party(ctx.message.author, pkmn_id)\n if res:\n tier = _get_tier(int(pkmn_id))\n money = int(_get_money_earned(tier)/3)\n message = \"Oak: Don't worry I'll take care of {} ;) \\nHere's ₱{}, buy yourself something nice.\"\\\n .format(database.get_pokemon_name(pkmn_id), money)\n database.add_pokedollars(ctx.message.author, money)\n else:\n message = \"**Oak**: Make sure you actually have that pokemon or if your party is not full ya scrub.\"\n await ctx.send(message)","def delete_expired(self):\n check_time = datetime.now()\n if self.can_expire and self.duration:\n exp_times = deepcopy(self.exp_times)\n for key in exp_times:\n if exp_times[key] < check_time:\n self.delete(key)","def update_lease_expiration(self, network_id, ip_address, lease_remaining):\n self.cast(self.context,\n self.make_msg('update_lease_expiration',\n network_id=network_id,\n ip_address=ip_address,\n lease_remaining=lease_remaining,\n host=self.host),\n topic=self.topic)","def cooldown_checker(self):\n self.cooldown_tick += 1\n if self.cooldown_tick == self.pattern_cooldown:\n self.wait = False\n self.cooldown_tick = 0","def _release_resources(self, job_limits: Dict[str, int]) -> None:\n for limit_name, count in job_limits.items():\n self.limits_used[limit_name] -= count","def test_email_warnings_not_sent_out_of_hours(self, mock_tz):\n # payment_due_date 2015/2/11 23:59 (within 24hrs - warnings sent)\n ticketed_event = baker.make_recipe(\n 'booking.ticketed_event_max10',\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\n payment_open=True,\n ticket_cost=10,\n payment_due_date=datetime(2015, 2, 11, tzinfo=dt_timezone.utc),\n )\n\n baker.make(\n TicketBooking, ticketed_event=ticketed_event, paid=False,\n date_booked=datetime(2015, 2, 1, 0, 0, tzinfo=dt_timezone.utc),\n _quantity=5,\n )\n\n _add_user_email_addresses(TicketBooking)\n\n for ticket_booking in TicketBooking.objects.all():\n baker.make(Ticket, ticket_booking=ticket_booking)\n\n mock_tz.now.return_value = datetime(2015, 2, 11, 6, 59, tzinfo=dt_timezone.utc)\n management.call_command('email_ticket_booking_warnings')\n self.assertEqual(len(mail.outbox), 0)\n\n mock_tz.now.return_value = datetime(2015, 2, 11, 22, 5, tzinfo=dt_timezone.utc)\n management.call_command('email_ticket_booking_warnings')\n self.assertEqual(len(mail.outbox), 0)\n\n mock_tz.now.return_value = datetime(2015, 2, 11, 21, 59, tzinfo=dt_timezone.utc)\n management.call_command('email_ticket_booking_warnings')\n self.assertEqual(len(mail.outbox), 5)","def confirm_recent_new_issues(self, msg, hour):\n self._asset_bind(msg)\n yield (\"Processing....\")\n trans = self._translation_util(msg)\n client = self._github_operator(msg)\n cmd = \"repo:{} is:open type:issue\".format(\n task_repository_name())\n issue_list = client.search_issue(cmd, 10)\n end_time = datetime.datetime.now()\n cnt = 0\n for issue in issue_list:\n start_time = issue.created_at\n if end_time - start_time <= datetime.timedelta(hours=hour):\n trans.wait_for_limit(MAX_RESULT, MAX_RESULT)\n confirmed = False\n for label in issue.get_labels():\n if label.name == \"welcome\":\n issue.remove_from_labels(\"welcome\")\n else:\n if label.name in [\"pending\", \"translating\", \"pushed\", \"finished\"]:\n confirmed = True\n if not confirmed:\n issue.add_to_labels(\"pending\")\n cnt += 1\n yield \"{} issues confirmed.\".format(cnt)","def test_reject_proposal_demand(self):\n pass","def take_weekly_agreements():\n\n #collect data from form template\n chore_id = request.form.get(\"chore_id\")\n daysagreed = request.form.get(\"daysagreed\")\n daysagreed = daysagreed.split(\"|\")\n\n #no more unicode\n days_agreed = [str(i) for i in daysagreed]\n\n #recast agreements from T/F to days of the week (by name)\n days_agreed = [days_of_the_week[i] for i in range(7) if days_agreed[i] == 'true']\n\n #format list of daily agreements for addition to database (string)\n days_agreed = \"|\".join(days_agreed) \n\n #save to database\n dbwrangler.add_commitment(days_agreed, chore_id)\n\n #redirect to form for further agreements\n return redirect(\"/takeachore\")","def on_delete(self, req, resp):\n try:\n days_to_retain = int(req.params[\"days\"])\n except Exception:\n days_to_retain = 90\n\n try:\n retention_status = self.state_manager.task_retention(\n retain_days=str(days_to_retain))\n if not retention_status:\n resp.status = falcon.HTTP_404\n return\n resp.text = \"Tables purged successfully.\"\n except Exception as e:\n self.error(req.context, \"Unknown error: %s\" % (str(e)))\n resp.text = \"Unexpected error.\"\n resp.status = falcon.HTTP_500\n return\n resp.status = falcon.HTTP_200","def clean_passes(threshold=misc.get_now_utc()):\n logger.info('>>> Cleaning passes, threshold = ' + str(threshold))\n\n try:\n\n no_deleted = pass_models.PassSlots.objects.filter(\n end__lte=threshold\n ).delete()\n logger.debug('>>> tasks@clean_passes.filtered = ' + str(no_deleted))\n\n except Exception as ex:\n\n logger.exception('>>> Exception cleaning passes, ex = ' + str(ex), ex)\n return\n\n logger.info('>>> DONE cleaning passes')","def send_notifications():\n due_notifications = Notification.query.filter(Notification.delivery_date <= datetime.now(timezone.utc))\n for notification in due_notifications:\n send_notification.delay(notification.id)","def commit_actions(self, period=1, repair_future_actions=False, verbose=False):\n while period < self.horizon:\n if verbose: print('growing period', period)\n self.grow(period, cascade=False)\n period += 1\n if repair_future_actions:\n if verbose: print('repairing actions in period', period)\n self.repair_actions(period)\n else:\n self.reset_actions(period)","def check_msg_release_on_commit(self, broker, ftd_msgs):\n hits = self._get_hits(broker, re.compile(\"debug Message id=\\\"[0-9a-f-]{36}\\\"; pid=0x[0-9a-f]+: \"\n \"Content released on commit$\", re.MULTILINE))\n self._reconsile_hits(broker, ftd_msgs, hits)","def remove_expired_records(self, database): #pylint: disable-msg=R0201\n #special case to ignore eat files that are uploaded every day\n self.remove_eat_upload_records(database)\n \n expiry_time = 20 # 20 in seconds\n aborted_expiry_time = 43200 # 12 hours in seconds\n now = datetime.datetime.utcnow()\n \n for rec in database:\n #finished records\n if rec.get('finished_time_insert', None) and (now - rec['finished_time_insert']) > datetime.timedelta(seconds = expiry_time) :\n #archive finished record\n self.archiver.archive(rec)\n database.delete(rec)\n #aborted for more than 12 hours records \n elif rec.get('aborted', None) and ( now - rec.get('last_update', now) ) > datetime.timedelta(seconds = aborted_expiry_time) :\n #archive aborted record that was never finished\n self.archiver.archive(rec)\n database.delete(rec)"],"string":"[\n \"def maintain_leases(self) -> None:\\n while not self._stop_event.is_set():\\n # Determine the appropriate duration for the lease. This is\\n # based off of how long previous messages have taken to ack, with\\n # a sensible default and within the ranges allowed by Pub/Sub.\\n # Also update the deadline currently used if enough new ACK data has been\\n # gathered since the last deadline update.\\n deadline = self._manager._obtain_ack_deadline(maybe_update=True)\\n _LOGGER.debug(\\\"The current deadline value is %d seconds.\\\", deadline)\\n\\n # Make a copy of the leased messages. This is needed because it's\\n # possible for another thread to modify the dictionary while\\n # we're iterating over it.\\n leased_messages = copy.copy(self._leased_messages)\\n\\n # Drop any leases that are beyond the max lease time. This ensures\\n # that in the event of a badly behaving actor, we can drop messages\\n # and allow the Pub/Sub server to resend them.\\n cutoff = time.time() - self._manager.flow_control.max_lease_duration\\n to_drop = [\\n requests.DropRequest(ack_id, item.size, item.ordering_key)\\n for ack_id, item in leased_messages.items()\\n if item.sent_time < cutoff\\n ]\\n\\n if to_drop:\\n _LOGGER.warning(\\n \\\"Dropping %s items because they were leased too long.\\\", len(to_drop)\\n )\\n assert self._manager.dispatcher is not None\\n self._manager.dispatcher.drop(to_drop)\\n\\n # Remove dropped items from our copy of the leased messages (they\\n # have already been removed from the real one by\\n # self._manager.drop(), which calls self.remove()).\\n for item in to_drop:\\n leased_messages.pop(item.ack_id)\\n\\n # Create a modack request.\\n # We do not actually call `modify_ack_deadline` over and over\\n # because it is more efficient to make a single request.\\n ack_ids = leased_messages.keys()\\n expired_ack_ids = set()\\n if ack_ids:\\n _LOGGER.debug(\\\"Renewing lease for %d ack IDs.\\\", len(ack_ids))\\n\\n # NOTE: This may not work as expected if ``consumer.active``\\n # has changed since we checked it. An implementation\\n # without any sort of race condition would require a\\n # way for ``send_request`` to fail when the consumer\\n # is inactive.\\n assert self._manager.dispatcher is not None\\n ack_id_gen = (ack_id for ack_id in ack_ids)\\n expired_ack_ids = self._manager._send_lease_modacks(\\n ack_id_gen, deadline\\n )\\n\\n start_time = time.time()\\n # If exactly once delivery is enabled, we should drop all expired ack_ids from lease management.\\n if self._manager._exactly_once_delivery_enabled() and len(expired_ack_ids):\\n assert self._manager.dispatcher is not None\\n self._manager.dispatcher.drop(\\n [\\n requests.DropRequest(\\n ack_id,\\n leased_messages.get(ack_id).size, # type: ignore\\n leased_messages.get(ack_id).ordering_key, # type: ignore\\n )\\n for ack_id in expired_ack_ids\\n if ack_id in leased_messages\\n ]\\n )\\n # Now wait an appropriate period of time and do this again.\\n #\\n # We determine the appropriate period of time based on a random\\n # period between:\\n # minimum: MAX_BATCH_LATENCY (to prevent duplicate modacks being created in one batch)\\n # maximum: 90% of the deadline\\n # This maximum time attempts to prevent ack expiration before new lease modacks arrive at the server.\\n # This use of jitter (http://bit.ly/2s2ekL7) helps decrease contention in cases\\n # where there are many clients.\\n # If we spent any time iterating over expired acks, we should subtract this from the deadline.\\n snooze = random.uniform(\\n _MAX_BATCH_LATENCY, (deadline * 0.9 - (time.time() - start_time))\\n )\\n _LOGGER.debug(\\\"Snoozing lease management for %f seconds.\\\", snooze)\\n self._stop_event.wait(timeout=snooze)\\n\\n _LOGGER.debug(\\\"%s exiting.\\\", _LEASE_WORKER_NAME)\",\n \"def __cleanup(self, ttl_in_sec):\\n ttl_in_ms = ttl_in_sec * 1000\\n while True:\\n logging.debug(\\\"cleanup action...\\\")\\n current_ts = self.__current_timestamp_in_ms()\\n self.lock.acquire()\\n for key, value in self.orderedDict.items():\\n if value[1] > current_ts - ttl_in_ms:\\n break\\n else:\\n self.orderedDict.pop(key, None)\\n self.lock.release()\\n time.sleep(ttl_in_sec)\",\n \"def remove_expired(cls):\\n max_trailers = 10\\n current_trailers = cls.get_all(collection='approved_trailers')\\n current_trailers.reverse()\\n queued_trailers = cls.get_all(collection='queued_trailers')\\n\\n if len(current_trailers) >= max_trailers and len(queued_trailers) > 0:\\n for trailer in current_trailers:\\n time_active = trailer.date.timetuple().tm_yday - datetime.now().timetuple().tm_yday\\n if time_active >= 14 and len(queued_trailers) > 0:\\n cls.move(trailer, 'approved_trailers', 'archived_trailers')\\n cls.move(queued_trailers[0], 'queued_trailers', 'approved_trailers')\",\n \"def trigger_expiration_notices(at_time=None, nb_days=15, dry_run=False):\\n\\n def _handle_organization_notices(organization):\\n if organization.processor_card_key:\\n card = organization.retrieve_card()\\n try:\\n exp_month, exp_year = card['exp_date'].split('/')\\n exp_date = datetime(year=int(exp_year),\\n month=int(exp_month), day=1, tzinfo=at_time.tzinfo)\\n if lower >= exp_date:\\n LOGGER.info(\\\"payment method expires soon for %s\\\",\\n organization)\\n if not dry_run:\\n signals.card_expires_soon.send(\\n sender=__name__, organization=organization,\\n nb_days=nb_days)\\n except (KeyError, ValueError):\\n # exp info is missing or the format is incorrect\\n pass\\n else:\\n LOGGER.info(\\\"%s doesn't have a payment method attached\\\",\\n organization)\\n if not dry_run:\\n signals.payment_method_absent.send(sender=__name__,\\n organization=organization)\\n\\n at_time = datetime_or_now(at_time)\\n lower = at_time + relativedelta(days=nb_days)\\n upper = at_time + relativedelta(days=nb_days + 1)\\n LOGGER.info(\\n \\\"trigger notifications for subscription expiring within [%s,%s[ ...\\\",\\n lower, upper)\\n prev_organization = None\\n subscription = None\\n for subscription in Subscription.objects.valid_for(ends_at__gte=lower,\\n ends_at__lt=upper).order_by('organization'):\\n org = subscription.organization\\n plan = subscription.plan\\n\\n try:\\n if subscription.auto_renew:\\n if plan.renewal_type == plan.AUTO_RENEW:\\n if org.id != prev_organization:\\n _handle_organization_notices(org)\\n\\n prev_organization = org.id\\n else:\\n if plan.renewal_type == plan.ONE_TIME:\\n LOGGER.info(\\\"trigger upgrade soon for %s\\\", subscription)\\n if not dry_run:\\n signals.subscription_upgrade.send(sender=__name__,\\n subscription=subscription, nb_days=nb_days)\\n\\n elif plan.renewal_type == plan.REPEAT:\\n LOGGER.info(\\\"trigger expires soon for %s\\\", subscription)\\n if not dry_run:\\n signals.expires_soon.send(sender=__name__,\\n subscription=subscription, nb_days=nb_days)\\n\\n except Exception as err: #pylint:disable=broad-except\\n # We use `Exception` because the email server might be\\n # unavailable but ConnectionRefusedError is not a subclass\\n # of RuntimeError.\\n LOGGER.exception(\\\"error: %s\\\", err)\\n\\n # flushing the last organization\\n if subscription and subscription.organization.id != prev_organization:\\n if subscription.auto_renew:\\n if subscription.plan.renewal_type == subscription.plan.AUTO_RENEW:\\n _handle_organization_notices(subscription.organization)\",\n \"def _reconsile_hits(self, broker, ftd_msgs, release_hits):\\n for msg in ftd_msgs:\\n found = False\\n for hit in release_hits:\\n if str(msg.id) in hit:\\n release_hits.remove(hit)\\n #print \\\"Found %s in %s\\\" % (msg.id, broker.log)\\n found = True\\n break\\n if not found:\\n self.assert_(False, \\\"Unable to locate released message %s in log %s\\\" % (msg.id, broker.log))\\n if len(release_hits) > 0:\\n err = \\\"Messages were unexpectedly released in log %s:\\\\n\\\" % broker.log\\n for hit in release_hits:\\n err += \\\" %s\\\\n\\\" % hit\\n self.assert_(False, err)\",\n \"def test_penalty_is_made_on_delay(self):\\n twenty_days_ago = timezone.now() - timezone.timedelta(days=20)\\n borrow = Borrow.objects.create(\\n book_id=1,\\n student=self.students[0],\\n requested_at=twenty_days_ago,\\n borrowed_at=twenty_days_ago,\\n duration=10,\\n )\\n client = APIClient()\\n client.login(username=self.manager.username, password=\\\"salam*123\\\")\\n client.post(\\\"/borrows/1/terminate/\\\", data={\\\"duration\\\": 5})\\n borrow.refresh_from_db()\\n self.assertIsNotNone(borrow.returned_at)\\n self.assertIsNotNone(borrow.delaypenalty)\\n self.assertEqual(borrow.delaypenalty.amount, 11 * 1000)\",\n \"def revise_agreements_expirations_planned(self):\\n for agreement in self.search([('prolong', '=', 'unlimited')]):\\n if agreement.next_expiration_date <= fields.Date.today():\\n # force recalculate next_expiration_date\\n agreement.write({'prolong': 'unlimited'})\\n return True\",\n \"def prune_out_of_date_queuing_delays(self, global_clock_sec):\\n # Replacement deque\\n new_queuing_delays = deque()\\n for exit_time, queuing_delay in list(self.queuing_delays):\\n time_since_exit = global_clock_sec - exit_time\\n assert time_since_exit >= 0.0\\n\\n if time_since_exit <= LinkBuffer.MAX_DATA_AGE_SEC:\\n new_queuing_delays.append((exit_time, queuing_delay))\\n\\n self.queuing_delays = new_queuing_delays\",\n \"def cleanup_expired_dev_entries(**kwargs):\\n logging.debug(\\\"In the cleanup_expired_dev_entries() method.\\\")\\n logging.info(\\\"Checking for expired Developer Objects.\\\")\\n\\n # Get all rules for this ACP.\\n\\n all_acp_rules = ACPRule(fmc=fmc1, acp_name=acp_name)\\n all_rules = all_acp_rules.get()\\n if all_rules.get('items', '') is '':\\n logging.warning(\\\"No rules found for Access Control Policy: {}.\\\".format(kwargs['acp_name']))\\n else:\\n for item in all_rules['items']:\\n if 'Dev-' in item['name']:\\n namesplit = item['name'].split('-')\\n if int(namesplit[2]) < kwargs['threshold_time']:\\n logging.info(\\\"Deleting {} rule from {}.\\\".format(item['name'], kwargs['acp_name']))\\n tmp_rule = None\\n tmp_rule = ACPRule(fmc=fmc1, acp_name=acp_name)\\n tmp_rule.get(name=item['name'])\\n tmp_rule.delete()\\n # Now Delete any expired Host objects.\\n all_ips = IPAddresses(fmc=fmc1)\\n all_hosts = all_ips.get()\\n for item in all_hosts['items']:\\n if 'Dev-' in item['name']:\\n namesplit = item['name'].split('-')\\n if int(namesplit[2]) < kwargs['threshold_time']:\\n logging.info(\\\"Deleting {} host object.\\\".format(item['name']))\\n tmp_rule = None\\n tmp_rule = IPHost(fmc=fmc1)\\n tmp_rule.get(name=item['name'])\\n tmp_rule.delete()\\n # Finally Delete any expired Port objects.\\n all_ports = ProtocolPort(fmc=fmc1)\\n response = all_ports.get()\\n for item in response['items']:\\n if 'Dev-' in item['name']:\\n namesplit = item['name'].split('-')\\n if int(namesplit[2]) < kwargs['threshold_time']:\\n logging.info(\\\"Deleting {} port object.\\\".format(item['name']))\\n tmp_rule = None\\n tmp_rule = ProtocolPort(fmc=fmc1)\\n tmp_rule.get(name=item['name'])\\n tmp_rule.delete()\",\n \"def status_check(self):\\n from coordinator.tasks import cancel_release\\n # Check if we hit the time limit\\n last_update = self.events.order_by('-created_at')\\\\\\n .first().created_at\\n diff = datetime.datetime.utcnow() - last_update.replace(tzinfo=None)\\n\\n if diff.total_seconds() > settings.RELEASE_TIMEOUT:\\n if self.state == 'canceling':\\n return\\n logger.error(f'canceling release {self.kf_id} for time out.')\\n self.cancel()\\n self.save()\\n django_rq.enqueue(cancel_release, self.kf_id)\\n return\\n\\n # Check if any contained tasks have failed/canceled\\n for task in self.tasks.all():\\n if task.state in ['failed', 'canceled', 'rejected']:\\n if self.state == 'canceling':\\n return\\n logger.error(f'canceling release: {self.kf_id} task is ' +\\n f'{task.state}')\\n self.cancel()\\n self.save()\\n django_rq.enqueue(cancel_release, self.kf_id)\\n return\",\n \"def DissolutionAfterReinstatement(obj):\\n if (obj.dissolution_date is None) or (obj.reinstatement_date is None): \\n return\\n if obj.dissolution_date > obj.reinstatement_date:\\n raise interface.Invalid(\\n _(\\\"A committee must be disolved before it can be reinstated\\\"), \\n \\\"dissolution_date\\\", \\n \\\"reinstatement_date\\\")\",\n \"def test_debt_target_not_expired_when_new_issue_added(self):\\n measurement = self.measurement(\\n self.metric(accept_debt=True, debt_target=\\\"100\\\", issue_ids=[\\\"FOO-41\\\", \\\"FOO-42\\\"]),\\n count={\\\"debt_target\\\": \\\"100\\\"},\\n issue_status=[{\\\"status_category\\\": \\\"done\\\", \\\"issue_id\\\": \\\"FOO-41\\\"}],\\n )\\n self.assertFalse(measurement.debt_target_expired())\",\n \"def test_order_ejection_4_hours_extended_24(self):\\n manager = EjectionManager(self.user)\\n curr_time = self.curr_time\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=61))\\n self.assertEqual(self.order.number_of_ejection_notices, 1)\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=121))\\n self.assertEqual(self.order.number_of_ejection_notices, 2)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=121))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=181))\\n self.assertEqual(self.order.number_of_ejection_notices, 3)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=181))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=211))\\n self.assertEqual(self.order.number_of_ejection_notices, 4)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=211))\\n\\n OrderUpdate.objects.create(\\n order=self.order,\\n creator=self.user,\\n time_limit_delta=timedelta(hours=24))\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=211, seconds=1))\\n\\n curr_time = curr_time + timedelta(minutes=211, seconds=1)\\n # Now that we've extended the Order by 24 hours left to go, we should\\n # start getting periodic notifications\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=31))\\n self.assertEqual(self.order.number_of_ejection_notices, 5)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=31))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=511))\\n self.assertEqual(self.order.number_of_ejection_notices, 6)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=511))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=991))\\n self.assertEqual(self.order.number_of_ejection_notices, 7)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=991))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=1231))\\n self.assertEqual(self.order.number_of_ejection_notices, 8)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=1231))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=1351))\\n self.assertEqual(self.order.number_of_ejection_notices, 9)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=1351))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=1411))\\n self.assertEqual(self.order.number_of_ejection_notices, 10)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=1411))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=1441))\\n self.assertEqual(self.order.number_of_ejection_notices, 11)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=1441))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=1456))\\n self.assertEqual(self.order.number_of_ejection_notices, 12)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=1456))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=1471))\\n self.assertEqual(self.order.number_of_ejection_notices, 12)\\n self.assertEqual(self.order.status, Order.STATUS_CLOSED)\",\n \"def check_msg_release(self, broker, ftd_msgs):\\n hits = self._get_hits(broker, re.compile(\\\"debug Message id=\\\\\\\"[0-9a-f-]{36}\\\\\\\"; pid=0x[0-9a-f]+: \\\"\\n \\\"Content released$\\\", re.MULTILINE))\\n self._reconsile_hits(broker, ftd_msgs, hits)\",\n \"def ssl_expires_in(entity, serial_number, remaining, buffer_days=14):\\n # if the cert expires in less than two weeks, we should reissue it\\n if remaining < datetime.timedelta(days=0):\\n # cert has already expired - uhoh!\\n print(\\\"Cert %s issued to '%s' expired %s days ago!\\\"\\n % (serial_number, entity, remaining.days))\\n elif remaining < datetime.timedelta(days=buffer_days):\\n # expires sooner than the buffer\\n print(\\\"Cert %s issued to '%s' is nearly expired - %s more days\\\"\\n % (serial_number, entity, remaining.days))\\n else:\\n # everything is fine\\n print(\\\"Cert %s issued to '%s' is valid for %s more days\\\"\\n % (serial_number, entity, remaining.days))\",\n \"def __removing_loop(self) -> None:\\r\\n\\r\\n # repeat until stop flag is set\\r\\n while not self.__stopper.wait(self.CLEANUP_EXPIRED_INTERVAL):\\r\\n now = int(datetime.now(self.__tz).timestamp())\\r\\n log.debug('Removing...')\\r\\n\\r\\n # iterate through database and remove expired encounters\\r\\n for enc_id, despawn_time in self.__pokes_db.copy().items():\\r\\n if despawn_time - now < 5:\\r\\n del self.__pokes_db[enc_id]\",\n \"def _cleanupDownCounts(self):\\n now = datetime.now()\\n timeout = timedelta(minutes=DOWN_COUNT_TIMEOUT_MINUTES)\\n for taskName, (down_count, last_time) in self._down_counts.iteritems():\\n if now - last_time > timeout:\\n del self._down_counts[taskName]\",\n \"def release_budget(self, plan_id, amount):\\n budget_line = self.search([('id', '=', plan_id)])\\n if float_compare(amount, budget_line.planned_amount, 2) == 1:\\n raise ValidationError(\\n _('Release amount exceed planned amount!'))\\n budget_line.write({'released_amount': amount})\\n budget_line.project_id._trigger_auto_sync()\\n return True\",\n \"def test_maxttl_greater_doc_expiry(self):\\n for bucket in self.buckets:\\n self._load_json(bucket, self.num_items, exp=int(self.maxttl)-100)\\n self.sleep(int(self.maxttl-100), \\\"waiting for all docs to expire per maxTTL rule...\\\")\\n self.expire_pager(self.servers)\\n self.sleep(20, \\\"waiting for item count to come down...\\\")\\n for bucket in self.buckets:\\n items = RestConnection(self.master).get_active_key_count(bucket)\\n self.log.info(\\\"Doc expiry set to = {0}s, maxTTL = {1}s, after {2}s, item count = {3}\\\".format(\\n int(self.maxttl) - 100,\\n self.maxttl-100,\\n self.maxttl-100,\\n items))\\n if items == 0:\\n self.log.info(\\\"SUCCESS: Docs with lesser expiry deleted\\\")\\n else:\\n self.fail(\\\"FAIL: Doc with lesser expiry still present past ttl\\\")\",\n \"def renew_close_to_expiration(self, margin_in_seconds=A_DAY):\\n subscriptions = self.storage.close_to_expiration(margin_in_seconds)\\n for subscription in subscriptions:\\n try:\\n self.subscribe_impl(**subscription)\\n except SubscriberError as e:\\n warn(RENEW_FAILURE % (subscription['topic_url'],\\n subscription['callback_id']), e)\",\n \"def test_debt_target_expired(self):\\n measurement = self.measurement(\\n self.metric(accept_debt=True, debt_target=\\\"100\\\", issue_ids=[\\\"FOO-40\\\"]),\\n count={\\\"debt_target\\\": \\\"100\\\"},\\n issue_status=[{\\\"status_category\\\": \\\"done\\\", \\\"issue_id\\\": \\\"FOO-40\\\"}],\\n )\\n self.assertTrue(measurement.debt_target_expired())\",\n \"def update_validity(sender, instance, **kwargs):\\n\\tinstance.up_to_date = True\\n\\n\\tfor sol in instance.post_set.all():\\n\\t\\tsol.up_to_date = False\\n\\t\\tsol.save()\",\n \"def check_for_canceled(self, index):\\n if 0 <= index < len(self.stops):\\n stop = self.stops[index]\\n if stop.status == StopStatuses.planned or stop.status == StopStatuses.announced:\\n self.issue_time += timedelta(seconds=config.INTERVAL_BETWEEN_UPDATE_MSG)\\n self.tasks.append(self.instruction_task(stop.station_url, 'prio', self.issue_time))\",\n \"def check_due(self, objects, time=None):\\n time = time or datetime.utcnow()\\n recurrences = self.recurrenceforobject_set.prefetch_related('content_object').all()\\n all_scheduled_objs = set(r.content_object for r in recurrences)\\n past_due = set(r.content_object for r in recurrences.filter(next_scheduled__lt=time))\\n\\n due = []\\n for obj in objects:\\n if obj in all_scheduled_objs and obj in past_due:\\n due.append(obj)\\n elif obj not in all_scheduled_objs:\\n due.append(obj)\\n self._sub_recurrence(obj)\\n return due\",\n \"def clean_reports():\\n logger.info('Cleaning reports older than %d days' % lgr_settings.report_expiration_delay)\\n # set the last run in the settings data\\n lgr_settings.report_expiration_last_run = timezone.now()\\n lgr_settings.save()\\n nbr, __ = LGRReport.objects.filter(\\n created_at__lt=datetime.datetime.now() - datetime.timedelta(days=lgr_settings.report_expiration_delay)\\n ).delete()\\n logger.info('%d reports removed' % nbr)\",\n \"def release_unclaimed_goods():\\n print('releasing unclaimed goods...')\\n released = 0\\n goods = db.session.query(Good).filter(Good.tx_hash == None).filter(Good.order_id != None).all()\\n for good in goods:\\n from .order import has_expired # don't move me to prevent cyclical deps\\n if has_expired(good.order_id):\\n release_good(good.order_id)\\n released = released + 1\\n\\n log.info('released %s goods' % released)\\n return released\",\n \"def reap_stale_submissions():\\n\\n print(\\\"Reaping stale submissions\\\")\\n\\n # Find and update stale submissions\\n Submission.query.filter(\\n Submission.last_updated < datetime.now() - timedelta(minutes=60),\\n Submission.processed == False,\\n Submission.state != 'regrading',\\n ).update({\\n 'processed': True,\\n 'state': \\\"Reaped after timeout\\\",\\n }, False)\\n\\n # Commit any changes\\n db.session.commit()\",\n \"def adjustableDelay(delay, ants) :\\n antlist = helpers.makeList(ants)\\n s.adjustableDelay(delay, antlist)\",\n \"def remind_all_finally_to_update_release_notes(self):\\n names = read_from_file(self.pr.config.releaseItemsFileMergedBy)\\n LOG.debug(\\\"final names list =\\\" + names)\\n if names:\\n time.sleep(10)\\n\\n msg = RELEASE_NOTES_REMINDER.format(msg=names, release_notes_link=self.pr.config.release_notes_link,\\n qa_team=self.pr.config.qaTeamMembers)\\n self.slack.postToSlack(channel=self.pr.config.alertChannelName, msg=msg)\\n self.clean_up_for_next_cycle()\",\n \"def start_lease_expiry_timer(self, ack_ids: Iterable[str]) -> None:\\n with self._add_remove_lock:\\n for ack_id in ack_ids:\\n lease_info = self._leased_messages.get(ack_id)\\n # Lease info might not exist for this ack_id because it has already\\n # been removed by remove().\\n if lease_info:\\n self._leased_messages[ack_id] = lease_info._replace(\\n sent_time=time.time()\\n )\",\n \"def do(self):\\n self.message += 'Searching for unpayed loans \\\\n'\\n today = dt.now()\\n tomorrow = today + timedelta(days=1)\\n\\n expired = Loan.objects.filter(due_date=today)\\n almost = Loan.objects.filter(due_date=tomorrow)\\n\\n self.charge(expired)\\n self.notify(almost)\\n\\n self.log_success()\",\n \"def check_auto_reject(self):\\r\\n for pr in self:\\r\\n if not pr.line_ids.filtered(lambda l: l.cancelled is False):\\r\\n pr.write({'state': 'rejected'})\",\n \"def test_form_invalid_date_more_4_weeks(self):\\n login = self.client.login(\\n username='testuser2',\\n password='2HJ1vRV0Z&3iD')\\n future_date = datetime.date.today()\\\\\\n + datetime.timedelta(weeks=4, days=1)\\n response = self.client.post(\\n reverse('librarian-renew-book',\\n kwargs={'pk': self.test_bookinstance1.pk}),\\n {'due_back': future_date})\\n self.assertEqual(response.status_code, 200)\\n self.assertFormError(response,\\n 'form',\\n 'due_back',\\n 'Invalid date - renewal more than 4 weeks a ahead')\",\n \"async def process(self, timeout=60):\\n\\n previous_date = self.previous_date()\\n new_date = previous_date\\n last_sent_message_date = previous_date\\n now = pendulum.now('UTC')\\n\\n self.log.info(\\\"Begining processing feed %s, previous date %s\\\",\\n self.name, previous_date)\\n\\n for entry in await self.fetch_and_parse(timeout):\\n\\n pubdate = dateutil.parser.parse(entry.published, tzinfos=rssalertbot.BOGUS_TIMEZONES)\\n entry.published = pendulum.from_timestamp(pubdate.timestamp())\\n # also save a prettified string format\\n entry.datestring = self.format_timestamp_local(entry.published)\\n\\n # skip anything that's stale\\n if entry.published <= previous_date:\\n continue\\n\\n event_id = md5((entry.title + entry.description).encode()).hexdigest()\\n last_sent = self.storage.load_event(self.feed, event_id)\\n re_alert = self.cfg.get('re_alert', rssalertbot.RE_ALERT_DEFAULT)\\n should_delete_message = False\\n\\n if entry.published > now:\\n if last_sent and now < last_sent.add(hours=re_alert):\\n continue\\n self.storage.save_event(self.feed, event_id, now)\\n else:\\n if entry.published > new_date:\\n new_date = entry.published\\n should_delete_message = last_sent\\n\\n self.log.debug(\\\"Found new entry %s\\\", entry.published)\\n\\n # alert on it\\n await self.alert(entry)\\n if new_date > last_sent_message_date:\\n self.storage.save_date(self.feed, new_date)\\n last_sent_message_date = new_date\\n\\n if should_delete_message:\\n self.log.debug(f\\\"Deleting stored date for message {event_id}\\\")\\n self.storage.delete_event(self.feed, event_id)\\n\\n self.log.info(\\\"End processing feed %s, previous date %s\\\", self.name, new_date)\",\n \"def _deactivate_campaigns(self):\\n now = timezone.now()\\n for campaign in self.campaigns.active():\\n campaign.until = now\\n campaign.save(update_fields=['until'])\",\n \"def killQueueOrder(self):\\n # CHECK ALL QUEUE ORDERS AND CANCEL ORDER IF GREATER THAN TWO HOURS OLD\\n queue_orders = self.queue.find(\\n {\\\"Trader\\\": self.user[\\\"Name\\\"], \\\"Asset_Type\\\": self.asset_type, \\\"Account_ID\\\": self.account_id})\\n\\n dt = datetime.now(tz=pytz.UTC).replace(microsecond=0)\\n\\n dt_central = dt.astimezone(pytz.timezone('US/Central'))\\n\\n two_hours_ago = datetime.strptime(datetime.strftime(\\n dt_central - timedelta(hours=2), \\\"%Y-%m-%d %H:%M:%S\\\"), \\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n ten_minutes_ago = datetime.strptime(datetime.strftime(\\n dt_central - timedelta(minutes=10), \\\"%Y-%m-%d %H:%M:%S\\\"), \\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n for order in queue_orders:\\n\\n order_date = order[\\\"Date\\\"]\\n\\n order_type = order[\\\"Order_Type\\\"]\\n\\n id = order[\\\"Order_ID\\\"]\\n\\n forbidden = [\\\"REJECTED\\\", \\\"CANCELED\\\", \\\"FILLED\\\"]\\n\\n if two_hours_ago > order_date and (order_type == \\\"BUY\\\" or order_type == \\\"BUY_TO_OPEN\\\") and id != None and order[\\\"Order_Status\\\"] not in forbidden:\\n\\n # FIRST CANCEL ORDER\\n resp = self.tdameritrade.cancelOrder(id)\\n\\n if resp.status_code == 200 or resp.status_code == 201:\\n\\n other = {\\n \\\"Symbol\\\": order[\\\"Symbol\\\"],\\n \\\"Order_Type\\\": order[\\\"Order_Type\\\"],\\n \\\"Order_Status\\\": \\\"CANCELED\\\",\\n \\\"Strategy\\\": order[\\\"Strategy\\\"],\\n \\\"Account_ID\\\": self.account_id,\\n \\\"Aggregation\\\": order[\\\"Aggregation\\\"],\\n \\\"Trader\\\": self.user[\\\"Name\\\"],\\n \\\"Date\\\": getDatetime()\\n }\\n\\n if self.asset_type == \\\"OPTION\\\":\\n\\n other[\\\"Pre_Symbol\\\"] = order[\\\"Pre_Symbol\\\"]\\n\\n other[\\\"Exp_Date\\\"] = order[\\\"Exp_Date\\\"]\\n\\n self.other.insert_one(other)\\n\\n self.queue.delete_one(\\n {\\\"Trader\\\": self.user[\\\"Name\\\"], \\\"Symbol\\\": order[\\\"Symbol\\\"], \\\"Strategy\\\": order[\\\"Strategy\\\"], \\\"Asset_Type\\\": self.asset_type})\\n\\n self.logger.INFO(\\n f\\\"CANCELED ORDER FOR {order['Symbol']} - TRADER: {self.user['Name']}\\\", True)\\n\\n # IF QUEUE ORDER DATE GREATER THAN 10 MINUTES OLD AND ORDER ID EQUALS NONE, SEND ALERT\\n if ten_minutes_ago > order_date and order[\\\"Order_ID\\\"] == None and order[\\\"Account_ID\\\"] == self.account_id:\\n\\n if order[\\\"Symbol\\\"] not in self.no_ids_list:\\n\\n self.logger.ERROR(\\n \\\"QUEUE ORDER ID ERROR\\\", f\\\"ORDER ID FOR {order['Symbol']} NOT FOUND - TRADER: {self.user['Name']} - ACCOUNT ID: {self.account_id}\\\")\\n\\n self.no_ids_list.append(order[\\\"Symbol\\\"])\\n\\n else:\\n\\n if order[\\\"Symbol\\\"] in self.no_ids_list:\\n\\n self.no_ids_list.remove(order[\\\"Symbol\\\"])\",\n \"def test_expiration_banner_with_expired_upgrade_deadline(self):\\n past = datetime(2010, 1, 1, tzinfo=UTC)\\n CourseDurationLimitConfig.objects.create(enabled=True, enabled_as_of=past)\\n course = CourseFactory.create(start=now() - timedelta(days=10))\\n CourseModeFactory.create(course_id=course.id, mode_slug=CourseMode.AUDIT)\\n CourseModeFactory.create(course_id=course.id, mode_slug=CourseMode.VERIFIED, expiration_datetime=past)\\n user = UserFactory(password=self.TEST_PASSWORD)\\n self.client.login(username=user.username, password=self.TEST_PASSWORD)\\n CourseEnrollment.enroll(user, course.id, mode=CourseMode.AUDIT)\\n\\n url = course_home_url(course)\\n response = self.client.get(url)\\n bannerText = get_expiration_banner_text(user, course)\\n self.assertContains(response, bannerText, html=True)\\n self.assertContains(response, TEST_BANNER_CLASS)\",\n \"def _check_timeouts(self):\\n\\n expired_tokens = []\\n for token in self._capability_timeouts:\\n interval = datetime.utcnow() - self._capability_timeouts[token]\\n if interval.total_seconds() >= 10:\\n expired_tokens.append(token)\\n\\n for token in expired_tokens:\\n cap_withdraw = mplane.model.Withdrawal(capability=self._capabilities[token])\\n self.handle_message(cap_withdraw, self.identity_for(token))\",\n \"def test_notify_delay_not_elapsed(self):\\n # self.client.force_authenticate(user=self.admin)\\n\\n self.wait_queue_notif = WaitQueueNotification.objects.create(\\n user=self.user2,\\n retreat=self.retreat,\\n )\\n\\n response = self.client.get(\\n '/'.join([\\n reverse('retreat:waitqueuenotification-list'),\\n 'notify',\\n ])\\n )\\n\\n self.assertEqual(\\n response.status_code,\\n status.HTTP_200_OK,\\n response.content,\\n )\\n\\n response_data = json.loads(response.content)\\n\\n content = {\\n 'detail': 'Last notification was sent less than 24h ago.'\\n }\\n\\n self.assertEqual(response_data, content)\",\n \"def test_revoke_stops(self):\\n # Load sample data:\\n TSStation.update_stations('TestTAMission.data/stations.xml')\\n TASeries.import_xml('TestTAMission.data/series.xml')\\n\\n mission = TAMission.get('nl.3046')\\n mission.activate_mission(mark_cet(datetime(2013, 2, 18, 2)))\\n self.assertEqual(len(mission.stops), 5)\\n self.assertEqual(mission.origin_id, 'nl.ah')\\n self.assertEqual(mission.destination_id, 'nl.asd')\\n mission.put()\\n\\n # STEP 10a revoke Ede\\n self.post_stops_from_file('TestTAMission.data/step_10_10a.json')\\n mission = TAMission.get('nl.3046')\\n self.assertEqual(len(mission.stops), 4)\\n self.assertEqual(mission.origin_id, 'nl.ah')\\n self.assertEqual(mission.destination_id, 'nl.asd')\\n\\n # ...and set a delay for Arnhem - this must not cause an error when revoking Arnhem\\n self.assertEqual(mission.first_stop.station_id, 'nl.ah')\\n self.assertEqual(mission.first_stop.delay_dep, 15.0)\\n\\n # STEP 10b revoke Arnhem\\n self.post_stops_from_file('TestTAMission.data/step_10_10b.json')\\n mission = TAMission.get('nl.3046')\\n self.assertEqual(len(mission.stops), 3)\\n self.assertEqual(mission.origin_id, 'nl.klp')\\n\\n # STEP 10c revoke Utrecht and Veenendaal De Klomp\\n self.post_stops_from_file('TestTAMission.data/step_10_10c.json')\\n mission = TAMission.get('nl.3046')\\n self.assertEqual(len(mission.stops), 0)\\n self.assertEqual(mission.origin_id, None)\\n self.assertEqual(mission.destination_id, None)\\n\\n mission = TAMission.get('nl.3046')\\n mission.activate_mission(mark_cet(datetime(2013, 2, 19, 2)))\\n self.assertEqual(len(mission.stops), 8)\\n self.assertEqual(mission.origin_id, 'nl.ah')\\n self.assertEqual(mission.destination_id, 'nl.amr')\\n mission.put()\\n\\n # STEP 10d reset Arnhem\\n self.post_stops_from_file('TestTAMission.data/step_10_10d.json')\\n mission = TAMission.get('nl.3046')\\n self.assertEqual(len(mission.stops), 5)\\n self.assertEqual(mission.origin_id, 'nl.ah')\\n self.assertEqual(mission.destination_id, 'nl.asd')\\n self.assertEqual(mission.stops[4].status, StopStatuses.finalDestination)\",\n \"def test_email_warnings_only_sent_for_unpaid(self, mock_tz):\\n mock_tz.now.return_value = datetime(\\n 2015, 2, 11, 10, 0, tzinfo=dt_timezone.utc\\n )\\n\\n # payment_due_date 2015/2/11 23:59 (within 24hrs - warnings sent)\\n ticketed_event = baker.make_recipe(\\n 'booking.ticketed_event_max10',\\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\\n payment_open=True,\\n ticket_cost=10,\\n payment_due_date=datetime(2015, 2, 11, tzinfo=dt_timezone.utc),\\n )\\n\\n baker.make(\\n TicketBooking, ticketed_event=ticketed_event, paid=False,\\n date_booked=datetime(2015, 2, 1, 0, 0, tzinfo=dt_timezone.utc),\\n _quantity=5,\\n )\\n baker.make(\\n TicketBooking, ticketed_event=ticketed_event, paid=True,\\n date_booked=datetime(2015, 2, 1, 0, 0, tzinfo=dt_timezone.utc),\\n _quantity=5,\\n )\\n _add_user_email_addresses(TicketBooking)\\n for ticket_booking in TicketBooking.objects.all():\\n baker.make(Ticket, ticket_booking=ticket_booking)\\n\\n management.call_command('email_ticket_booking_warnings')\\n self.assertEqual(len(mail.outbox), 5)\\n for ticket_booking in TicketBooking.objects.filter(paid=False):\\n self.assertTrue(ticket_booking.warning_sent)\\n for ticket_booking in TicketBooking.objects.filter(paid=True):\\n self.assertFalse(ticket_booking.warning_sent)\",\n \"def test_dont_cancel_bookings_within_cancellation_period_without_warning_sent(self, mock_tz):\\n mock_tz.now.return_value = datetime(2015, 2, 10, 18, 0, tzinfo=dt_timezone.utc)\\n # reset warning flags\\n self.unpaid.warning_sent = False\\n self.unpaid.date_warning_sent = None\\n self.unpaid.save()\\n\\n self.assertEqual(self.unpaid.status, 'OPEN')\\n self.assertFalse(self.unpaid.warning_sent)\\n self.assertIsNone(self.unpaid.date_warning_sent)\\n management.call_command('cancel_unpaid_bookings')\\n self.unpaid.refresh_from_db()\\n # still open\\n self.assertEqual(self.unpaid.status, 'OPEN')\\n\\n # set the warning sent flag to < 2hrs ago\\n self.unpaid.warning_sent = True\\n self.unpaid.date_warning_sent = datetime(2015, 2, 10, 17, 0, tzinfo=dt_timezone.utc)\\n self.unpaid.save()\\n management.call_command('cancel_unpaid_bookings')\\n self.unpaid.refresh_from_db()\\n # still open\\n self.assertEqual(self.unpaid.status, 'OPEN')\\n\\n # set the warning sent flag to > 2hrs ago\\n self.unpaid.warning_sent = True\\n self.unpaid.date_warning_sent = datetime(2015, 2, 10, 15, 0, tzinfo=dt_timezone.utc)\\n self.unpaid.save()\\n management.call_command('cancel_unpaid_bookings')\\n self.unpaid.refresh_from_db()\\n # now cancelled\\n self.assertEqual(self.unpaid.status, 'CANCELLED')\",\n \"def test_maxttl_lesser_doc_expiry(self):\\n for bucket in self.buckets:\\n self._load_json(bucket, self.num_items, exp=int(self.maxttl)+500)\\n self.sleep(int(self.maxttl), \\\"waiting for all docs to expire per maxTTL rule...\\\")\\n self.expire_pager(self.servers)\\n self.sleep(20, \\\"waiting for item count to come down...\\\")\\n for bucket in self.buckets:\\n items = RestConnection(self.master).get_active_key_count(bucket)\\n RestConnection(self.master).get_active_key_count(bucket)\\n self.log.info(\\\"Doc expiry set to = {0}s, maxTTL = {1}s, after {2}s, item count = {3}\\\".format(\\n int(self.maxttl) + 500,\\n self.maxttl,\\n self.maxttl,\\n items))\\n if items > 0:\\n self.fail(\\\"Bucket maxTTL of {0} is not honored\\\".format(self.maxttl))\\n else:\\n self.log.info(\\\"SUCCESS: Doc expiry set to = {0}s, maxTTL = {1}s, after {2}s, item count = {3}\\\".format(\\n int(self.maxttl) + 500,\\n self.maxttl,\\n self.maxttl,\\n items))\",\n \"def check_msg_release_on_recover(self, broker, ftd_msgs):\\n hits = self._get_hits(broker, re.compile(\\\"debug Message id=\\\\\\\"[0-9a-f-]{36}\\\\\\\"; pid=0x[0-9a-f]+: \\\"\\n \\\"Content released after recovery$\\\", re.MULTILINE))\\n self._reconsile_hits(broker, ftd_msgs, hits)\",\n \"def test_pypi_py2py3_expired_major_grace_period(self):\\n obsolete_dep_info = dict(UP_TO_DATE_DEPS)\\n obsolete_dep_info['google-auth'] = {\\n 'current_time': datetime.datetime(2019, 3, 23, 0, 0, 0),\\n 'installed_version': '0.9.9',\\n 'installed_version_time': datetime.datetime(\\n 2019, 2, 19, 21, 15, 56),\\n 'is_latest': False,\\n 'latest_version': '1.0.0',\\n 'latest_version_time': datetime.datetime(2019, 2, 19, 21, 15, 56)\\n }\\n\\n obsolete_dep_compat_results = list(RECENT_SUCCESS_DATA)\\n obsolete_dep_compat_results.remove(GOOGLE_API_CORE_RECENT_SUCCESS_2)\\n obsolete_dep_compat_results.remove(GOOGLE_API_CORE_RECENT_SUCCESS_3)\\n obsolete_dep_compat_results.append(\\n GOOGLE_API_CORE_RECENT_SUCCESS_2.with_updated_dependency_info(\\n obsolete_dep_info))\\n obsolete_dep_compat_results.append(\\n GOOGLE_API_CORE_RECENT_SUCCESS_3.with_updated_dependency_info(\\n obsolete_dep_info))\\n\\n self.fake_store.save_compatibility_statuses(\\n obsolete_dep_compat_results)\\n package_name = 'google-api-core'\\n self.assertImageResponsePyPI(package_name)\\n self.assertTargetResponse(\\n package_name, self.expired_major_grace_period_expected_details)\",\n \"def invalidate_artifacts(buf0, t_reject=T_REJECT,\\n n_reject=N_REJECT, verbose=True):\\n ti_all = [(b['timestamp'], i) for i, b in enumerate(buf0)]\\n ti_all = sorted(ti_all)\\n t_all = np.array([t[0] for t in ti_all])\\n i_all = [t[1] for t in ti_all]\\n\\n nb = len(buf0)\\n ri = range(nb)\\n i = 0\\n while i < nb - 1:\\n ii = []\\n t0 = t_all[i]\\n for j in xrange(i + 1, nb):\\n if t_all[j] < t0 + t_reject:\\n ii.append(j)\\n else:\\n break\\n i = j\\n\\n if len(ii) < n_reject:\\n continue\\n for ix in ii:\\n try:\\n ri.remove(i_all[ix])\\n except ValueError:\\n pass\\n\\n buf = [buf0[i] for i in ri]\\n if verbose and len(buf) != nb:\\n print '* Rejecting', nb - len(buf), 'spikes.'\\n return buf\",\n \"def test_order_ejection_4_hours(self):\\n manager = EjectionManager(self.user)\\n curr_time = self.curr_time\\n\\n manager.process_order_time_limit(self.order, curr_time)\\n self.assertEqual(self.order.number_of_ejection_notices, 1)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time)\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=61))\\n self.assertEqual(self.order.number_of_ejection_notices, 1)\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=121))\\n self.assertEqual(self.order.number_of_ejection_notices, 2)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=121))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=181))\\n self.assertEqual(self.order.number_of_ejection_notices, 3)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=181))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=211))\\n self.assertEqual(self.order.number_of_ejection_notices, 4)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=211))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=226))\\n self.assertEqual(self.order.number_of_ejection_notices, 5)\\n self.set_latest_order_update_time_created(\\n self.order,\\n curr_time + timedelta(minutes=226))\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=230))\\n self.assertEqual(self.order.number_of_ejection_notices, 5)\\n\\n manager.process_order_time_limit(self.order,\\n curr_time + timedelta(minutes=241))\\n self.assertEqual(self.order.number_of_ejection_notices, 5)\\n self.assertEqual(self.order.status, Order.STATUS_CLOSED)\",\n \"def test_dont_cancel_rebookings_within_cancellation_period_without_warning_sent(self, mock_tz):\\n mock_tz.now.return_value = datetime(2015, 2, 10, 18, 0, tzinfo=dt_timezone.utc)\\n # cancel booking to reset warning flags\\n self.unpaid.status = \\\"CANCELLED\\\"\\n self.unpaid.save()\\n # rebook\\n self.unpaid.status = \\\"OPEN\\\"\\n self.unpaid.date_rebooked = datetime(2015, 2, 10, 12, 30, tzinfo=dt_timezone.utc)\\n self.unpaid.save()\\n\\n self.assertEqual(self.unpaid.status, 'OPEN')\\n self.assertFalse(self.unpaid.warning_sent)\\n self.assertIsNone(self.unpaid.date_warning_sent)\\n management.call_command('cancel_unpaid_bookings')\\n self.unpaid.refresh_from_db()\\n # still open\\n self.assertEqual(self.unpaid.status, 'OPEN')\\n\\n # set the warning sent flag to < 2hrs ago\\n self.unpaid.warning_sent = True\\n self.unpaid.date_warning_sent = datetime(2015, 2, 10, 17, 0, tzinfo=dt_timezone.utc)\\n self.unpaid.save()\\n management.call_command('cancel_unpaid_bookings')\\n self.unpaid.refresh_from_db()\\n # still open\\n self.assertEqual(self.unpaid.status, 'OPEN')\\n\\n # set the warning sent flag to > 2hrs ago\\n self.unpaid.warning_sent = True\\n self.unpaid.date_warning_sent = datetime(2015, 2, 10, 15, 0, tzinfo=dt_timezone.utc)\\n self.unpaid.save()\\n management.call_command('cancel_unpaid_bookings')\\n self.unpaid.refresh_from_db()\\n # now cancelled\\n self.assertEqual(self.unpaid.status, 'CANCELLED')\",\n \"def _periodically_cleanup_candidates(self):\\n while True:\\n yield 5 * 60.0\\n\\n now = time()\\n for key, candidate in [(key, candidate) for key, candidate in self._candidates.iteritems() if candidate.is_all_obsolete(now)]:\\n if __debug__: dprint(\\\"removing obsolete candidate \\\", candidate)\\n del self._candidates[key]\\n self.wan_address_unvote(candidate)\",\n \"def solve_collisions(self, t2):\\n self.t_start, self.t_end = self.t1, t2\\n self.current_dt = self.t_end-self.t_start\\n\\n # Loop until an acceptable state is found.\\n while True:\\n\\n # Check if any events have occurred.\\n impact_surfaces = detect_impacts(self)\\n release_surfaces = detect_releases(self)\\n\\n # Case 1: no events, return with the current state.\\n if not any((release_surfaces, impact_surfaces)):\\n return \\n\\n # Case 2: impact but no releases, solve for impacts.\\n elif (impact_surfaces and (not release_surfaces)):\\n surfaces, impact_state = find_first_impact(self,\\n impact_surfaces, self._impact_solve_method)\\n self.impact_update(surfaces, impact_state) \\n self._state1_impacts = surfaces\\n #print '\\\\tImpact', impact_state['t'], surfaces\\n \\n # Case 3: releases but no impacts, solve for releases. \\n elif (release_surfaces and (not impact_surfaces)):\\n surfaces, release_state = find_first_release(self,\\n release_surfaces, self._release_solve_method)\\n self.release_update(surfaces, release_state) \\n self._state1_releases = []\\n #print '\\\\tRelease', release_state['t'], surfaces\\n\\n # Case 4: both impacts and releases, find first event.\\n else:\\n impact_surfaces, impact_state = find_first_impact(self,\\n impact_surfaces, self._impact_solve_method)\\n release_surfaces, release_state = find_first_release(self,\\n release_surfaces, self._release_solve_method)\\n\\n # Times are very close to one another - transfer of contact.\\n if abs(impact_state['t']-release_state['t'])<1e-3:\\n self.constraint_transition_update(impact_surfaces,\\n release_surfaces, impact_state)\\n #print '\\\\tTransition of constraints'\\n\\n # Impact occurred first.\\n elif impact_state['t'] < release_state['t']:\\n self.impact_update(impact_surfaces, impact_state)\\n self._state1_impacts = impact_surfaces\\n\\n # Release occurred first.\\n else:\\n self.release_update(release_surfaces, release_state)\",\n \"def decrement_notice(self):\\n\\t\\tassert not any(self.feat_time_left==0)\\n\\t\\tself.feat_time_left[self.feat_time_left>0]-=1\\n\\t\\tbool_feats_to_remove=(self.feat_time_left==0)\\n\\t\\tself.remove_feats(bool_feats_to_remove)\",\n \"async def afterHoursAutoPurge(self, ctx: Context):\",\n \"def _repair_crossed_asks(self, ask):\\r\\n while len(self.asks) and self.asks[0].price < ask:\\r\\n volume = self.asks[0].volume\\r\\n self._update_total_ask(-volume)\\r\\n self.asks.pop(0)\\r\\n self._valid_ask_cache = -1\\r\\n #self.debug(\\\"### repaired ask\\\")\\r\",\n \"async def afterHoursAutoPurgeInactiveDuration(self, ctx: Context, *, duration: Optional[str]):\\n\\n guildConfig = self.config.guild(ctx.guild)\\n autoPurgeConfig = guildConfig.get_attr(KEY_AUTO_PURGE)\\n autoPurgeInactiveDurationConfig = autoPurgeConfig.get_attr(KEY_INACTIVE_DURATION)\\n\\n if duration:\\n if duration == \\\"0\\\":\\n await autoPurgeInactiveDurationConfig.set(0)\\n await ctx.send(\\\"Disabled auto-purge on inactive duration.\\\")\\n return\\n else:\\n match = re.match(\\n r\\\"^\\\\s*\\\"\\n r\\\"(?:(\\\\d{1,5})\\\\s*y(?:(?:ea)?rs?)?)?\\\" # years\\n r\\\"\\\\s*\\\"\\n r\\\"(?:(\\\\d{1,5})\\\\s*mo(?:nth)?s?)?\\\" # months\\n r\\\"\\\\s*\\\"\\n r\\\"(?:(\\\\d{1,5})\\\\s*w(?:(?:ee)?ks?)?)?\\\" # weeks\\n r\\\"\\\\s*\\\"\\n r\\\"(?:(\\\\d{1,5})\\\\s*d(?:ays?)?)?\\\" # days\\n r\\\"\\\\s*\\\"\\n r\\\"(?:(\\\\d{1,5})\\\\s*h(?:(?:ou)?rs?)?)?\\\" # hours\\n r\\\"\\\\s*\\\"\\n r\\\"(?:(\\\\d{1,5})\\\\s*m(?:in(?:ute)?s?)?)?\\\" # minutes\\n r\\\"\\\\s*\\\"\\n r\\\"(?:(\\\\d{1,5})\\\\s*s(?:ec(?:ond)?s?)?)?\\\" # seconds\\n r\\\"\\\\s*$\\\",\\n duration,\\n re.IGNORECASE,\\n )\\n if match is None:\\n await ctx.send(\\\"Invalid duration.\\\")\\n return\\n else:\\n groups = match.groups()\\n years, months, weeks, days, hours, minutes, seconds = [\\n int(x) if x else 0 for x in groups\\n ]\\n\\n totalSeconds = (\\n years * 365 * 24 * 60 * 60\\n + months * 30 * 24 * 60 * 60\\n + weeks * 7 * 24 * 60 * 60\\n + days * 24 * 60 * 60\\n + hours * 60 * 60\\n + minutes * 60\\n + seconds\\n )\\n\\n await autoPurgeInactiveDurationConfig.set(totalSeconds)\\n\\n if totalSeconds == 0:\\n await ctx.send(\\\"Disabled auto-purge on inactive duration.\\\")\\n else:\\n await ctx.send(\\n f\\\"Set the inactive duration for auto-purge to {humanize_timedelta(seconds=totalSeconds)}.\\\"\\n )\\n else:\\n totalSeconds: int = await autoPurgeInactiveDurationConfig()\\n if totalSeconds == 0:\\n await ctx.send(\\\"The inactive duration for auto-purge is currently not set.\\\")\\n else:\\n await ctx.send(\\n f\\\"The inactive duration for auto-purge is currently set to {humanize_timedelta(seconds=totalSeconds)}.\\\"\\n )\",\n \"def test_email_warnings(self, mock_tz):\\n mock_tz.now.return_value = datetime(\\n 2015, 2, 10, 10, 0, tzinfo=dt_timezone.utc\\n )\\n\\n # cancellation period starts 2015/2/14 17:00\\n # payment_due_date 2015/2/11 23:59\\n event = baker.make_recipe(\\n 'booking.future_EV',\\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\\n payment_open=True,\\n cost=10,\\n payment_due_date=datetime(2015, 2, 11, tzinfo=dt_timezone.utc),\\n cancellation_period=1)\\n # cancellation period starts 2015/2/14 17:00\\n # payment_due_date 2015/2/12 23:59\\n event1 = baker.make_recipe(\\n 'booking.future_EV',\\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\\n payment_open=True,\\n cost=10,\\n payment_due_date=datetime(2015, 2, 12, tzinfo=dt_timezone.utc),\\n cancellation_period=1)\\n # no cost, no warnings sent\\n event2 = baker.make_recipe(\\n 'booking.future_EV',\\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\\n payment_open=True,\\n cost=0,\\n cancellation_period=1)\\n baker.make_recipe(\\n 'booking.booking', event=event, paid=False,\\n payment_confirmed=False,\\n date_booked=datetime(2015, 2, 9, 19, 30, tzinfo=dt_timezone.utc),\\n _quantity=5,\\n )\\n baker.make_recipe(\\n 'booking.booking', event=event1, paid=False,\\n payment_confirmed=False,\\n date_booked=datetime(2015, 2, 9, 19, 30, tzinfo=dt_timezone.utc),\\n _quantity=5,\\n )\\n baker.make_recipe(\\n 'booking.booking', event=event2, paid=False,\\n payment_confirmed=False,\\n date_booked=datetime(2015, 2, 9, 21, 00, tzinfo=dt_timezone.utc),\\n _quantity=5,\\n )\\n _add_user_email_addresses(Booking)\\n management.call_command('email_warnings')\\n self.assertEqual(len(mail.outbox), 10)\",\n \"def release(self, delay=None):\\n the_tuple = self.deque.release(self.tube, self.task_id, delay=delay)\\n\\n self.update_from_tuple(the_tuple)\\n\\n if delay is None:\\n return bool(self.state == 1)\\n else:\\n return bool(self.state == 0)\",\n \"def test_sms_campaign_expire_check(self):\\n result = sms_campaign_expire_check.delay()\\n self.assertEqual(result.successful(), True)\",\n \"def unsend_scheduled_messages_after(self, time_cutoff):\\n for user_id in self.user_id_to_scheduled_message_ts:\\n for scheduled_ts in list(self.user_id_to_scheduled_message_ts[user_id]):\\n if scheduled_ts >= time_cutoff:\\n # The below if statement is likley redundant\\n if scheduled_ts in self.user_id_to_scheduled_message_ts[user_id]:\\n self.user_id_to_scheduled_message_ts[user_id].remove(scheduled_ts)\",\n \"def _clean_outdated(self):\\n now = _now()\\n outdated = []\\n for request_no, request_info in self._current_requests.items():\\n if now - request_info.start_time > self._force_clean_after:\\n outdated.append(request_no)\\n if outdated:\\n logging.error(\\\"There are {} requests which were started but haven't \\\"\\n \\\"been finished in more than {}s.\\\"\\n .format(len(outdated), self._force_clean_after))\\n for request_no in outdated:\\n del self._current_requests[request_no]\\n self._last_autoclean_time = now\",\n \"def notify_overdue(self, **kwargs):\\n return self.notify(\\\"notify_overdue\\\", **kwargs)\",\n \"def test_dont_cancel_bookings_in_cancellation_period_if_warning_not_sent(self, mock_tz):\\n mock_tz.now.return_value = datetime(2015, 2, 11, 12, 0, tzinfo=dt_timezone.utc)\\n\\n # self.ticketed_event payment due date 2015/2/11 23:59\\n\\n unpaid_no_warning = baker.make(\\n TicketBooking,\\n ticketed_event=self.ticketed_event,\\n paid=False,\\n date_booked=datetime(2015, 2, 10, 5, 30, tzinfo=dt_timezone.utc),\\n warning_sent=False\\n )\\n unpaid_warning_within_2_hrs = baker.make(\\n TicketBooking,\\n ticketed_event=self.ticketed_event,\\n paid=False,\\n date_booked=datetime(2015, 2, 10, 5, 30, tzinfo=dt_timezone.utc),\\n warning_sent=True,\\n date_warning_sent=datetime(2015, 2, 11, 10, 30, tzinfo=dt_timezone.utc),\\n )\\n unpaid_warning_more_than_2_hrs_ago = baker.make(\\n TicketBooking,\\n ticketed_event=self.ticketed_event,\\n paid=False,\\n date_booked=datetime(2015, 2, 10, 5, 30, tzinfo=dt_timezone.utc),\\n warning_sent=True,\\n date_warning_sent=datetime(2015, 2, 11, 9, 30, tzinfo=dt_timezone.utc),\\n )\\n\\n self.assertFalse(unpaid_no_warning.cancelled)\\n self.assertFalse(unpaid_warning_within_2_hrs.cancelled)\\n self.assertFalse(unpaid_warning_more_than_2_hrs_ago.cancelled)\\n\\n management.call_command('cancel_unpaid_ticket_bookings')\\n unpaid_no_warning.refresh_from_db()\\n unpaid_warning_within_2_hrs.refresh_from_db()\\n unpaid_warning_more_than_2_hrs_ago.refresh_from_db()\\n self.assertFalse(unpaid_no_warning.cancelled)\\n self.assertFalse(unpaid_warning_within_2_hrs.cancelled)\\n self.assertTrue(unpaid_warning_more_than_2_hrs_ago.cancelled)\",\n \"def _cleanUp(self):\\r\\n limit = datetime.now() - timedelta(seconds=self._timeout)\\r\\n\\r\\n toClean = [msg for msg in self._incompleteMsgs if msg.older(limit)]\\r\\n\\r\\n if toClean:\\r\\n for msg in toClean:\\r\\n self._incompleteMsgs.remove(msg)\\r\\n\\r\\n log.msg('{0} incomplete messages have been dropped '\\r\\n 'from assembler.'.format(len(toClean)))\\r\\n\\r\\n toClean = [uri for uri, (_, timestamp) in self._binaries.iteritems()\\r\\n if timestamp < limit]\\r\\n\\r\\n if toClean:\\r\\n for uri in toClean:\\r\\n del self._binaries[uri]\\r\\n\\r\\n log.msg('{0} unused binaries have been dropped '\\r\\n 'from assembler.'.format(len(toClean)))\",\n \"def test_redeem_error_after_delay(\\n self, get_config: GetConfig, clock: Clock, voucher: bytes\\n ) -> None:\\n datetime_now = clock_to_now(clock)\\n store = self.useFixture(\\n TemporaryVoucherStore(\\n datetime_now,\\n get_config,\\n ),\\n ).store\\n controller = PaymentController(\\n clock,\\n store,\\n UnpaidRedeemer(),\\n default_token_count=100,\\n allowed_public_keys=set(),\\n )\\n self.assertThat(\\n Deferred.fromCoroutine(controller.redeem(voucher)),\\n succeeded(Always()),\\n )\\n # It fails this time.\\n self.assertThat(\\n controller.get_voucher(voucher).state,\\n MatchesAll(\\n IsInstance(model_Unpaid),\\n MatchesStructure(\\n finished=Equals(datetime_now()),\\n ),\\n ),\\n )\\n\\n # Some time passes.\\n interval = timedelta(hours=1)\\n clock.advance(interval.total_seconds())\\n\\n # It failed again.\\n self.assertThat(\\n controller.get_voucher(voucher).state,\\n MatchesAll(\\n IsInstance(model_Unpaid),\\n MatchesStructure(\\n # At the new time, demonstrating the retry was performed.\\n finished=Equals(datetime_now()),\\n ),\\n ),\\n )\",\n \"def put_busy(duration, headers):\\n debug = os.environ.get(\\\"DREPIN_DEBUG\\\")\\n message = \\\"DREPIN_DEBUG is not defined. Delay duration: 0 sec.\\\"\\n if debug:\\n message = \\\"Delay duration: {} sec.\\\".format(duration)\\n now_sec = time.time()\\n end_sec = now_sec + duration\\n while now_sec < end_sec:\\n now_sec = time.time()\\n return message\",\n \"def test_pypi_py2py3_expired_default_grace_period(self):\\n obsolete_dep_info = dict(UP_TO_DATE_DEPS)\\n obsolete_dep_info['google-auth'] = {\\n 'current_time': datetime.datetime(2019, 8, 23, 0, 0, 0),\\n 'installed_version': '1.3.0',\\n 'installed_version_time': datetime.datetime(\\n 2019, 2, 19, 21, 15, 56),\\n 'is_latest': False,\\n 'latest_version': '1.0.0',\\n 'latest_version_time': datetime.datetime(2019, 2, 19, 21, 15, 56)\\n }\\n\\n obsolete_dep_compat_results = list(RECENT_SUCCESS_DATA)\\n obsolete_dep_compat_results.remove(GOOGLE_API_CORE_RECENT_SUCCESS_2)\\n obsolete_dep_compat_results.remove(GOOGLE_API_CORE_RECENT_SUCCESS_3)\\n obsolete_dep_compat_results.append(\\n GOOGLE_API_CORE_RECENT_SUCCESS_2.with_updated_dependency_info(\\n obsolete_dep_info))\\n obsolete_dep_compat_results.append(\\n GOOGLE_API_CORE_RECENT_SUCCESS_3.with_updated_dependency_info(\\n obsolete_dep_info))\\n\\n self.fake_store.save_compatibility_statuses(\\n obsolete_dep_compat_results)\\n package_name = 'google-api-core'\\n self.assertImageResponsePyPI(package_name)\\n self.assertTargetResponse(\\n package_name, self.expired_default_grace_period_expected_details)\",\n \"def releaseWave(t):\\n for w in sorted(waveTimes.keys(), reverse=True):\\n wt = waveTimes[w]\\n if t - p >= wt:\\n return w\\n raise Exception('Tiempo de liberación de solicitud infactible.')\",\n \"def delay_completion(mission):\\n if mission.ut() - mission.current_step[\\\"start_ut\\\"] > 5:\\n vessel = mission.conn.space_center.active_vessel\\n vessel.auto_pilot.disengage()\\n mission.next()\",\n \"def action_payslip_done(self):\\n for recd in self.overtime_ids:\\n recd.payslip_paid = True\\n return super(PayslipOverTime, self).action_payslip_done()\",\n \"def test_crash_on_impossible_fixity(self):\\n campaign, propositions, categories, user_campaigns = self.generate_batch_wishes(\\n 2, 5\\n )\\n\\n campaign.manager = User.objects.get(pk=\\\"17bocquet\\\")\\n campaign.save()\\n\\n categories_uc = {}\\n for uc in user_campaigns:\\n categories_uc[uc.category.id] = categories_uc.get(uc.category.id, []) + [uc]\\n\\n for i in range(3):\\n uc = categories_uc[categories[0].id][i]\\n uc.fixed_to = propositions[0]\\n uc.save()\\n\\n with self.assertRaises(ValueError):\\n self.login(\\\"17bocquet\\\")\\n self.patch(\\n \\\"/repartitions/campaigns/{}/\\\".format(campaign.id),\\n data={\\\"status\\\": \\\"RESULTS\\\"},\\n )\",\n \"def test_delete_on_background_response_descriptor_projects_release_release_resource(self):\\n pass\",\n \"def reap_recent_assignments():\\n from anubis.config import config\\n\\n recent_assignments = Assignment.query.filter(\\n Assignment.release_date > datetime.now(),\\n Assignment.due_date > datetime.now() - config.STATS_REAP_DURATION,\\n ).all()\\n\\n print(json.dumps({\\n 'reaping assignments:': [assignment.data for assignment in recent_assignments]\\n }, indent=2))\\n\\n for assignment in recent_assignments:\\n for submission in Submission.query.filter(\\n Submission.assignment_id == assignment.id,\\n Submission.build == None,\\n ).all():\\n if submission.build is None:\\n init_submission(submission)\\n enqueue_autograde_pipeline(submission.id)\\n\\n for assignment in recent_assignments:\\n bulk_autograde(assignment.id)\",\n \"def check_for_timeouts(self) -> None:\\n\\n if self.id != self._leader_id:\\n return\\n\\n for instance in self._undecided_instances:\\n if (time.time() - self._last_prepare_time[instance]) > self._round_timeouts[instance]:\\n #print('\\\\033[93m' + \\\"Instance {0} round {1} exceeded timeout!\\\".format(instance, self._round_id[instance]) + \\\"\\\\033[0m\\\")\\n self._round_timeouts[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\\n self._enable_phase1_optimization = False\\n self.prepare_phase_parallel(instance)\\n # Handle a single time out per loop to improve responsiveness to input messages\\n break\\n\\n for instance in self._decided_values.keys():\\n if not self._acked_decided_values[instance]:\\n if (time.time() - self._last_decide_time[instance]) > self._learners_decide_timeout[instance]:\\n # Timed out waiting the learner ACK: send the decided value to the learners and increase the timeout\\n self._learners_decide_timeout[instance] *= Proposer.TIMEOUT_GROWTH_FACTOR\\n decide_message: Decide = Decide(sender=self,\\n receiver_role=Role.LEARNER,\\n payload=DecidePayload((self._decided_values[instance], instance))\\n )\\n self.send(decide_message)\\n\\n # Handle a single time out per loop to improve responsiveness to input messages\\n break\",\n \"def test_remove_recurring_schedule(self):\\n pass\",\n \"def _thresholdAlarm(self, project_usage, proj_name, old_used_space_perc):\\n\\n message = \\\"\\\"\\n threshold_soft = 95\\n threshold_hard = 100\\n if (proj_name in self.conf.mirrored_projects):\\n threshold_soft = 95*2\\n threshold_hard = 100*2\\n if ((project_usage['used_space_perc'] > threshold_soft) and \\n (project_usage['used_space_perc'] > old_used_space_perc)):\\n message = \\\"project \\\" + proj_name + \\\" is reaching its quota limit \\\" \\\\\\n + \\\"(used space > 95%): \\\" + str(project_usage['used_space'])\\\\\\n + \\\" \\\" + self.conf.storage_space_unity\\n if (project_usage['used_space_perc'] >= threshold_hard):\\n message = \\\"project \\\" + proj_name + \\\" reached its quota limit \\\" \\\\\\n + \\\"(used space > 100%): \\\" + str(project_usage['used_space'])\\\\\\n + \\\" \\\" + self.conf.storage_space_unity\\n if (len(message) > 0):\\n mailsnd = MailSender()\\n mailsnd.send(message, self.conf.notification_sender, \\n self.conf.notification_receiver)\\n logger.debug(\\\"sent alert for quota over limit related to project: \\\"\\n + proj_name)\",\n \"def test_escalate_questions_cron(self, submit_ticket):\\n\\n questions_to_escalate = [\\n # Questions over 24 hours old without an answer.\\n question(\\n created=datetime.now() - timedelta(hours=24, minutes=10),\\n save=True),\\n question(\\n created=datetime.now() - timedelta(hours=24, minutes=50),\\n save=True),\\n ]\\n\\n # Question about Firefox OS\\n fxos = product(slug='firefox-os', save=True)\\n q = question(\\n created=datetime.now() - timedelta(hours=24, minutes=10),\\n product=fxos,\\n save=True)\\n questions_to_escalate.append(q)\\n\\n questions_not_to_escalate = [\\n # Questions newer than 24 hours without an answer.\\n question(save=True),\\n question(created=datetime.now() - timedelta(hours=11), save=True),\\n question(created=datetime.now() - timedelta(hours=21), save=True),\\n ]\\n\\n # Question older than 24 hours with a recent answer.\\n q = question(\\n created=datetime.now() - timedelta(hours=24, minutes=10),\\n save=True)\\n answer(created=datetime.now() - timedelta(hours=10), question=q,\\n save=True)\\n answer(created=datetime.now() - timedelta(hours=1), creator=q.creator,\\n question=q, save=True)\\n questions_not_to_escalate.append(q)\\n\\n # Question older than 24 hours with a recent answer by the asker.\\n q = question(\\n created=datetime.now() - timedelta(hours=24, minutes=10),\\n save=True)\\n answer(\\n created=datetime.now() - timedelta(hours=15), creator=q.creator,\\n question=q, save=True)\\n questions_not_to_escalate.append(q)\\n\\n # Question older than 24 hours without an answer already escalated.\\n q = question(\\n created=datetime.now() - timedelta(hours=24, minutes=10),\\n save=True)\\n q.tags.add(config.ESCALATE_TAG_NAME)\\n questions_not_to_escalate.append(q)\\n\\n # Question with an inactive user.\\n q = question(\\n created=datetime.now() - timedelta(hours=24, minutes=10),\\n save=True)\\n q.creator.is_active = False\\n q.creator.save()\\n questions_not_to_escalate.append(q)\\n\\n # Question about Thunderbird, which is one of the products we exclude.\\n tb = product(slug='thunderbird', save=True)\\n q = question(\\n created=datetime.now() - timedelta(hours=24, minutes=10),\\n product=tb,\\n save=True)\\n questions_not_to_escalate.append(q)\\n\\n # Run the cron job and verify only 3 questions were escalated.\\n eq_(len(questions_to_escalate), escalate_questions())\",\n \"def _notify_exceeded_budget(self, budget: Budget) -> None:\\n print(f'[NOTIFICATION] You have exceeded the {budget.name} budget.')\",\n \"def test_issue_edit_issue_deadline(self):\\n pass\",\n \"def test_email_warnings_not_sent_within_2_hrs_of_booking(self, mock_tz):\\n mock_tz.now.return_value = datetime(\\n 2015, 2, 11, 10, 0, tzinfo=dt_timezone.utc\\n )\\n\\n # payment_due_date 2015/2/11 23:59 (within 24hrs - warnings sent)\\n ticketed_event = baker.make_recipe(\\n 'booking.ticketed_event_max10',\\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\\n payment_open=True,\\n ticket_cost=10,\\n payment_due_date=datetime(2015, 2, 11, tzinfo=dt_timezone.utc),\\n )\\n booking1 = baker.make(\\n TicketBooking, ticketed_event=ticketed_event, paid=False,\\n date_booked=datetime(2015, 2, 1, 0, 0, tzinfo=dt_timezone.utc),\\n )\\n booking2 = baker.make(\\n TicketBooking, ticketed_event=ticketed_event, paid=False,\\n date_booked=datetime(2015, 2, 11, 8, 5, tzinfo=dt_timezone.utc),\\n )\\n _add_user_email_addresses(TicketBooking)\\n for ticket_booking in TicketBooking.objects.all():\\n baker.make(Ticket, ticket_booking=ticket_booking)\\n\\n management.call_command('email_ticket_booking_warnings')\\n self.assertEqual(len(mail.outbox), 1)\\n booking1.refresh_from_db()\\n booking2.refresh_from_db()\\n self.assertTrue(booking1.warning_sent)\\n self.assertFalse(booking2.warning_sent)\",\n \"def admin_bankruptcy_forced_cancellation(self, name, with_report):\\n discards = list()\\n for delay in self.DELAYS:\\n write_keys = self._get_sample_owners(name=name, delay=delay)\\n leaderboard = self.get_leaderboard(granularity=LeaderboardGranularity.name_and_delay, count=10000,\\n name=name, delay=delay, readable=False)\\n losers = [key for key in write_keys if\\n (self.shash(key) in leaderboard) and (leaderboard[self.shash(key)] < -1.0)]\\n\\n for write_key in losers:\\n if self.bankrupt(write_key=write_key):\\n code = self.shash(write_key)\\n memo = Memo(activity=Activity.cancel, context=ActivityContext.bankruptcy, name=name, code=code,\\n write_key=write_key, message='Initiating cancellation of submissions due to bankruptcy')\\n self.add_memo_as_owner_confirm(memo=memo)\\n self.cancel(name=name, write_key=write_key, delay=delay)\\n discards.append((name, write_key))\\n report_data = dict(discards)\\n report_memo = Memo(activity=Activity.daemon, context=ActivityContext.bankruptcy, count=len(report_data), data=report_data)\\n self.add_memo_as_system_confirm(memo=report_memo,private_actor=PrivateActor.bankruptcy_daemon)\\n return len(discards) if not with_report else report_memo.as_dict()\",\n \"def killQueueOrder(self):\\n # CHECK ALL QUEUE ORDERS AND CANCEL ORDER IF GREATER THAN TWO HOURS OLD\\n queue_orders = self.queue.find(\\n {\\\"Trader\\\": self.user[\\\"Name\\\"], \\\"Account_ID\\\": self.account_id})\\n\\n dt = datetime.now(tz=pytz.UTC).replace(microsecond=0)\\n\\n dt_central = dt.astimezone(pytz.timezone('US/Central'))\\n\\n two_hours_ago = datetime.strptime(datetime.strftime(\\n dt_central - timedelta(hours=2), \\\"%Y-%m-%d %H:%M:%S\\\"), \\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n ten_minutes_ago = datetime.strptime(datetime.strftime(\\n dt_central - timedelta(minutes=10), \\\"%Y-%m-%d %H:%M:%S\\\"), \\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n for order in queue_orders:\\n\\n order_date = order[\\\"Date\\\"]\\n\\n order_type = order[\\\"Order_Type\\\"]\\n\\n id = order[\\\"Order_ID\\\"]\\n\\n forbidden = [\\\"REJECTED\\\", \\\"CANCELED\\\", \\\"FILLED\\\"]\\n\\n if two_hours_ago > order_date and (order_type == \\\"BUY\\\" or order_type == \\\"BUY_TO_OPEN\\\") and id != None and order[\\\"Order_Status\\\"] not in forbidden:\\n\\n # FIRST CANCEL ORDER\\n resp = self.tdameritrade.cancelOrder(id)\\n\\n if resp.status_code == 200 or resp.status_code == 201:\\n\\n other = {\\n \\\"Symbol\\\": order[\\\"Symbol\\\"],\\n \\\"Order_Type\\\": order[\\\"Order_Type\\\"],\\n \\\"Order_Status\\\": \\\"CANCELED\\\",\\n \\\"Strategy\\\": order[\\\"Strategy\\\"],\\n \\\"Account_ID\\\": self.account_id,\\n \\\"Trader\\\": self.user[\\\"Name\\\"],\\n \\\"Date\\\": getDatetime()\\n }\\n\\n self.other.insert_one(other)\\n\\n self.queue.delete_one(\\n {\\\"Trader\\\": self.user[\\\"Name\\\"], \\\"Symbol\\\": order[\\\"Symbol\\\"], \\\"Strategy\\\": order[\\\"Strategy\\\"]})\\n\\n self.logger.INFO(\\n f\\\"CANCELED ORDER FOR {order['Symbol']} - TRADER: {self.user['Name']}\\\", True)\\n\\n # IF QUEUE ORDER DATE GREATER THAN 10 MINUTES OLD AND ORDER ID EQUALS NONE, SEND ALERT\\n if ten_minutes_ago > order_date and order[\\\"Order_ID\\\"] == None and order[\\\"Account_ID\\\"] == self.account_id:\\n\\n if order[\\\"Symbol\\\"] not in self.no_ids_list:\\n\\n self.logger.ERROR(\\n \\\"QUEUE ORDER ID ERROR\\\", f\\\"ORDER ID FOR {order['Symbol']} NOT FOUND - TRADER: {self.user['Name']} - ACCOUNT ID: {self.account_id}\\\")\\n\\n self.no_ids_list.append(order[\\\"Symbol\\\"])\\n\\n else:\\n\\n if order[\\\"Symbol\\\"] in self.no_ids_list:\\n\\n self.no_ids_list.remove(order[\\\"Symbol\\\"])\",\n \"def renew(self):\\n remaining=self.time_left()\\n if ( (remaining !=-1) and (self.update_frequency!=-1) and \\n (remaining<self.update_frequency) ): \\n self.create()\",\n \"def test_update_maxttl(self):\\n for bucket in self.buckets:\\n self._load_json(bucket, self.num_items, exp=100)\\n self._update_bucket_maxTTL(maxttl=40)\\n\\n self.sleep(40, \\\"waiting before running expiry pager...\\\")\\n self.expire_pager(self.servers)\\n self.sleep(20, \\\"waiting for item count to come down...\\\")\\n for bucket in self.buckets:\\n items = RestConnection(self.master).get_active_key_count(bucket)\\n self.log.info(\\\"Doc expiry set to = 100s, maxTTL at the time of doc creation = 200s\\\"\\n \\\" updated maxttl = 40s, after 40s item count = {0}\\\".format(items))\\n if items != self.num_items:\\n self.fail(\\\"FAIL: Updated ttl affects docs with larger expiry before updation!\\\")\\n\\n self.sleep(60, \\\"waiting before running expiry pager...\\\")\\n self.expire_pager(self.servers)\\n self.sleep(20, \\\"waiting for item count to come down...\\\")\\n for bucket in self.buckets:\\n items = RestConnection(self.master).get_active_key_count(bucket)\\n self.log.info(\\\"Doc expiry set to = 100s, maxTTL at the time of doc creation = 200s\\\"\\n \\\" updated maxttl = 40s, after 100s item count = {0}\\\".format(items))\\n if items != 0:\\n self.fail(\\\"FAIL: Docs with 100s as expiry before maxTTL updation still alive!\\\")\",\n \"def _putMailInPast(self, mail, days):\\n doc = mail.getEditableContent()\\n fid = self.archiver.date_field_id\\n doc.edit({fid: doc.getDataModel()[fid] - days}, mail)\",\n \"def cleanup(keep_num=5):\\n\\n keep_num = int(keep_num)\\n assert keep_num > 0, \\\"[ERROR] keep_num must be > 0; refusing to proceed.\\\"\\n\\n with cd(\\\"%(path)s/packages\\\" % env):\\n package_files = sorted(run(\\\"ls -1\\\").split())\\n package_files = [_.replace(\\\".tar.gz\\\", \\\"\\\") for _ in package_files]\\n\\n with cd(\\\"%(path)s/releases\\\" % env):\\n release_files = sorted(run(\\\"ls -1\\\").split())\\n release_files.remove('current')\\n\\n diff = set(package_files).symmetric_difference(set(release_files))\\n\\n if diff:\\n raise Exception(\\\"[ERROR]: Package and release directories are out of sync;\\\"\\n \\\" refusing to proceed. Please fix this difference manually: %s\\\" % diff)\\n\\n package_files = package_files[:-keep_num]\\n release_files = release_files[:-keep_num]\\n\\n with cd(\\\"%(path)s/packages\\\" % env):\\n [sudo(\\\"rm %s.tar.gz\\\" % _) for _ in package_files]\\n\\n with cd(\\\"%(path)s/releases\\\" % env):\\n [sudo(\\\"rm -r %s\\\" % _) for _ in release_files]\",\n \"def check_cycle_durations(\\n request: Request, policy: RequestPolicy, logger: Logger\\n) -> None:\\n if not policy.check_cycle_length:\\n logger.warning(\\n \\\"KSR-BUNDLE-CYCLE-DURATION: Disabled by policy (check_cycle_length)\\\"\\n )\\n return\\n\\n if not request.bundles:\\n logger.warning(\\\"KSR-BUNDLE-CYCLE-DURATION: No bundles - can't check anything\\\")\\n return\\n\\n cycle_inception_length = (\\n request.bundles[-1].inception - request.bundles[0].inception\\n )\\n _inc_len_str = fmt_timedelta(cycle_inception_length)\\n _min_inc_str = fmt_timedelta(policy.min_cycle_inception_length)\\n _max_inc_str = fmt_timedelta(policy.max_cycle_inception_length)\\n\\n logger.debug(\\n f\\\"Verifying that first bundle inception to last bundle inception ({_inc_len_str}) \\\"\\n f\\\"is between {_min_inc_str} and {_max_inc_str}\\\"\\n )\\n\\n if cycle_inception_length < policy.min_cycle_inception_length:\\n raise KSR_BUNDLE_CYCLE_DURATION_Violation(\\n f\\\"Cycle inception length ({_inc_len_str}) \\\"\\n f\\\"less than minimum acceptable length {_min_inc_str}\\\"\\n )\\n if cycle_inception_length > policy.max_cycle_inception_length:\\n raise KSR_BUNDLE_CYCLE_DURATION_Violation(\\n f\\\"Cycle inception length ({_inc_len_str}) \\\"\\n f\\\"greater than maximum acceptable length {_max_inc_str}\\\"\\n )\\n\\n logger.info(\\n \\\"KSR-BUNDLE-CYCLE-DURATION: The cycle length is in accordance with the KSK operator policy\\\"\\n )\",\n \"async def release(ctx, pkmn_id: int):\\n res = database.release_from_party(ctx.message.author, pkmn_id)\\n if res:\\n tier = _get_tier(int(pkmn_id))\\n money = int(_get_money_earned(tier)/3)\\n message = \\\"Oak: Don't worry I'll take care of {} ;) \\\\nHere's ₱{}, buy yourself something nice.\\\"\\\\\\n .format(database.get_pokemon_name(pkmn_id), money)\\n database.add_pokedollars(ctx.message.author, money)\\n else:\\n message = \\\"**Oak**: Make sure you actually have that pokemon or if your party is not full ya scrub.\\\"\\n await ctx.send(message)\",\n \"def delete_expired(self):\\n check_time = datetime.now()\\n if self.can_expire and self.duration:\\n exp_times = deepcopy(self.exp_times)\\n for key in exp_times:\\n if exp_times[key] < check_time:\\n self.delete(key)\",\n \"def update_lease_expiration(self, network_id, ip_address, lease_remaining):\\n self.cast(self.context,\\n self.make_msg('update_lease_expiration',\\n network_id=network_id,\\n ip_address=ip_address,\\n lease_remaining=lease_remaining,\\n host=self.host),\\n topic=self.topic)\",\n \"def cooldown_checker(self):\\n self.cooldown_tick += 1\\n if self.cooldown_tick == self.pattern_cooldown:\\n self.wait = False\\n self.cooldown_tick = 0\",\n \"def _release_resources(self, job_limits: Dict[str, int]) -> None:\\n for limit_name, count in job_limits.items():\\n self.limits_used[limit_name] -= count\",\n \"def test_email_warnings_not_sent_out_of_hours(self, mock_tz):\\n # payment_due_date 2015/2/11 23:59 (within 24hrs - warnings sent)\\n ticketed_event = baker.make_recipe(\\n 'booking.ticketed_event_max10',\\n date=datetime(2015, 2, 14, 18, 0, tzinfo=dt_timezone.utc),\\n payment_open=True,\\n ticket_cost=10,\\n payment_due_date=datetime(2015, 2, 11, tzinfo=dt_timezone.utc),\\n )\\n\\n baker.make(\\n TicketBooking, ticketed_event=ticketed_event, paid=False,\\n date_booked=datetime(2015, 2, 1, 0, 0, tzinfo=dt_timezone.utc),\\n _quantity=5,\\n )\\n\\n _add_user_email_addresses(TicketBooking)\\n\\n for ticket_booking in TicketBooking.objects.all():\\n baker.make(Ticket, ticket_booking=ticket_booking)\\n\\n mock_tz.now.return_value = datetime(2015, 2, 11, 6, 59, tzinfo=dt_timezone.utc)\\n management.call_command('email_ticket_booking_warnings')\\n self.assertEqual(len(mail.outbox), 0)\\n\\n mock_tz.now.return_value = datetime(2015, 2, 11, 22, 5, tzinfo=dt_timezone.utc)\\n management.call_command('email_ticket_booking_warnings')\\n self.assertEqual(len(mail.outbox), 0)\\n\\n mock_tz.now.return_value = datetime(2015, 2, 11, 21, 59, tzinfo=dt_timezone.utc)\\n management.call_command('email_ticket_booking_warnings')\\n self.assertEqual(len(mail.outbox), 5)\",\n \"def confirm_recent_new_issues(self, msg, hour):\\n self._asset_bind(msg)\\n yield (\\\"Processing....\\\")\\n trans = self._translation_util(msg)\\n client = self._github_operator(msg)\\n cmd = \\\"repo:{} is:open type:issue\\\".format(\\n task_repository_name())\\n issue_list = client.search_issue(cmd, 10)\\n end_time = datetime.datetime.now()\\n cnt = 0\\n for issue in issue_list:\\n start_time = issue.created_at\\n if end_time - start_time <= datetime.timedelta(hours=hour):\\n trans.wait_for_limit(MAX_RESULT, MAX_RESULT)\\n confirmed = False\\n for label in issue.get_labels():\\n if label.name == \\\"welcome\\\":\\n issue.remove_from_labels(\\\"welcome\\\")\\n else:\\n if label.name in [\\\"pending\\\", \\\"translating\\\", \\\"pushed\\\", \\\"finished\\\"]:\\n confirmed = True\\n if not confirmed:\\n issue.add_to_labels(\\\"pending\\\")\\n cnt += 1\\n yield \\\"{} issues confirmed.\\\".format(cnt)\",\n \"def test_reject_proposal_demand(self):\\n pass\",\n \"def take_weekly_agreements():\\n\\n #collect data from form template\\n chore_id = request.form.get(\\\"chore_id\\\")\\n daysagreed = request.form.get(\\\"daysagreed\\\")\\n daysagreed = daysagreed.split(\\\"|\\\")\\n\\n #no more unicode\\n days_agreed = [str(i) for i in daysagreed]\\n\\n #recast agreements from T/F to days of the week (by name)\\n days_agreed = [days_of_the_week[i] for i in range(7) if days_agreed[i] == 'true']\\n\\n #format list of daily agreements for addition to database (string)\\n days_agreed = \\\"|\\\".join(days_agreed) \\n\\n #save to database\\n dbwrangler.add_commitment(days_agreed, chore_id)\\n\\n #redirect to form for further agreements\\n return redirect(\\\"/takeachore\\\")\",\n \"def on_delete(self, req, resp):\\n try:\\n days_to_retain = int(req.params[\\\"days\\\"])\\n except Exception:\\n days_to_retain = 90\\n\\n try:\\n retention_status = self.state_manager.task_retention(\\n retain_days=str(days_to_retain))\\n if not retention_status:\\n resp.status = falcon.HTTP_404\\n return\\n resp.text = \\\"Tables purged successfully.\\\"\\n except Exception as e:\\n self.error(req.context, \\\"Unknown error: %s\\\" % (str(e)))\\n resp.text = \\\"Unexpected error.\\\"\\n resp.status = falcon.HTTP_500\\n return\\n resp.status = falcon.HTTP_200\",\n \"def clean_passes(threshold=misc.get_now_utc()):\\n logger.info('>>> Cleaning passes, threshold = ' + str(threshold))\\n\\n try:\\n\\n no_deleted = pass_models.PassSlots.objects.filter(\\n end__lte=threshold\\n ).delete()\\n logger.debug('>>> tasks@clean_passes.filtered = ' + str(no_deleted))\\n\\n except Exception as ex:\\n\\n logger.exception('>>> Exception cleaning passes, ex = ' + str(ex), ex)\\n return\\n\\n logger.info('>>> DONE cleaning passes')\",\n \"def send_notifications():\\n due_notifications = Notification.query.filter(Notification.delivery_date <= datetime.now(timezone.utc))\\n for notification in due_notifications:\\n send_notification.delay(notification.id)\",\n \"def commit_actions(self, period=1, repair_future_actions=False, verbose=False):\\n while period < self.horizon:\\n if verbose: print('growing period', period)\\n self.grow(period, cascade=False)\\n period += 1\\n if repair_future_actions:\\n if verbose: print('repairing actions in period', period)\\n self.repair_actions(period)\\n else:\\n self.reset_actions(period)\",\n \"def check_msg_release_on_commit(self, broker, ftd_msgs):\\n hits = self._get_hits(broker, re.compile(\\\"debug Message id=\\\\\\\"[0-9a-f-]{36}\\\\\\\"; pid=0x[0-9a-f]+: \\\"\\n \\\"Content released on commit$\\\", re.MULTILINE))\\n self._reconsile_hits(broker, ftd_msgs, hits)\",\n \"def remove_expired_records(self, database): #pylint: disable-msg=R0201\\n #special case to ignore eat files that are uploaded every day\\n self.remove_eat_upload_records(database)\\n \\n expiry_time = 20 # 20 in seconds\\n aborted_expiry_time = 43200 # 12 hours in seconds\\n now = datetime.datetime.utcnow()\\n \\n for rec in database:\\n #finished records\\n if rec.get('finished_time_insert', None) and (now - rec['finished_time_insert']) > datetime.timedelta(seconds = expiry_time) :\\n #archive finished record\\n self.archiver.archive(rec)\\n database.delete(rec)\\n #aborted for more than 12 hours records \\n elif rec.get('aborted', None) and ( now - rec.get('last_update', now) ) > datetime.timedelta(seconds = aborted_expiry_time) :\\n #archive aborted record that was never finished\\n self.archiver.archive(rec)\\n database.delete(rec)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5600951","0.5391433","0.53077644","0.530182","0.5256958","0.5214244","0.52004874","0.5079904","0.50456727","0.5010773","0.49739408","0.49477166","0.49456263","0.4935877","0.4900822","0.48766607","0.48477307","0.4822325","0.48165026","0.48124647","0.48102093","0.48034805","0.47978336","0.47944477","0.4765785","0.47649437","0.47493556","0.47465053","0.4730351","0.47257578","0.4706688","0.47056645","0.46915472","0.46872258","0.46675864","0.46587107","0.4653445","0.46508068","0.4647537","0.46467337","0.4645308","0.4644578","0.46397275","0.46392107","0.4638601","0.4634294","0.46236113","0.46175933","0.46074215","0.46028638","0.46003738","0.4598646","0.4595604","0.45880857","0.45875478","0.45847598","0.4582032","0.45791227","0.45762682","0.4575199","0.45649832","0.45606983","0.4556567","0.45554113","0.4554539","0.4550466","0.45481163","0.45446992","0.4543176","0.45375434","0.45278296","0.45237845","0.45175552","0.45111555","0.4508717","0.4505748","0.45032918","0.4501412","0.44965905","0.44938606","0.44937393","0.4493409","0.4492343","0.44914144","0.44876224","0.4486188","0.44820482","0.44800764","0.44679692","0.44645146","0.44603318","0.44598663","0.4448565","0.44422665","0.442608","0.44232482","0.44205","0.4418341","0.44158682","0.44133124"],"string":"[\n \"0.5600951\",\n \"0.5391433\",\n \"0.53077644\",\n \"0.530182\",\n \"0.5256958\",\n \"0.5214244\",\n \"0.52004874\",\n \"0.5079904\",\n \"0.50456727\",\n \"0.5010773\",\n \"0.49739408\",\n \"0.49477166\",\n \"0.49456263\",\n \"0.4935877\",\n \"0.4900822\",\n \"0.48766607\",\n \"0.48477307\",\n \"0.4822325\",\n \"0.48165026\",\n \"0.48124647\",\n \"0.48102093\",\n \"0.48034805\",\n \"0.47978336\",\n \"0.47944477\",\n \"0.4765785\",\n \"0.47649437\",\n \"0.47493556\",\n \"0.47465053\",\n \"0.4730351\",\n \"0.47257578\",\n \"0.4706688\",\n \"0.47056645\",\n \"0.46915472\",\n \"0.46872258\",\n \"0.46675864\",\n \"0.46587107\",\n \"0.4653445\",\n \"0.46508068\",\n \"0.4647537\",\n \"0.46467337\",\n \"0.4645308\",\n \"0.4644578\",\n \"0.46397275\",\n \"0.46392107\",\n \"0.4638601\",\n \"0.4634294\",\n \"0.46236113\",\n \"0.46175933\",\n \"0.46074215\",\n \"0.46028638\",\n \"0.46003738\",\n \"0.4598646\",\n \"0.4595604\",\n \"0.45880857\",\n \"0.45875478\",\n \"0.45847598\",\n \"0.4582032\",\n \"0.45791227\",\n \"0.45762682\",\n \"0.4575199\",\n \"0.45649832\",\n \"0.45606983\",\n \"0.4556567\",\n \"0.45554113\",\n \"0.4554539\",\n \"0.4550466\",\n \"0.45481163\",\n \"0.45446992\",\n \"0.4543176\",\n \"0.45375434\",\n \"0.45278296\",\n \"0.45237845\",\n \"0.45175552\",\n \"0.45111555\",\n \"0.4508717\",\n \"0.4505748\",\n \"0.45032918\",\n \"0.4501412\",\n \"0.44965905\",\n \"0.44938606\",\n \"0.44937393\",\n \"0.4493409\",\n \"0.4492343\",\n \"0.44914144\",\n \"0.44876224\",\n \"0.4486188\",\n \"0.44820482\",\n \"0.44800764\",\n \"0.44679692\",\n \"0.44645146\",\n \"0.44603318\",\n \"0.44598663\",\n \"0.4448565\",\n \"0.44422665\",\n \"0.442608\",\n \"0.44232482\",\n \"0.44205\",\n \"0.4418341\",\n \"0.44158682\",\n \"0.44133124\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57937604"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74594,"cells":{"query":{"kind":"string","value":"Find new files from a branch for a language."},"document":{"kind":"string","value":"def find_new_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n new_file_list = trans.find_new_files(\n REPOSITORY_NAME, branch, TARGET_LANG)\n\n if create_issue == 0:\n yield (\"\\n\".join(limit_result(new_file_list)))\n else:\n yield (\"Processing....\")\n new_count, skip_count = build_new_issue(trans, branch, new_file_list)\n yield (\"{} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n yield (\"Please cache issues again.\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def find_updated_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n updated_files = trans.find_updated_files(REPOSITORY_NAME, branch, TARGET_LANG)\n if create_issue == 0:\n yield (\"\\n\".join(limit_result(list(updated_files.keys()))))\n else:\n yield (\"Processing....\")\n new_count, skip_count = build_update_issue(trans, branch, updated_files)\n yield (\"{} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n yield (\"Please cache issues again.\")","def find_new_files(self):\r\n in_file_system = self._find_data_files(self.basepath,\r\n self.data_file_extension, [])\r\n status_msg('Search for data files. Found: {0}'.format(\r\n len(in_file_system)), True\r\n )\r\n status_msg('Check that there are no duplicate filenames', True)\r\n in_db = self._get_files_in_db()\r\n status_msg('Get list of files in data base. Found: {0}'.format(\r\n len(in_db)), True\r\n )\r\n for filename in in_file_system:\r\n # Filename has been added to the database both where a part of the\r\n # path was a part of the unique name and where it was not,\r\n # therefore we need to check for both AND we ignore filenames that\r\n # start with IGNORE\r\n if form_unique_name(filename) not in in_db and\\\r\n os.path.basename(filename) not in in_db and not\\\r\n os.path.basename(filename).startswith('IGNORE'):\r\n self.new_data_files.append(filename)\r\n\r\n print # Adds a new line to the statusses before parsing\r\n status_msg('Collecting new files. Found: {0}'.format(\r\n len(self.new_data_files)), True\r\n )","def read_files(folder):\n print_header(\"READING FILES FROM FOLDER (RECURSIVE)\", \"=\")\n files = []\n for dirpath, dirnames, filenames in os.walk(folder):\n if not dirpath.endswith(\"updates\"):\n for filename in filenames:\n root, ext = os.path.splitext(filename)\n if ext.lower() == \".sql\":\n full_path = os.path.join(dirpath, filename)\n with open(full_path, \"r\") as f:\n sql = f.read()\n sql = sql.decode(\"latin-1\")\n\n files.append((filename, sql))\n return files","def download_history_for_files(self, folder, globstring, dropbox_location, recursive=True):\n if recursive:\n files = self._recursive_file_search(folder, globstring)\n else:\n files = glob(folder + globstring)\n\n #print(files)\n for f in files:\n #print(f)\n dropboxpath = os.path.relpath(f, dropbox_location).replace(\"\\\\\", \"/\")\n dropboxpath = '/' + (dropboxpath)\n self.download_revisions(dropboxpath)","def full_find(self, file, version):\n matches = []\n for root, dirnames, filenames in os.walk(self.checkout_path(version)):\n for filename in fnmatch.filter(filenames, file):\n matches.append(os.path.join(root, filename))\n return matches","def git_callback(files): # type: (t.List[t.Tuple[str, str]]) -> None\n for dirpath, _dirnames, filenames in os.walk(os.path.join(data_context().content.root, '.git')):\n paths = [os.path.join(dirpath, filename) for filename in filenames]\n files.extend((path, os.path.relpath(path, data_context().content.root)) for path in paths)","def createFileList(my_dir, term):\n \n for subdir, dirs, files in os.walk(my_dir):\n for file in files:\n if re.search(re.escape(term), file):\n result = os.path.join(subdir, file)\n fileList.append(result)\n return fileList","def find_langs(args):\n infile = args.source\n langs = {}\n for line in infile:\n name_and_files = line.split()\n name = name_and_files[0]\n if name not in langs:\n langs[name] = []\n langs[name] += read_files.filter_files(name_and_files[1:])\n langs[args.unknown] += read_files.filter_files(args.classify)\n return langs","def find_files():\n \n p = re.compile(REGEX_PART_NUMBER)\n job_files = []\n \n for root, dirs, files in os.walk(project_path): # r at start of string need to prevent unicode error\n for filename in files:\n re_part_number = p.match(filename)\n if re_part_number:\n file_ext = filename.split(\".\")[-1].lower() # extract file extension \n file_size = os.path.getsize((os.path.join(root, filename))) # filesize in bytes \n \n part_number = re_part_number.group() # extract part number from regular expression match\n part_code = part_number.split(\"-\")[0]\n \n destinations = [] # destinations is a list in case a filetype is both a source and output filetype\n \n if (file_ext in EXTS_SOURCE_FILES) and flag_find_source_files:\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \n \n if (file_ext in EXTS_OUTPUT_FILES) and flag_find_output_files:\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \n \n if destinations: \n job_files.append(File(filename,root,file_size,destinations,part_number,part_code))\n print(f\"Found: {filename}\")\n \n return job_files","def filesearch(word=\"\"):\n logger.info('Starting filesearch')\n file = []\n for f in glob.glob(\"*\"):\n if word[0] == \".\":\n if f.endswith(word):\n file.append(f)\n\n elif word in f:\n file.append(f)\n #return file\n logger.debug(file)\n return file","def tracked_files(self, branch):\n d = {}\n ls_tree = self.git_cmd('ls-tree -r --name-only --full-tree %s' %\n branch)\n for rpath in ls_tree.splitlines():\n if rpath == '.gitignore':\n continue\n if branch.startswith('timestamps'):\n d[rpath] = pathlib.PosixPath(self.repodir, rpath)\n else:\n if not rpath.startswith(ROOT_SUBDIR):\n continue\n d[rpath] = EtcPath(self.repodir, rpath)\n return d","def test_case_6():\n print(\"*********Test_case_6***********\")\n path = os.path.join(os.path.dirname(__file__), 'testdir', 't1.c')\n result = find_files('.c', path)\n print(result)","def find_old_files(directory):\n # go to the directory\n os.chdir(directory)\n \n # construct the find command to interogate the directory\n find_cmd = [\"find\"]\n find_cmd.append(\"*\")\n find_cmd.append(\"-atime\")\n find_cmd.append(\"+\"+str(DAYS_OLD))\n \n # run the find command\n try:\n find = subprocess.Popen(\" \".join(find_cmd),shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\n except:\n raise\n # get the standard output\n out, err = find.communicate() # get the standard output\n found = out.decode().split() # split the text into a list\n \n # go back to our starting directory \n os.chdir(iwd)\n \n return(found)","def find_file(self):\n selected_file = tk.filedialog.askopenfilename(initialdir='/', title='Select File',\n filetypes=(('txt Files', '*.txt'), ('All Files', '*.*')))\n self.markov_chain.add_file(selected_file)","def test_case_5():\n print(\"*********Test_case_5***********\")\n result = find_files('.c', \"\")\n print(result)","def initialise_languages():\n is_language_folder = r\"^[^\\\\\\.]*\" # Cannot have backslash or dot.\n language_folder_path = os.path.join(definitions.ROOT_DIR, \"languages\")\n\n for root, dirs, files in os.walk(language_folder_path):\n for name in files:\n if name.startswith(\"_NEW_\") or name.startswith(\"_CHANGED_\"):\n # Files that are auto-generated will not be added.\n continue\n\n full_dir = os.path.join(root, name)\n relative_dir = full_dir.replace(language_folder_path + \"\\\\\", \"\")\n\n match = re.match(is_language_folder, relative_dir)\n language_id = match.group(0)\n\n language = None\n if language_id != \"cache\":\n language = definitions.LANGUAGES[language_id]\n else:\n language = definitions.CACHE_LANGUAGE\n\n if relative_dir == \"{id}\\\\commands.json\".format(id=language_id):\n # Take the commands.\n definitions.COMMANDS.add_command_localisation(full_dir, language_id)\n\n elif relative_dir == \"{id}\\\\languages.json\".format(id=language_id):\n # Take the languages.\n language.add_languages(full_dir)\n\n elif relative_dir == \"{id}\\\\meta.json\".format(id=language_id):\n # Take the metadata.\n language.add_meta(full_dir)\n\n elif relative_dir == \"{id}\\\\permissions.json\".format(id=language_id):\n # Take the permissions.\n language.add_permission_names(full_dir)\n\n elif relative_dir == \"{id}\\\\units.json\".format(id=language_id):\n language.add_units(full_dir)\n\n else:\n # Take the keys\n language.add_keys_from_path(full_dir)\n\n for name in dirs:\n full_dir = os.path.join(root, name)\n relative_dir = full_dir.replace(language_folder_path + \"\\\\\", \"\")\n\n match = re.fullmatch(is_language_folder, relative_dir)\n if match is not None:\n if name != \"cache\":\n definitions.LANGUAGES[name] = Language(name)\n else:\n definitions.CACHE_LANGUAGE = Language(name)","def checkGit(directory):","def find_deleted_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n deleted_file_list = trans.find_deleted_files(\n REPOSITORY_NAME, branch, TARGET_LANG)\n\n moved_files = trans.find_moved_files_in_deleted_files(REPOSITORY_NAME, branch, TARGET_LANG, deleted_file_list)\n\n deleted_files = list(sorted(set(deleted_file_list) - set(moved_files.values())))\n\n if create_issue == 0:\n yield (\"move: \\n\" + \"\\n\".join(limit_result(list(sorted(moved_files.values())))))\n yield (\"delete: \\n\" + \"\\n\".join(limit_result(deleted_file_list)))\n else:\n yield (\"Processing....\")\n client = self._github_operator(msg)\n new_count, skip_count = build_move_issue(trans, client, branch, moved_files)\n yield (\"move: {} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n new_count, skip_count = build_delete_issue(trans, client, branch, deleted_file_list)\n yield (\"delete: {} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n yield (\"Please cache issues again.\")","def test_case_3():\n print(\"*********Test_case_3***********\")\n result = find_files('.c', None)\n print(result)","def _findfile(self,path,label):\n files=[];filenames=os.listdir(path)\n for name in filenames:\n if os.path.splitext(name)[0]==str(label):\n files.append(name)\n return files","def get_files_changed():\n files_list = []\n test = os.popen('git show --name-only')\n repo_location = os.popen('git rev-parse --show-toplevel')\n repo_location = repo_location.readlines()\n repo_location = repo_location[0]\n repo_location = repo_location.replace('\\n', '')\n if \"Not a git repository\" in repo_location:\n files_list.append(\"Not a git repository\")\n return files_list\n files_list.append(repo_location.split('/')[-1])\n output = test.readlines()\n for a in range(6, len(output)):\n files_list.append(output[a].replace('\\n', ''))\n return files_list","def get_changed_files(self, old_commit, new_commit):\n if old_commit is not None and not self.pygit.descendant_of(\n new_commit, old_commit\n ):\n raise ValueError(\"Second commit must be a descendant of first commit\")\n\n old_index = pygit2.Index()\n new_index = pygit2.Index()\n if old_commit is not None:\n old_tree = self.pygit.get(old_commit).tree\n old_index.read_tree(old_tree)\n else:\n # This is a special hash that represents an empty tree\n old_tree = self.pygit.get(\"4b825dc642cb6eb9a060e54bf8d69288fbee4904\")\n\n new_tree = self.pygit.get(new_commit).tree\n new_index.read_tree(new_tree)\n\n for patch in self.pygit.diff(old_tree, new_tree):\n if patch.delta.status_char() != \"M\":\n continue\n\n if not patch.delta.new_file.path.startswith(\"locales/\"):\n continue\n\n old_file_oid = old_index[patch.delta.old_file.path].oid\n new_file_oid = new_index[patch.delta.new_file.path].oid\n old_file = self.pygit.get(old_file_oid)\n new_file = self.pygit.get(new_file_oid)\n yield patch.delta.new_file.path, old_file.data, new_file.data","def scanForNewContent (self):\n\n self._logGeneral('scan-new-content-starting').debug2('starting to scan for new content')\n \n totalMetaFilesScanned = 0\n totalNewFilesAdded = 0\n\n metaFilesPattern = None\n if self._isPrediction is False:\n metaFilesPattern = self._metaFilesPattern\n else:\n metaFilesPattern = self._metaPredictionFilesPattern\n\n mminParam = self._cfg.modificationCheckThresholdMinutes\n if mminParam != None and mminParam == 0:\n mminParam = 1\n\n findCommand = \"find -L . -type f -name '%(filePattern)s'\" % {'filePattern':metaFilesPattern}\n if not self._isFirstScan and (mminParam != None):\n findCommand = \"find -L . -mmin -%(minutes)s -type f -name '%(filePattern)s'\" % {'minutes':str(mminParam), 'filePattern':metaFilesPattern}\n findCommand += \" | xargs --no-run-if-empty stat -c '%n %Y'\"\n\n self._logGeneral('new-content-find-command').debug2('going to search for new files. command: %s', findCommand)\n\n # We have set of meta file directories. go over each directory.\n for disk in self._disksSet:\n self._logGeneral('scan-new-files-starting-in-disk').debug3('starting to scan for new content in disk %02u', disk)\n\n metaRamRootDir = os.path.join(self._cfg.metaRamBaseDir, \"%02u\" % disk)\n metaPersistentRootDir = os.path.join(self._cfg.metaPersistentBaseDir, \"%02u\" % disk)\n data = None\n try:\n process = subprocess.Popen(findCommand, shell=True, stdout=subprocess.PIPE, cwd=metaRamRootDir, bufsize=65536)\n data = process.communicate()[0]\n except Exception as ex:\n self._logGeneral(\"cant-run-find\").error(\"error trying to run 'find' command. exception: %s\", ex)\n continue\n \n if data == None:\n self._logGeneral(\"no-output-data\").error(\"data from 'find' command is None.\")\n continue\n\n for line in data.split('\\n'):\n if len(line)==0:\n break \n\n # Split the output line to the relative path and the modification time\n # Relative path is: aa/bb/aabbsdaa12231.meta / aa/bb/aabbsdaa12231.meta.prd\n splittedLine = line.split(\" \")\n metaFileRelativePath = splittedLine[0]\n modificationTime = int(splittedLine[1])\n\n # Get the full meta path\n metaFileFullNamePath = os.path.join(metaRamRootDir, metaFileRelativePath)\n\n # Get only the meta file name: aabbsdaa12231.meta / aabbsdaa12231.meta.prd\n metaFileName = os.path.split(metaFileRelativePath)[1]\n # Get the cid\n cid = metaFileName.split('.')[0]\n\n # Get the meta persistent full name path\n metaPersistentFileFullNamePath = os.path.join(metaPersistentRootDir, metaFileRelativePath)\n \n # We check if self._keepRunning is up before we scan the next folder\n if not self._keepRunning:\n return totalMetaFilesScanned, totalNewFilesAdded\n\n # Count total files scanned\n self.counters['numTotalMetaFilesScanned'] += 1\n totalMetaFilesScanned += 1\n\n if (totalMetaFilesScanned % DO_PERIODIC_WORK_INTERVAL_RECOREDS) == 0:\n self._periodicWorkCallBack()\n\n updateLastAccess = False\n updateLastModificationTime = False\n isCounted = True\n isExist = False\n\n # If in dict, check if updates are needed\n if cid in self._cidLastAccessTimeDict:\n isExist = True\n # We check if we need to update modification time and bytes acquired.\n if KEY_LAST_MODIFICATION_TIME in self._cidLastAccessTimeDict[cid]:\n cidLastModificationTime = self._cidLastAccessTimeDict[cid][KEY_LAST_MODIFICATION_TIME]\n if modificationTime > cidLastModificationTime:\n updateLastModificationTime = True\n else:\n updateLastModificationTime = True\n\n if KEY_WAS_COUNTED not in self._cidLastAccessTimeDict[cid]:\n isCounted = False\n self._cidLastAccessTimeDict[cid][KEY_WAS_COUNTED] = 1\n\n cidCurrentLastAccessTime = self._cidLastAccessTimeDict[cid][KEY_LAST_ACCESS_TIME]\n if cidCurrentLastAccessTime < modificationTime:\n self._logGeneral('in-dict-update').debug2('cid=%s already exists in dict. updating its lastAccessTime from %d to %d', cid, cidCurrentLastAccessTime, modificationTime)\n updateLastModificationTime = True\n else:\n self._logGeneral('in-dict-no-update').debug2('cid=%s already exists in dict. no need to update lastAccessTime', cid)\n\n\n # If not exists or we need to update the modification time - we need to look for the bytes acquired inside the meta.\n bytesDiskSize = 0\n cgid = -1\n \n if (not isExist) or updateLastModificationTime or not isCounted or self._isFirstScan:\n (cgid, bytesDiskSize) = self._getCgidAndBytesUsed(cid, metaFileFullNamePath) \n\n # Update or insert to data base\n updateBytes = False\n bytesToAdd = 0;\n updateNumTitle = False\n if isExist:\n if not isCounted:\n updateNumTitle = True\n self._cidLastAccessTimeDict[cid][KEY_CGID]=cgid\n if updateLastModificationTime:\n updateBytes = True\n if KEY_LAST_BYTES_ON_DISK in self._cidLastAccessTimeDict[cid]:\n bytesToAdd = bytesDiskSize - self._cidLastAccessTimeDict[cid][KEY_LAST_BYTES_ON_DISK]\n else:\n bytesToAdd = bytesDiskSize\n self._cidLastAccessTimeDict[cid][KEY_LAST_BYTES_ON_DISK] = bytesDiskSize\n self._cidLastAccessTimeDict[cid][KEY_LAST_MODIFICATION_TIME] = modificationTime\n\n if updateLastAccess:\n self._cidLastAccessTimeDict[cid][KEY_LAST_ACCESS_TIME] = modificationTime\n else:\n bytesToAdd = bytesDiskSize\n updateBytes = True\n updateNumTitle = True\n self._cidLastAccessTimeDict[cid] = {KEY_LAST_ACCESS_TIME:modificationTime, KEY_META_FILE_FULL_NAME_PATH:metaFileFullNamePath,\\\n KEY_META_PERSISTENT_FILE_FULL_NAME_PATH:metaPersistentFileFullNamePath, KEY_DISK_NUM:disk, \\\n KEY_LAST_MODIFICATION_TIME:modificationTime, KEY_LAST_BYTES_ON_DISK: bytesDiskSize, KEY_WAS_COUNTED:1, \\\n KEY_CGID:cgid}\n self._logGeneral('new-content').debug2('new content has been found. cid=%s, metaFileFullNamePath=\"%s\"', cid, metaFileFullNamePath)\n self.counters['numTotalNewFilesAdded'] += 1\n totalNewFilesAdded += 1\n \n if self._isFirstScan:\n updateBytes = True\n bytesToAdd = bytesDiskSize;\n updateNumTitle = True\n\n # Update counters\n self._incrCounters(updateBytes, updateNumTitle, cgid, bytesToAdd)\n\n \n if self._isFirstScan:\n if len(self._cidLastAccessTimeDict) > totalMetaFilesScanned:\n self._logGeneral('meta-count-too-large').notice('number meta on disk is less then on dict. going to remove junk entries. totalMetaFilesScanned=%d, len(Dict)=%d', totalMetaFilesScanned, len(self._cidLastAccessTimeDict))\n self._scanLastAccessDictForJunk()\n self._isFirstScan = False\n\n return totalMetaFilesScanned, totalNewFilesAdded","def find_reference_files():\n for root, _, files in os.walk(\"./tests/references/\"):\n for basename in fnmatch.filter(files, \"*.tex\"):\n yield os.path.join(root, basename)","def find(self, file, version):\n matches = []\n for root, dirnames, filenames in os.walk(self.full_doc_path(version)):\n for filename in fnmatch.filter(filenames, file):\n matches.append(os.path.join(root, filename))\n return matches","def _get_files(repo, patterns, options):\n ctx = repo[None]\n match = match_func(repo, ctx, patterns, options)\n try:\n status = ctx.status(listclean=True, listignored=True, listunknown=True)\n except TypeError:\n # Compatibility with older Mercurial versions.\n status = ctx.status(clean=True, ignored=True, unknown=True)\n modified = status[0]\n added = status[1]\n unknown = status[4]\n ignored = status[5]\n clean = status[6]\n files = []\n for file_list in [clean, modified, added]:\n for filename in file_list:\n if match(filename):\n files.append(filename)\n for file_list in [ignored, unknown]:\n for filename in file_list:\n if match.exact(filename):\n files.append(filename)\n return files","def find_files(directory, extension, magictext):\n global files_logged\n global found_text\n dir_path = os.path.abspath(directory)\n dir_files = os.listdir(dir_path)\n for file in dir_files:\n if file.endswith(extension) and file not in files_logged:\n logger.info('New file found: {}'.format(file))\n files_logged.append(file)\n if file.endswith(extension):\n file_path = os.path.join(dir_path, file)\n if find_string_in_files(file_path, magictext):\n break\n for file in files_logged:\n if file not in dir_files:\n logger.info('File deleted: {}'.format(file))\n files_logged.remove(file)\n found_text[file] = 0","def getFiles(folderToProcess,filter):\n\n print(f\"Parsing {folderToProcess} for {filter} files\")\n\n if debug:\n for path in Path(folderToProcess).rglob(filter):\n print(f\"Found {path}\")\n\n all_files = [str(x) for x in Path(folderToProcess).rglob(filter)] \n\n return all_files","def calc_lang_features(commits, author):\n\tlang_features = ['/\\*\\*', '\\\\\"\\\\\"\\\\\"', '///', # documentation\n\t\t\t'^\\s*@', 'def.+:.+->', 'using\\s+System\\.ComponentModel\\.DataAnnotations', # assertion\n\t\t\t'assert', 'TODO', 'lambda']\n\n\t# delete contents\n\topen('lang_features.csv', 'w').close()\n\t\n\tfor count, commit in enumerate(commits):\n\t\t# status update\n\t\tif (count + 1) % 5 == 0:\n\t\t\tprint commit, '.. ..', count + 1, ' / ', len(commits)\n\n\n\t\t\t# for each blob modified\n\t\tquery = (\"for x in $(echo \" + commit + \" | ssh da4 ~/lookup/cmputeDiff2.perl); do \" +\n\t\t\t\t# get the chold and parent blob\n\t\t\t\t\"diff_blobs=$(echo $x | awk -v RS=';' 1 | sed -n '3,4 p');\" +\n\t\t\t\t# if a parent blob does not exist, the author authored all of the content of the file\n\t\t\t\t\"if [ $(echo $diff_blobs|wc -w) -eq 1 ]; then \" +\n\t\t\t\t\t\"echo $diff_blobs | ~/lookup/showCnt blob 2> /dev/null; \" +\n\t\t\t\t# if a parent blob exists, find the diff, in order to search only the modified lines\n\t\t\t\t\"elif [ $(echo $diff_blobs|wc -w) -eq 2 ]; then \" +\n\t\t\t\t\t\"vars=( $diff_blobs );\" +\n\t\t\t\t\t# using bash instead of sh in order to use the process substitution,\n\t\t\t\t\t# to get the modified lines\n\t\t\t\t\t\"/bin/bash -c \\\"diff <(echo ${vars[0]} | ~/lookup/showCnt blob)\" +\n\t\t\t\t\t\t\t\t\" <(echo ${vars[1]} | ~/lookup/showCnt blob)\\\";\" +\n\t\t\t\t\"fi;\" +\n\t\t\t# grep the above practices and discard the lines that were deleted from the parent blob\n\t\t\t# (they start with \">\" in diff)\n\t\t\t\"done | egrep \\\"\" + \"|\".join(lang_features) + \"\\\" | grep -v '^>' | wc -l \")\n\t\tcount_uses = int(bash(query).strip())\n\t\tif count_uses > 0: # good practice feature is used\n\t\t\tout = bash('echo ' + commit + ' | ~/lookup/getValues c2P')\n\t\t\tmain_proj = out.strip().split(';')[1]\n\t\t\ttime = search(commit, 'commit')[2]\n\n\t\t\tf = open(\"lang_features.csv\", \"a\")\n\t\t\tprint 'lang_f'\n\t\t\tf.write(author + ', ' + 'LANG_F' + ', ' + str(time) + ', ' + main_proj + ', ' + str(count_uses) + '\\n')\n\t\t\tf.close()\n\t\t\tprint 'wrote: -->', commit","def find(pattern):\n files = config.index.files(path_glob=\"*%s*\" % pattern)\n print_files(files)","def find_files(\n base,\n languages=all_languages,\n dir_ignore=default_dir_ignore,\n file_ignore=default_file_ignore,\n):\n if base[-1] != \"/\":\n base += \"/\"\n\n def update_dirs(dirs):\n \"\"\"strip the ignored directories out of the provided list\"\"\"\n index = len(dirs) - 1\n for i, d in enumerate(reversed(dirs)):\n if d in dir_ignore:\n del dirs[index - i]\n\n # walk over base\n for root, dirs, files in os.walk(base):\n root = root.replace(base, \"\", 1)\n\n # strip ignored directories from the list\n update_dirs(dirs)\n\n for filename in files:\n if filename in file_ignore:\n # skip ignored files\n continue\n\n # try to figure out the language of the specified file\n fullpath = os.path.join(base, root, filename)\n language = lang_type(fullpath)\n\n # if the file is one of the langauges that we want return\n # its name and the language\n if language in languages:\n yield fullpath, language","def _select_search_file(self, change):\n self.file = change.get(\"new\")","def check_file_references(name):\n references = []\n for root, dirs, files in walk(Path(\".\")):\n for file_name in files:\n if is_notebook(file_name):\n nb_markdown_cells = markdown_cells(os.path.join(root, file_name))\n for cell in nb_markdown_cells:\n for line in cell:\n if name in line:\n references.append(file_name)\n else:\n with open(os.path.join(root, file_name), encoding=\"utf8\", errors='ignore') as non_nb_file:\n if name in non_nb_file.read():\n references.append(file_name)\n return references","def find(query):\n acc = []\n for root, dirs, files in os.walk(query, topdown=False):\n for name in files:\n acc += [os.path.join(root, name)]\n return acc","def test_case_1():\n print(\"*********Test_case_1***********\")\n path = os.path.join(os.path.dirname(__file__), 'testdir')\n result = find_files('.c', path)\n for file in result:\n print(file)","def files_in_folder(self):\n non_til = set()\n filesInFolder = []\n for f in self.find_all_files():\n newstr = f.replace(\"~\", \"\") \n if newstr in self.find_all_files():\n non_til.add(newstr)\n for fs in non_til:\n filesInFolder.append(fs)\n return filesInFolder","def FindCheckerFiles(path):\n if not path:\n Logger.fail(\"No source path provided\")\n elif os.path.isfile(path):\n return [ path ]\n elif os.path.isdir(path):\n foundFiles = []\n for root, dirs, files in os.walk(path):\n for file in files:\n extension = os.path.splitext(file)[1]\n if extension in [\".java\", \".smali\"]:\n foundFiles.append(os.path.join(root, file))\n return foundFiles\n else:\n Logger.fail(\"Source path \\\"\" + path + \"\\\" not found\")","def _search(self, log, progressbar):\n self._urls = []\n for filename in os.listdir(self._path):\n url = 'file:////' + filename\n self._urls.append(url)\n self._urls.sort()","def detect_changed_files(self) -> list[Path]:\n repos = [(self.open_repo(), self.git_directory)]\n # Check server and api dirs too\n # Normally these are ignored but we need to check these\n if (server_repo_path := Path(self.git_directory, \"Paper-Server\")).exists():\n repos.append((pygit2.Repository(str(server_repo_path)), server_repo_path))\n if (api_repo_path := Path(self.git_directory, \"Paper-API\")).exists():\n repos.append((pygit2.Repository(str(api_repo_path)), api_repo_path))\n changed = []\n for repo, repo_path in repos:\n changed.extend(p.relative_to(self.git_directory) for p in detect_changed_files(repo, repo_path))\n changed.sort()\n return changed","def search_translation(file_name,line_original_language,line_new_language,to_translate):\n csv_file = open(file_name, 'rb')\n rd = csv.reader(csv_file, delimiter=';',quoting=csv.QUOTE_ALL)\n nb_row = 0\n for row in rd:\n nb_row += 1\n if line_original_language >= 0 and line_new_language >= 0:\n if row[line_original_language] == to_translate:\n to_translate = row[line_new_language]\n break\n return to_translate","def search_for_files(\n run_name: str,\n raw_extension: Optional[str] = None,\n cellpy_file_extension: Optional[str] = None,\n raw_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n project_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n cellpy_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n prm_filename: Union[pathlib.Path, str, None] = None,\n file_name_format: Optional[str] = None,\n reg_exp: Optional[str] = None,\n sub_folders: Optional[bool] = True,\n file_list: Optional[List[str]] = None,\n with_prefix: Optional[bool] = True,\n pre_path: Union[OtherPath, pathlib.Path, str, None] = None,\n) -> Tuple[List[str], str]:\n\n # TODO: @jepe - use reg_exp\n # TODO: @jepe - allow for searching in cloud\n # TODO: @jepe - how to implement searching in db?\n # TODO: update prms and conf file to allow for setting if search should be done in\n # sub-folders, several folders, db, cloud etc\n # TODO: @jepe - find a way to implement automatic file_list creation in a top level func.\n logging.debug(f\"searching for {run_name}\")\n version = 0.4\n t0 = time.time()\n\n if reg_exp is None:\n reg_exp = prms.FileNames.reg_exp\n\n if raw_extension is None:\n raw_extension = prms.FileNames.raw_extension\n\n if raw_extension is None:\n raw_extension = prms.FileNames.raw_extension\n\n if cellpy_file_extension is None:\n cellpy_file_extension = prms.FileNames.cellpy_file_extension\n\n # backward compatibility check:\n if cellpy_file_extension.startswith(\".\"):\n warnings.warn(\n \"Deprecation warning: cellpy_file_extension should not include the '.'\"\n )\n cellpy_file_extension = cellpy_file_extension[1:]\n\n if raw_file_dir is None:\n raw_file_dir = prms.Paths.rawdatadir\n\n if file_name_format is None:\n file_name_format = prms.FileNames.file_name_format\n\n # 'dressing' the raw_file_dir in a list in case we want to\n # search in several folders (not implemented yet):\n if not isinstance(raw_file_dir, (list, tuple)):\n raw_file_dir = [OtherPath(raw_file_dir)]\n else:\n raw_file_dir = [OtherPath(d) for d in raw_file_dir]\n\n if project_dir is not None:\n raw_file_dir = [r / project_dir for r in raw_file_dir]\n\n if reg_exp:\n logging.warning(f\"Got reg_exp: {reg_exp}\")\n logging.warning(\"Sorry, but using reg exp is not implemented yet.\")\n\n if prm_filename is not None:\n logging.debug(\"Sorry, reading prm file is not implemented yet.\")\n\n if cellpy_file_dir is None:\n cellpy_file_dir = OtherPath(prms.Paths.cellpydatadir)\n else:\n cellpy_file_dir = OtherPath(cellpy_file_dir)\n\n if file_name_format is None and reg_exp is None:\n try:\n # To be implemented in version 0.5:\n file_name_format = prms.FileNames.file_name_format\n except AttributeError:\n file_name_format = \"YYYYMMDD_[name]EEE_CC_TT_RR\"\n\n if file_name_format.upper() == \"YYYYMMDD_[NAME]EEE_CC_TT_RR\":\n # backward compatibility check\n if raw_extension.startswith(\".\"):\n warnings.warn(\n \"Deprecation warning: raw_extension should not include the '.'\"\n )\n raw_extension = raw_extension[1:]\n warnings.warn(f\"Removing it -> {raw_extension}\")\n # TODO: give warning/error-message if run_name contains more than one file (due to duplicate in db)\n glob_text_raw = f\"{run_name}*.{raw_extension}\"\n else:\n glob_text_raw = file_name_format\n\n logging.debug(f\"searching for raw files in: {raw_file_dir}\")\n logging.debug(f\"searching for {run_name}\")\n logging.debug(f\"using glob {glob_text_raw}\")\n\n cellpy_file = f\"{run_name}.{cellpy_file_extension}\"\n cellpy_file = cellpy_file_dir / cellpy_file\n # Not sure if for example pandas can handle OtherPath objects:\n cellpy_file = cellpy_file.full_path\n\n logging.debug(f\"generated cellpy filename {cellpy_file}\")\n\n if file_list is not None:\n if len(raw_file_dir) > 1:\n logging.info(\"you provided several raw file directories\")\n logging.debug(\"searching within provided list of files\")\n\n if with_prefix:\n glob_text_raw_for_list = f\"*{glob_text_raw}\"\n else:\n glob_text_raw_for_list = glob_text_raw\n\n run_files = fnmatch.filter(file_list, glob_text_raw_for_list)\n\n if pre_path is not None:\n pre_path = OtherPath(pre_path)\n run_files = list(map(lambda x: pre_path / x, run_files))\n\n else:\n run_files = []\n for d in raw_file_dir:\n if d.is_external:\n logging.debug(\"external file\")\n if not d.is_dir():\n warnings.warn(\"your raw file directory cannot be accessed!\")\n # raise cellpy.exceptions.IOError(\"your raw file directory cannot be accessed!\")\n _run_files = []\n else:\n logging.debug(f\"checking in folder {d}\")\n logging.debug(f\"{sub_folders=}\")\n if sub_folders:\n _run_files = d.rglob(glob_text_raw)\n\n else:\n _run_files = d.glob(glob_text_raw)\n\n _run_files = [str(_f.resolve()) for _f in _run_files]\n _run_files.sort()\n run_files.extend(_run_files)\n\n return run_files, cellpy_file","def find_enc(self,en_code):\n encode = \"(.\"+en_code+\")\"\n file_of_type = []\n for file_en in self.files_in_folder():\n found = re.search(encode, file_en)\n if found:\n file_of_type.append(file_en)\n return file_of_type","def _get_changed_files():\n if not ci_diff_helper:\n return None\n\n try:\n config = ci_diff_helper.get_config()\n except OSError: # Not on CI.\n return None\n\n changed_files = ci_diff_helper.get_changed_files('HEAD', config.base)\n\n changed_files = set([\n './{}'.format(filename) for filename in changed_files])\n\n return changed_files","def __searchFiles(self):\n self.ui.showFindFilesDialog(self.textForFind())","def luke_filewalker(Malware_bin):\n file_analyses=[]\n for (dirpath, dirname, filenames) in os.walk(Malware_bin):\n for f in filenames:\n file_analyses.append(os.path.join(Malware_bin,f))\n #print \"Added : \"+'\\033[1m'+f+'\\033[0m'\n return file_analyses","def filter_changed_files(changed_files, path_to_repository, file_encoding):\n filtered_files = []\n for changed_file in changed_files:\n file_is_valid = True\n if (os.path.isdir(os.path.join(path_to_repository, changed_file))):\n #ignore directories (e.g., git submodule folders)\n continue\n if os.path.getsize(os.path.join(path_to_repository, changed_file)) > 1 * 1024 * 1024:\n print('File too large for precommit analysis. Ignoring: %s' % changed_file)\n file_is_valid = False\n\n try:\n with open(os.path.join(path_to_repository, changed_file), encoding=file_encoding) as file:\n file.read()\n except UnicodeDecodeError:\n encoding_string = file_encoding\n if encoding_string is None:\n encoding_string = locale.getpreferredencoding() + ' (system encoding)'\n\n print(\n 'File at %s is not encoded in %s. Try using the --file-encoding option.' % (\n changed_file, encoding_string))\n file_is_valid = False\n\n if file_is_valid:\n filtered_files.append(changed_file)\n\n return filtered_files","def get_changed_files_and_content(path_to_repository, file_encoding, ignore_subrepositories):\n changed_files = filter_changed_files(get_changed_files(path_to_repository, ignore_subrepositories),\n path_to_repository, file_encoding)\n return {filename: open(os.path.join(path_to_repository, filename), encoding=file_encoding).read() for filename in\n changed_files}","def _findFiles(self, inputfolder):\n protofile, caffemodel = None, None\n files = os.listdir(inputfolder)\n for f in files:\n name, ext = splitext(f)\n if ext == '.caffemodel':\n caffemodel = join(inputfolder, f)\n elif f == 'deploy.prototxt':\n protofile = join(inputfolder, f)\n return protofile, caffemodel","def _find_changes(self):\n added = set()\n modified = set()\n existing_files = set()\n for dirpath_str, _, filenames in walk(str(self.path)):\n dirpath = Path(dirpath_str)\n for filename in filenames:\n if filename == DB_FILENAME:\n continue\n abs_filename = (dirpath / filename).absolute()\n if abs_filename in self.entries:\n entry = self.entries[abs_filename]\n existing_files.add(entry)\n st = lstat(str(abs_filename))\n if entry != st:\n modified.add(entry)\n else:\n try:\n entry = HashEntry(abs_filename)\n entry.update_attrs()\n added.add(entry)\n except FileNotFoundError:\n # If file was removed between listing and processing,\n # just treat it as if it never existed\n # We have nothing to compare it to anyway\n pass\n removed = set(self.entries.values()) - existing_files\n return added, removed, modified","def process_files(file_location, day):\n # construct file path\n file_dir = PREFIX+file_location\n file_pattern = file_dir+'lz_'+day+'*_raw.root'\n # print(file_pattern)\n file_list = glob.glob(file_pattern)\n print(\"There are %s MC files in the requested directory (%s).\" %(len(file_list), file_dir))\n file_names = []\n for f in file_list:\n file_name_only = f.split('/')\n file_names.append(file_name_only[-1])\n return file_names","def get_list_of_copied_sample_files(self, repo, changeset_revision, dir):\n deleted_sample_files = []\n sample_files = []\n for changeset in hg_util.reversed_upper_bounded_changelog(repo, changeset_revision):\n changeset_ctx = repo.changectx(changeset)\n for ctx_file in changeset_ctx.files():\n ctx_file_name = basic_util.strip_path(ctx_file)\n # If we decide in the future that files deleted later in the changelog should\n # not be used, we can use the following if statement. if ctx_file_name.endswith( '.sample' )\n # and ctx_file_name not in sample_files and ctx_file_name not in deleted_sample_files:\n if ctx_file_name.endswith('.sample') and ctx_file_name not in sample_files:\n fctx = hg_util.get_file_context_from_ctx(changeset_ctx, ctx_file)\n if fctx in ['DELETED']:\n # Since the possibly future used if statement above is commented out, the\n # same file that was initially added will be discovered in an earlier changeset\n # in the change log and fall through to the else block below. In other words,\n # if a file named blast2go.loc.sample was added in change set 0 and then deleted\n # in changeset 3, the deleted file in changeset 3 will be handled here, but the\n # later discovered file in changeset 0 will be handled in the else block below.\n # In this way, the file contents will always be found for future tools even though\n # the file was deleted.\n if ctx_file_name not in deleted_sample_files:\n deleted_sample_files.append(ctx_file_name)\n else:\n sample_files.append(ctx_file_name)\n tmp_ctx_file_name = os.path.join(dir, ctx_file_name.replace('.sample', ''))\n fh = open(tmp_ctx_file_name, 'wb')\n fh.write(fctx.data())\n fh.close()\n return sample_files, deleted_sample_files","def get_changed_files():\n upstream = \"origin/master\"\n local_commit = subprocess.check_output(\n \"git rev-list HEAD ^{} -- 2>/dev/null | tail -1\".format(upstream),\n shell=True).strip().decode()\n diff_base = subprocess.check_output(\n ['git', 'rev-parse', local_commit +\n '^']).strip().decode() if local_commit else \"HEAD\"\n files = subprocess.check_output(['git', 'diff', '--name-only',\n diff_base]).strip().decode().split('\\n')\n\n repo = subprocess.check_output(['git', 'rev-parse',\n '--show-toplevel']).strip().decode()\n # add prefixes so that all and targets can be specified relative to FUCHSIA_DIR\n if repo.endswith('topaz'):\n files = [os.path.join('topaz', p) for p in files]\n elif repo.endswith('third_party/go'):\n files = [os.path.join('third_party/go', p) for p in files]\n\n return files","def find_todo():\n remove_pyc_files()\n local('grep -ir \"TODO\" *')","def search(self):\n files = os.listdir(self.filePath)\n txt_file = []\n for f in files:\n f_ext = f.split('.')[-1]\n if f_ext == self.flag:\n if self.flag == 'txt':\n txt_file.append(FileTxt(os.sep.join([self.filePath, f])))\n\n if self.flag == 'csv':\n txt_file.append(FileCsv(os.sep.join([self.filePath, f])))\n\n return txt_file","def _search(self, btn):\n del btn\n if self.txt_search.value:\n found_files: Optional[List[Path]] = None\n while found_files is None:\n try:\n found_files = list(self.current_folder.rglob(self.txt_search.value))\n except FileNotFoundError:\n pass\n self.select_search.options = [\n str(file) for file in found_files if file.exists()\n ]","def list_all_files(self, branch_name) -> dict:\n try:\n files_response = self.repo.get_contents(self.repo_folder,\n ref=branch_name)\n files_list = []\n for file in files_response:\n files_list.append(file.raw_data.get('path'))\n return make_success_response(200, files_list)\n except GithubException as github_exc:\n return make_error_response(github_exc.status, github_exc.data)","def ImportRepo(\n session: orm.session.Session,\n language: scrape_repos_pb2.LanguageToClone,\n metafile: pathlib.Path,\n) -> None:\n meta = pbutil.FromFile(metafile, scrape_repos_pb2.GitHubRepoMetadata())\n clone_dir = metafile.parent / f\"{meta.owner}_{meta.name}\"\n repo = contentfiles.GitHubRepository.GetOrAdd(session, meta)\n repo.language = language.language\n session.flush()\n\n for importer in language.importer:\n if not importer.source_code_pattern:\n app.Error(\"No source_code_pattern specified! Stopping now.\")\n return\n\n pat = importer.source_code_pattern\n pat = f\"{clone_dir}/{pat[1:]}\" if pat[0] == \"^\" else f\"{clone_dir}/{pat}\"\n cmd = [\n \"find\",\n str(clone_dir),\n \"-type\",\n \"f\",\n \"-regex\",\n pat,\n \"-not\",\n \"-path\",\n \"*/.git/*\",\n ]\n app.Log(2, \"$ %s\", \" \".join(cmd))\n paths = (\n subprocess.check_output(cmd, universal_newlines=True).rstrip().split(\"\\n\")\n )\n if len(paths) == 1 and not paths[0]:\n app.Log(2, \"No files to import from %s\", clone_dir)\n return\n app.Log(\n 1,\n \"Importing %s '%s' files from %s ...\",\n humanize.Commas(len(paths)),\n importer.source_code_pattern,\n clone_dir.name,\n )\n all_files_relpaths = public.GetAllFilesRelativePaths(clone_dir)\n\n # Use progress bar if there's a lot of files to process.\n if len(paths) > 1000:\n bar = progressbar.ProgressBar(max_value=len(paths))\n else:\n bar = lambda x: x\n\n for path in bar(paths):\n job = scrape_repos_pb2.ImportWorker(\n clone_from_url=repo.clone_from_url,\n clone_dir=str(clone_dir),\n abspath=path,\n all_files_relpaths=all_files_relpaths,\n preprocessors=importer.preprocessor,\n )\n for output in ImportWorker(job):\n session.add(output)","def test_013_find_files(self):\n HEADING()\n db = self.db\n\n db.connect()\n\n # Clear all jobs currently in the database to ensure a correct final assertion\n db.clear()\n\n # Add the jobs outlined in the YAML file\n db.add_from_yaml(\"etc/jobs.yaml\")\n inputs, outputs = db.find_jobs_with_file(\"in1.txt\")\n\n # Assert that the lengths of the inputs and outputs arrays are correct\n count_fgrep = len(Shell.fgrep(\"in1.txt\", \"etc/jobs.yaml\").strip().split(\"\\n\"))\n assert(len(inputs) == count_fgrep)","def wingrep(self):\n for folder, files_ in self.walk():\n listed_files = self.list_appro_files(folder, files_)\n for file_o in self.open_files(listed_files=listed_files):\n self.search_in(file_o)","def changelog(self, branch, since=None):\n walker = Walker(self.repo, [self.latest_branch_revision(branch)])\n for entry in walker:\n if since is not None and entry.commit.id == since:\n break\n commit = entry.commit\n files = Command(\n 'git show --pretty=\"format:\" --name-only %s' % commit.id,\n cwd=self.path,\n ).out.split()\n yield Commit(\n commit.id,\n commit.committer,\n datetime.fromtimestamp(commit.commit_time),\n commit.message,\n files,\n )","def get_changed_files_from(old_commit_sha, new_commit_sha):\n return check_output(\n \"git diff-tree --no-commit-id --name-only -r {0}..{1}\".format(\n old_commit_sha,\n new_commit_sha\n ).split(\" \")\n ).decode('utf-8').strip()","def findRepositoryByBackTracking():\n \n cLookBack = '.'\n while(True):\n cDir = os.path.abspath(cLookBack)\n print(\"Searching in %s\" % cDir)\n if os.path.isdir( os.path.join(cDir, DB_SUBFOLDER) ):\n return cDir\n else:\n if os.path.abspath(cLookBack) == os.path.abspath(cLookBack + '/..'):\n return os.path.abspath('.')\n cLookBack = cLookBack + '/..'\n \n return cDir","def _on_path(self, change):\n if change.new:\n self._git = G.Repo.init(change.new)\n ignore = Path(change.new) / \".gitignore\"\n if not ignore.exists():\n ignore.write_text(\".ipynb_checkpoints/\")\n self.commit(\"initial commit\")\n\n self._initialize_watcher()\n self._update_head_history()","def search_file(repos, path_parts, rev, search_re):\n\n # Read in each line of a checked-out file, and then use re.search to\n # search line.\n fp = repos.openfile(path_parts, rev, {})[0]\n matches = 0\n while 1:\n line = fp.readline()\n if not line:\n break\n # FIXME: Is there what can we do about file encoding?\n if search_re.search(line.decode(\"utf-8\", \"surrogateescape\")):\n matches = 1\n fp.close()\n break\n return matches","def find_build(branch, tag, build_path=None, old_build_path=None):\n for directory_format in [build_path, old_build_path]:\n if directory_format is None:\n continue\n loc = directory_format % (branch, tag)\n if isdir(loc):\n return loc\n raise BuildNotFound(branch, tag)","def filter_files(files, lang=EXTES):\n\n lang_specific = []\n\n for f in files:\n if f.endswith(lang):\n lang_specific.append(f)\n return lang_specific","def search_file(filename, term):\n try:\n filepath = root + filename\n with open(filepath) as file:\n return term in file.read().splitlines()\n except FileNotFoundError:\n with open(filepath,\"w+\") as file:\n return (search_file(filename, term))","def find_files(directory, patterns):\n for root, dirs, files in os.walk(directory):\n for basename in files:\n if \".pyc\" not in basename and \"__pycache__\" not in basename:\n for pattern in patterns:\n if fnmatch.fnmatch(basename, pattern):\n filename = os.path.join(root, basename)\n yield filename","def jjb_files(project, release):\n files, skipped = set(), set()\n file_ending = re.compile(r'ya?ml$')\n search_string = re.compile(r'^\\s+stream:')\n release_string = re.compile(r'- %s:' % release)\n jjb_path = os.path.join('jjb', project)\n\n if not os.path.isdir(jjb_path):\n logging.warn(\"JJB directory does not exist at %s, skipping job \"\n \"creation\", jjb_path)\n return (files, skipped)\n\n for file_name in os.listdir(jjb_path):\n file_path = os.path.join(jjb_path, file_name)\n if os.path.isfile(file_path) and file_ending.search(file_path):\n if has_string(file_path, release_string):\n skipped.add(file_path)\n elif has_string(file_path, search_string):\n files.add(file_path)\n return (files, skipped)","def find_FEfiles(self):\n \n\t\t# take each item in the list of files to eve.\n for i in self.files_to_find:\n filename1 = i[0] \n file1_dye = i[1]\n filename2 = i[2]\n file2_dye = i[3]\n out_file_prefix=i[4]\n\n # set filename as none to help identify when no match has been found below\n file1_filename = None\n file2_filename = None\n\n # search for a FE file which matches the filename pattern\n for afile in os.listdir(self.chosenfolder):\n # file 1\n if fnmatch.fnmatch(afile, filename1):\n file1_filename = afile\n \n # file 2\n if fnmatch.fnmatch(afile, filename2):\n file2_filename = afile\n \n # if both files have been identified add this to a new list\n if file1_filename and file2_filename:\n self.list_of_files.append((file1_filename, file1_dye, file2_filename, file2_dye,out_file_prefix))\n\t\t\t# if either file could not be identified report this.\n else:\n raise ValueError(\"no match for \" + filename1 + \" and \" + filename2)","def _bbcomponentwalk(self):\n for path, subdirs, files in os.walk(self.oedir()):\n try:\n del subdirs[subdirs.index('.svn')]\n\n except ValueError:\n pass\n \n for file in files:\n pf, ext = os.path.splitext(file)\n if ext == '.bb':\n yield pf.partition('_')[0]","def searchfiles(pattern='C:\\\\RoboDK\\\\Library\\\\*.rdk'):\n import glob\n return glob.glob(pattern)","def check_branch(\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\n ) -> None:\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\n\n if not RE_BRANCH_NAME.match(branch_name):\n res.append(f\"{comp_name}: Invalid branch name: {branch_name}\")\n\n for ver, version in sorted(branch.items()):\n if not RE_VERSION_STRING.match(ver):\n res.append(f\"{comp_name}/{branch_name}: Invalid version string: {ver}\")\n\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\n if version.outdated:\n update_to = [\n o_version\n for o_version in branch.values()\n if not o_version.outdated\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\n == other_cksums\n ]\n if len(update_to) != 1:\n res.append(\n f\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \"\n f\"versions to update to instead of exactly one\"\n )\n else:\n bad_files = sorted(\n relpath\n for relpath, (path, fdata) in driver_cksums.items()\n if util.file_sha256sum(path) != fdata.sha256\n )\n if bad_files:\n res.append(f\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\")\n\n if not uptodate_files:\n uptodate_files = driver_cksums\n elif uptodate_files != driver_cksums:\n res.append(\n f\"{comp_name}/{branch_name}: All the up-to-date versions should \"\n f\"define the same set of files with the same checksums\"\n )\n\n if not any(not version.outdated for version in branch.values()):\n res.append(f\"{comp_name}/{branch_name}: No non-outdated versions\")","def findMayaFiles(directory):\n\n pass","def match_files(gold_folder, sys_folder):\n\n print \"Compiling files...\"\n # Get a list of files in the folders supplied.\n gold_files = compile_files(gold_folder) # nnnnG.xml\n sys_files = compile_files(sys_folder) # nnnnXXN.xml\n\n print \"%d gold files found in %s\" % (len(gold_files), base_name(gold_folder))\n print \"%d system files found in %s\\n\" % (len(sys_files), base_name(sys_folder))\n\n print \"Matching system files to gold files...\"\n # Match them up, where nnnn must be common in a pair.\n pairs = [(f1, f2) for f1 in gold_files for f2 in sys_files\n if base_name(f2).startswith(base_name(f1).split(\"GE.\")[0])]\n\n return pairs","def __gitBranchList(self):\n self.vcs.gitListTagBranch(self.project.getProjectPath(), False)","def coffeescript_files():\r\n dirs = \" \".join(THEME_COFFEE_PATHS + [Env.REPO_ROOT / coffee_dir for coffee_dir in COFFEE_DIRS])\r\n return cmd('find', dirs, '-type f', '-name \\\"*.coffee\\\"')","def get_relevant_files(self):\n relevant_files = []\n\n if self.tree_cache is None:\n return relevant_files\n\n tree = json.loads(self.tree_cache)\n if \"commit\" in tree:\n commit_data = tree[\"commit\"]\n master_commit_files = commit_data[\"files\"]\n\n for patched_files in master_commit_files:\n relevant_file_path = \"./\" + patched_files[\"path\"]\n relevant_files.append(relevant_file_path)\n\n return relevant_files","def find_files(suffix, path):\n returnList = []\n find_file_recursive(suffix, path, returnList)\n return returnList","def unix_find(pathin):\n return [os.path.join(path, file)\n for (path, dirs, files) in os.walk(pathin, followlinks=False)\n for file in files]","def getFiles(folder, pattern, labelfile):\n # read labelfile\n with open(labelfile, 'r') as f:\n all_lines = f.readlines()\n \n # get filenames from labelfile\n all_files = []\n labels = []\n check = True\n for line in all_lines:\n # using shlex we also allow spaces in filenames when escaped w. \"\"\n splits = shlex.split(line)\n file_name = splits[0]\n class_id = splits[1]\n\n # strip all known endings, note: os.path.splitext() doesnt work for\n # '.' in the filenames, so let's do it this way...\n for p in ['.pkl.gz', '.txt', '.png', '.jpg', '.tif', '.ocvmb','.csv']:\n if file_name.endswith(p):\n file_name = file_name.replace(p,'')\n\n # get now new file name\n true_file_name = os.path.join(folder, file_name + pattern)\n all_files.append(true_file_name)\n labels.append(class_id)\n\n return all_files, labels","def flake8_scan_file(commit_sha, owner, repo, parent_sha=None):\n if parent_sha is None:\n parent_sha = get_commit_parent(commit_sha, owner, repo)\n diff_url = GIT_COMPARE_URL.format(base=parent_sha,\n head=commit_sha,\n owner=owner,\n repo=repo,\n host=host_api)\n diff_info = get(diff_url, auth=auth).json()\n diff_content = get(diff_url,\n auth=auth,\n headers={\"Accept\": \"application/vnd.github.v3.diff\"}\n ).content.decode('utf8')\n patch_set = PatchSet(diff_content)\n comments_per_file = {}\n for file in diff_info['files']:\n content = get(file['contents_url'], auth=auth).json()\n file_content = get(content['download_url']).content\n with open(\"flake8_tmp_file.py\", 'wb') as test_file:\n test_file.write(file_content)\n style_guide = flake8.get_style_guide(ignore=['E24', 'W503'])\n style_guide.input_file('./flake8_tmp_file.py', )\n results = style_guide._application.file_checker_manager.checkers[\n 0].results\n comments_per_line = {}\n for code, line_n, offset, text, src in results:\n if changed_in_diff(get_file_by_name(patch_set,\n file['filename']), line_n):\n comments = comments_per_line.get(line_n, [])\n comments.append((file['filename'], line_n, offset, code, text))\n comments_per_line[line_n] = comments\n comments_per_file[file['filename']] = comments_per_line\n return comments_per_file","def get_files(self):\n\n cur = self.app.conn.cursor()\n sql = \"select distinct case_text.fid, source.name from case_text join source on case_text.fid=source.id where \"\n sql += \"caseid=? order by lower(source.name) asc\"\n cur.execute(sql, [self.case['caseid'], ])\n self.casefiles = cur.fetchall()\n sql = \"select id, name, fulltext, mediapath, memo, owner, date, av_text_id from source order by source.name asc\"\n cur.execute(sql)\n self.allfiles = cur.fetchall()\n msg = _(\"Files linked: \") + str(len(self.casefiles)) + \" / \" + str(len(self.allfiles))\n self.ui.label_files_linked.setText(msg)","def changed_files(conn, scan0, scan1):\n c = conn.cursor()\n c.execute(\"SELECT a.pathid as pathid, a.hashid, b.hashid FROM \" +\n \"( SELECT pathid, hashid, scanid FROM files WHERE scanid=?) AS 'a' \" +\n \"JOIN (SELECT pathid, hashid, scanid FROM FILES WHERE scanid=?) as 'b' \" +\n \"ON a.pathid=b.pathid WHERE a.hashid != b.hashid\",\n (scan0, scan1))\n return (DBFile(f) for f in c)","def touch_files_dependent_on_changes(kymera_path, dirs, suffixes, changes):\n for dir in dirs:\n if dir[0] != '/':\n # This is a relative path to kymera root\n dir = kymera_path + dir\n if not os.path.exists(dir):\n print \"Directory %s included in ALL_SRCDIRS, ALL_INCDIRS or CFG_LIBS doesn't exist, continuing...\" % dir\n else:\n for file_name in os.listdir(dir):\n full_file_path= os.path.join(dir, file_name)\n # Filter a list of filenames down to those with one of the given suffixes\"\n if matching_file(suffixes, full_file_path):\n # Find all the files from a set with one of a list of suffices\n # containing one of the changed definitions\n if grep_words(changes, full_file_path):\n print \"Mark file for rebuild:\", full_file_path\n touch_file(full_file_path)","def get_list_of_comitted_files():\n files = []\n output = []\n try:\n output = subprocess.check_output(['git','diff-index', '--name-status', '--cached','HEAD']\n ).decode(\"utf-8\")\n except subprocess.CalledProcessError:\n print(\"Error diff files get: trace %s\" % subprocess.CalledProcessError)\n return files\n\n for result in output.split(\"\\n\"):\n logging.info(result)\n if result != '':\n match = modified.match(result)\n if match:\n files.append(match.group('name'))\n\n return files","def get_files_from_of_type(path: str, ext: str) -> List[str]:\n files = []\n for root, dirnames, filenames in os.walk(path):\n for filename in fnmatch.filter(filenames, \"*.\" + str(ext)):\n files.append(os.path.join(root, filename))\n if not files:\n logging.error(\"No language files found in folder: \" + str(os.sep.join([convert_vars.BASE_PATH, \"source\"])))\n logging.debug(f\" --- found {len(files)} files of type {ext}. Showing first few:\\n* \" + str(\"\\n* \".join(files[:3])))\n return files","def cb_filelist(args):\n req = args[\"request\"]\n\n pyhttp = req.getHttp()\n config = req.getConfiguration()\n pathinfo = pyhttp[\"PATH_INFO\"]\n\n if not pathinfo.startswith(\"/\" + TRIGGER):\n return\n\n logger = tools.getLogger()\n\n data = req.getData()\n data[INIT_KEY] = 1\n datadir = config[\"datadir\"]\n data['root_datadir'] = config['datadir']\n wikidir = config.get(\"wikidir\", config['datadir'])\n\n # convert the / to os.sep so that we can use os.path stuff.\n wikidir = wikidir.replace(\"/\", os.sep)\n if not wikidir.endswith(os.sep):\n wikidir = wikidir + os.sep\n\n page_name = pathinfo[len(\"/\" + TRIGGER)+1:]\n\n if not page_name:\n return\n\n page_name = page_name.replace(\"/\", os.sep)\n\n if not page_name:\n return\n\n if page_name.endswith(os.sep):\n page_name = page_name[:-1]\n\n # if the page has a flavour, we use that. otherwise\n # we default to the wiki flavour\n page_name, flavour = os.path.splitext(page_name)\n if flavour:\n data[\"flavour\"] = flavour[1:]\n\n # wikifile should hold the absolute path on the file system to\n # the wiki file we're looking at. if it's in a parent directory\n # of wikidir, then we abort. \n wikifile = os.path.normpath(os.path.join(wikidir, page_name))\n if not wikifile.startswith(wikidir):\n logger.info(\"wiki file requested '%s' is not in wikidir.\" % wikifile)\n return []\n\n # we build our own config dict for the fileentry to kind of\n # fake it into loading this file correctly rather than\n # one of the entries.\n newdatadir = wikidir\n\n ext = tools.what_ext(data[\"extensions\"].keys(), wikifile)\n\n if not ext:\n logger.info(\"wiki file '%s' does not exist.\" % wikifile)\n return []\n\n data['root_datadir'] = page_name + '.' + ext\n data['bl_type'] = 'file'\n wikifile = wikifile + \".\" + ext\n\n if not os.path.isfile(wikifile):\n return []\n\n fe = FileEntry(req, wikifile, wikidir)\n\n # now we evaluate python code blocks\n body = fe.getData()\n body = eval_python_blocks(req, body)\n body = \"<!-- STATIC PAGE START -->\\n\\n%s\\n<!-- STATIC PAGE END -->\\n\" % body\n\n # now we evaluate for wikilinks\n body = connect_links(config[\"base_url\"],\n data[\"extensions\"].keys(),\n wikidir,\n body)\n\n fe.setData(body)\n\n fe[\"absolute_path\"] = TRIGGER\n fe[\"fn\"] = page_name\n fe[\"file_path\"] = TRIGGER + \"/\" + page_name\n fe[\"template_name\"] = \"wiki\"\n\n data['blog_title_with_path'] = \"%s : %s\" % \\\n (config.get(\"blog_title\", \"\"), fe.get(\"title_escaped\", \"\"))\n\n # set the datadir back\n config[\"datadir\"] = datadir\n\n return [fe]","def test_case_2():\n print(\"*********Test_case_2***********\")\n path = os.path.join(os.path.dirname(__file__), 'testdir')\n result = find_files(None, path)\n print(result)","def findthis(self):\n db = DbWork()\n searched_files = 0\n for root, dir, files in os.walk(self.path):\n for f in files:\n searched_files += 1\n try:\n absolute = os.path.join(root, f)\n if os.path.isfile(absolute):\n size = os.path.getsize(absolute)\n if size > self.size:\n readfile = open(absolute).read(7500)\n sh = hashlib.sha1(readfile).hexdigest()\n db.insert(absolute, size, sh)\n \n except IOError:\n pass\n db.insert_opts(searched_files, self.size)\n db.export()","def main(self, *directories):\n if not self.git and len(directories) == 0:\n print (\"ERROR: At least one directory must be provided (or the \"\n \"--git-precommit flag must be passed.\\n\")\n self.help()\n return\n\n if len(directories) > 0:\n find = local['find']\n files = []\n for directory in directories:\n real = os.path.expanduser(directory)\n if not os.path.exists(real):\n raise ValueError(\"{0} does not exist\".format(directory))\n files.extend(find(real, '-not', '-name', '._*', '-name', '*.py').strip().split('\\n'))\n else:\n status = local['git']('status', '--porcelain', '-uno')\n root = local['git']('rev-parse', '--show-toplevel').strip()\n\n # get all modified or added python files\n modified = re.findall(r\"^\\s[AM]\\s+(\\S+\\.py)$\", status, re.MULTILINE)\n\n # now just get the path part, which all should be relative to the\n # root\n files = [os.path.join(root, line.split(' ', 1)[-1].strip())\n for line in modified]\n\n if len(files) > 0:\n print \"Linting {0} python files.\\n\".format(len(files))\n lint(files)\n else:\n print \"No python files found to lint.\\n\"","def ci_lookup(tree_hash):\t\n\tquery = 'echo ' + tree_hash + ' | ~/lookup/showCnt tree | egrep \"' + '|'.join(ci_files) +'\"'\n\tout = bash(query)\n\t\n\t\"\"\"\n\t# alternate method\n\tblobs = search(tree_hash, 'tree')\n\tindex = {'mode':1, 'hash':1, 'name':2}\n\tci = False\n\tfor blob in blobs:\n\t\tname = blob[index['name']]\n\t\thash = blob[index['hash']]\n\t\tif ((name in ci_files) or\n\t\t (name in ci_config_dir and ';'+ci_config_dir[name] in search(hash, 'tree'))):\n\t\t\tci = True\n\t\t\tbreak\n\t\"\"\"\n\treturn bool(out)","def FileGen(TargetDir, extension = \".gbk\",subset_tag = \"final\"):\n \n for F in next(os.walk(TargetDir))[2]:\n print(F)\n if F[-1*len(extension):] == extension:\n if F.split(\".\")[-2][:len(subset_tag)] == subset_tag:\n yield(TargetDir + '/' + F)","def buildListOfFiles(searchGlob):\n return [fpath for fpath in glob2.iglob(searchGlob) if os.path.isfile(fpath)]","def flake8():\n py_files = subprocess.check_output(\"git ls-files\")\n if PY3:\n py_files = py_files.decode()\n py_files = [x for x in py_files.split() if x.endswith('.py')]\n py_files = ' '.join(py_files)\n sh(\"%s -m flake8 %s\" % (PYTHON, py_files), nolog=True)","def append_subfiles(self, di, lines, lang):\n if lang==\"en\":lang_index=1\n elif lang==\"it\":lang_index=2\n try:\n #if di[0] in self.name_stripped:\n for key in TREE.keys():\n if di[0]==key and di[0] in self.name_stripped:\n deco=[\" |- \" for i in range(len(TREE[key])-1)]\n deco.append(\" `- \")\n for d, fi in zip(deco, TREE[key]):\n bough=\"%s%s\" % (d,fi[lang_index])\n path=\"%s%s/%s/%s\" % (self.backstring, lang, di[0], fi[0])\n lines.append(\"%s %s\" % (path, bough))\n except IndexError:\n pass\n return lines","def scan_root(str_ri, lst_rx, lst_rxe, db):\n print(f\"scan_root(); Scanning: {str_ri, lst_rx, lst_rxe}\")\n # get db info on this dir\n dic_db = dict() # dic by ffn of files known to the db\n str_sql = f\"SELECT * FROM files where filename like '{str_ri}%'\"\n for row in db.execute(str_sql):\n dic_db[row[0]] = row\n # Remove files that no longer exist\n lst_del_this = list()\n for str_ffn_db in dic_db.keys():\n if not os.path.isfile(str_ffn_db):\n lst_del_this.append(str_ffn_db)\n str_ffn_db__sql = str_ffn_db.replace(\"'\", \"''\")\n str_sql = f\"DELETE FROM files WHERE filename='{str_ffn_db__sql}';\"\n db.execute(str_sql)\n db.commit()\n for itm in lst_del_this: # can't change iterable from inside loop\n del dic_db[itm]\n # Walk the root-dir\n num_cntfil = 0\n for root, dirs, files in os.walk(str_ri):\n for str_fn in files:\n # if str_fn.lower().endswith('.jpg'):\n # print(str_fn)\n num_cntfil += 1\n if not any([str_fn.endswith(e) for e in lst_rxe]):\n str_ffn = os.path.join(root, str_fn)\n if not any([str_ffn.startswith(x) for x in lst_rx]): # if the file is not excluded\n if str_ffn in dic_db.keys(): # db knows this file\n obj_bdg = dic_db[str_ffn]\n tim, siz = timeandsize(str_ffn)\n if tim == dic_db[str_ffn][1] and siz == dic_db[str_ffn][2]:\n pass # print(f\" - skipping known file: {str_ffn} == {dic_db[str_ffn]}\") #\n else:\n ## print(f\"WTF: tim? {tim == dic_db[str_ffn][1]} siz? {siz == dic_db[str_ffn][2]} @ ffn: {str_ffn}\")\n # time or date have changed - so re-scanning file, and update DB.\n str_sql = f\"DELETE FROM files WHERE filename='{str_ffn}';\"\n db.execute(str_sql)\n db.commit()\n add_file2db(str_ffn, db)\n else: # db don't know this file - add it.\n add_file2db(str_ffn, db)\n if num_cntfil % 1000000 == 0:\n print(f\"Count: {num_cntfil}: {str_ffn}\")","def search_file(path, f):\n output = []\n for root, dirs, files in os.walk(path, topdown=True):\n for file in files:\n if file == f:\n path = os.path.join(root, file)\n output.append(path)\n\n return output","def get_changed_packages(blob_name1, blob_name2, package_list):\n changed_files = check_output(\n 'git', 'diff', '--name-only', blob_name1, blob_name2)\n changed_files = changed_files.split('\\n')\n\n result = set()\n for filename in changed_files:\n file_root = rootname(filename)\n if file_root in package_list:\n result.add(file_root)\n\n return sorted(result)","def file_changes(self):\n new = []\n changed = []\n deleted = []\n parent = self.parent_tag\n # Loop through the files and find the ones that have changed\n for relative_path, file_dict in self.checksum[\"files\"].items():\n if relative_path not in parent[\"files\"]:\n new.append(relative_path)\n elif file_dict[\"checksum\"] != parent[\"files\"][relative_path][\"checksum\"]:\n changed.append(relative_path)\n # Loop through the parent files and see which files have been deleted\n for relative_path in parent[\"files\"].keys():\n if relative_path not in self.checksum[\"files\"]:\n deleted.append(relative_path)\n return {\"new\": new, \"changed\": changed, \"deleted\": deleted}"],"string":"[\n \"def find_updated_files_in(self, msg, branch, create_issue):\\n self._asset_bind(msg)\\n trans = self._translation_util(msg)\\n updated_files = trans.find_updated_files(REPOSITORY_NAME, branch, TARGET_LANG)\\n if create_issue == 0:\\n yield (\\\"\\\\n\\\".join(limit_result(list(updated_files.keys()))))\\n else:\\n yield (\\\"Processing....\\\")\\n new_count, skip_count = build_update_issue(trans, branch, updated_files)\\n yield (\\\"{} Issues had been created. {} Issues had been skipped.\\\".format(\\n new_count, skip_count))\\n yield (\\\"Please cache issues again.\\\")\",\n \"def find_new_files(self):\\r\\n in_file_system = self._find_data_files(self.basepath,\\r\\n self.data_file_extension, [])\\r\\n status_msg('Search for data files. Found: {0}'.format(\\r\\n len(in_file_system)), True\\r\\n )\\r\\n status_msg('Check that there are no duplicate filenames', True)\\r\\n in_db = self._get_files_in_db()\\r\\n status_msg('Get list of files in data base. Found: {0}'.format(\\r\\n len(in_db)), True\\r\\n )\\r\\n for filename in in_file_system:\\r\\n # Filename has been added to the database both where a part of the\\r\\n # path was a part of the unique name and where it was not,\\r\\n # therefore we need to check for both AND we ignore filenames that\\r\\n # start with IGNORE\\r\\n if form_unique_name(filename) not in in_db and\\\\\\r\\n os.path.basename(filename) not in in_db and not\\\\\\r\\n os.path.basename(filename).startswith('IGNORE'):\\r\\n self.new_data_files.append(filename)\\r\\n\\r\\n print # Adds a new line to the statusses before parsing\\r\\n status_msg('Collecting new files. Found: {0}'.format(\\r\\n len(self.new_data_files)), True\\r\\n )\",\n \"def read_files(folder):\\n print_header(\\\"READING FILES FROM FOLDER (RECURSIVE)\\\", \\\"=\\\")\\n files = []\\n for dirpath, dirnames, filenames in os.walk(folder):\\n if not dirpath.endswith(\\\"updates\\\"):\\n for filename in filenames:\\n root, ext = os.path.splitext(filename)\\n if ext.lower() == \\\".sql\\\":\\n full_path = os.path.join(dirpath, filename)\\n with open(full_path, \\\"r\\\") as f:\\n sql = f.read()\\n sql = sql.decode(\\\"latin-1\\\")\\n\\n files.append((filename, sql))\\n return files\",\n \"def download_history_for_files(self, folder, globstring, dropbox_location, recursive=True):\\n if recursive:\\n files = self._recursive_file_search(folder, globstring)\\n else:\\n files = glob(folder + globstring)\\n\\n #print(files)\\n for f in files:\\n #print(f)\\n dropboxpath = os.path.relpath(f, dropbox_location).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n dropboxpath = '/' + (dropboxpath)\\n self.download_revisions(dropboxpath)\",\n \"def full_find(self, file, version):\\n matches = []\\n for root, dirnames, filenames in os.walk(self.checkout_path(version)):\\n for filename in fnmatch.filter(filenames, file):\\n matches.append(os.path.join(root, filename))\\n return matches\",\n \"def git_callback(files): # type: (t.List[t.Tuple[str, str]]) -> None\\n for dirpath, _dirnames, filenames in os.walk(os.path.join(data_context().content.root, '.git')):\\n paths = [os.path.join(dirpath, filename) for filename in filenames]\\n files.extend((path, os.path.relpath(path, data_context().content.root)) for path in paths)\",\n \"def createFileList(my_dir, term):\\n \\n for subdir, dirs, files in os.walk(my_dir):\\n for file in files:\\n if re.search(re.escape(term), file):\\n result = os.path.join(subdir, file)\\n fileList.append(result)\\n return fileList\",\n \"def find_langs(args):\\n infile = args.source\\n langs = {}\\n for line in infile:\\n name_and_files = line.split()\\n name = name_and_files[0]\\n if name not in langs:\\n langs[name] = []\\n langs[name] += read_files.filter_files(name_and_files[1:])\\n langs[args.unknown] += read_files.filter_files(args.classify)\\n return langs\",\n \"def find_files():\\n \\n p = re.compile(REGEX_PART_NUMBER)\\n job_files = []\\n \\n for root, dirs, files in os.walk(project_path): # r at start of string need to prevent unicode error\\n for filename in files:\\n re_part_number = p.match(filename)\\n if re_part_number:\\n file_ext = filename.split(\\\".\\\")[-1].lower() # extract file extension \\n file_size = os.path.getsize((os.path.join(root, filename))) # filesize in bytes \\n \\n part_number = re_part_number.group() # extract part number from regular expression match\\n part_code = part_number.split(\\\"-\\\")[0]\\n \\n destinations = [] # destinations is a list in case a filetype is both a source and output filetype\\n \\n if (file_ext in EXTS_SOURCE_FILES) and flag_find_source_files:\\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \\n \\n if (file_ext in EXTS_OUTPUT_FILES) and flag_find_output_files:\\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \\n \\n if destinations: \\n job_files.append(File(filename,root,file_size,destinations,part_number,part_code))\\n print(f\\\"Found: {filename}\\\")\\n \\n return job_files\",\n \"def filesearch(word=\\\"\\\"):\\n logger.info('Starting filesearch')\\n file = []\\n for f in glob.glob(\\\"*\\\"):\\n if word[0] == \\\".\\\":\\n if f.endswith(word):\\n file.append(f)\\n\\n elif word in f:\\n file.append(f)\\n #return file\\n logger.debug(file)\\n return file\",\n \"def tracked_files(self, branch):\\n d = {}\\n ls_tree = self.git_cmd('ls-tree -r --name-only --full-tree %s' %\\n branch)\\n for rpath in ls_tree.splitlines():\\n if rpath == '.gitignore':\\n continue\\n if branch.startswith('timestamps'):\\n d[rpath] = pathlib.PosixPath(self.repodir, rpath)\\n else:\\n if not rpath.startswith(ROOT_SUBDIR):\\n continue\\n d[rpath] = EtcPath(self.repodir, rpath)\\n return d\",\n \"def test_case_6():\\n print(\\\"*********Test_case_6***********\\\")\\n path = os.path.join(os.path.dirname(__file__), 'testdir', 't1.c')\\n result = find_files('.c', path)\\n print(result)\",\n \"def find_old_files(directory):\\n # go to the directory\\n os.chdir(directory)\\n \\n # construct the find command to interogate the directory\\n find_cmd = [\\\"find\\\"]\\n find_cmd.append(\\\"*\\\")\\n find_cmd.append(\\\"-atime\\\")\\n find_cmd.append(\\\"+\\\"+str(DAYS_OLD))\\n \\n # run the find command\\n try:\\n find = subprocess.Popen(\\\" \\\".join(find_cmd),shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\\n except:\\n raise\\n # get the standard output\\n out, err = find.communicate() # get the standard output\\n found = out.decode().split() # split the text into a list\\n \\n # go back to our starting directory \\n os.chdir(iwd)\\n \\n return(found)\",\n \"def find_file(self):\\n selected_file = tk.filedialog.askopenfilename(initialdir='/', title='Select File',\\n filetypes=(('txt Files', '*.txt'), ('All Files', '*.*')))\\n self.markov_chain.add_file(selected_file)\",\n \"def test_case_5():\\n print(\\\"*********Test_case_5***********\\\")\\n result = find_files('.c', \\\"\\\")\\n print(result)\",\n \"def initialise_languages():\\n is_language_folder = r\\\"^[^\\\\\\\\\\\\.]*\\\" # Cannot have backslash or dot.\\n language_folder_path = os.path.join(definitions.ROOT_DIR, \\\"languages\\\")\\n\\n for root, dirs, files in os.walk(language_folder_path):\\n for name in files:\\n if name.startswith(\\\"_NEW_\\\") or name.startswith(\\\"_CHANGED_\\\"):\\n # Files that are auto-generated will not be added.\\n continue\\n\\n full_dir = os.path.join(root, name)\\n relative_dir = full_dir.replace(language_folder_path + \\\"\\\\\\\\\\\", \\\"\\\")\\n\\n match = re.match(is_language_folder, relative_dir)\\n language_id = match.group(0)\\n\\n language = None\\n if language_id != \\\"cache\\\":\\n language = definitions.LANGUAGES[language_id]\\n else:\\n language = definitions.CACHE_LANGUAGE\\n\\n if relative_dir == \\\"{id}\\\\\\\\commands.json\\\".format(id=language_id):\\n # Take the commands.\\n definitions.COMMANDS.add_command_localisation(full_dir, language_id)\\n\\n elif relative_dir == \\\"{id}\\\\\\\\languages.json\\\".format(id=language_id):\\n # Take the languages.\\n language.add_languages(full_dir)\\n\\n elif relative_dir == \\\"{id}\\\\\\\\meta.json\\\".format(id=language_id):\\n # Take the metadata.\\n language.add_meta(full_dir)\\n\\n elif relative_dir == \\\"{id}\\\\\\\\permissions.json\\\".format(id=language_id):\\n # Take the permissions.\\n language.add_permission_names(full_dir)\\n\\n elif relative_dir == \\\"{id}\\\\\\\\units.json\\\".format(id=language_id):\\n language.add_units(full_dir)\\n\\n else:\\n # Take the keys\\n language.add_keys_from_path(full_dir)\\n\\n for name in dirs:\\n full_dir = os.path.join(root, name)\\n relative_dir = full_dir.replace(language_folder_path + \\\"\\\\\\\\\\\", \\\"\\\")\\n\\n match = re.fullmatch(is_language_folder, relative_dir)\\n if match is not None:\\n if name != \\\"cache\\\":\\n definitions.LANGUAGES[name] = Language(name)\\n else:\\n definitions.CACHE_LANGUAGE = Language(name)\",\n \"def checkGit(directory):\",\n \"def find_deleted_files_in(self, msg, branch, create_issue):\\n self._asset_bind(msg)\\n trans = self._translation_util(msg)\\n deleted_file_list = trans.find_deleted_files(\\n REPOSITORY_NAME, branch, TARGET_LANG)\\n\\n moved_files = trans.find_moved_files_in_deleted_files(REPOSITORY_NAME, branch, TARGET_LANG, deleted_file_list)\\n\\n deleted_files = list(sorted(set(deleted_file_list) - set(moved_files.values())))\\n\\n if create_issue == 0:\\n yield (\\\"move: \\\\n\\\" + \\\"\\\\n\\\".join(limit_result(list(sorted(moved_files.values())))))\\n yield (\\\"delete: \\\\n\\\" + \\\"\\\\n\\\".join(limit_result(deleted_file_list)))\\n else:\\n yield (\\\"Processing....\\\")\\n client = self._github_operator(msg)\\n new_count, skip_count = build_move_issue(trans, client, branch, moved_files)\\n yield (\\\"move: {} Issues had been created. {} Issues had been skipped.\\\".format(\\n new_count, skip_count))\\n new_count, skip_count = build_delete_issue(trans, client, branch, deleted_file_list)\\n yield (\\\"delete: {} Issues had been created. {} Issues had been skipped.\\\".format(\\n new_count, skip_count))\\n yield (\\\"Please cache issues again.\\\")\",\n \"def test_case_3():\\n print(\\\"*********Test_case_3***********\\\")\\n result = find_files('.c', None)\\n print(result)\",\n \"def _findfile(self,path,label):\\n files=[];filenames=os.listdir(path)\\n for name in filenames:\\n if os.path.splitext(name)[0]==str(label):\\n files.append(name)\\n return files\",\n \"def get_files_changed():\\n files_list = []\\n test = os.popen('git show --name-only')\\n repo_location = os.popen('git rev-parse --show-toplevel')\\n repo_location = repo_location.readlines()\\n repo_location = repo_location[0]\\n repo_location = repo_location.replace('\\\\n', '')\\n if \\\"Not a git repository\\\" in repo_location:\\n files_list.append(\\\"Not a git repository\\\")\\n return files_list\\n files_list.append(repo_location.split('/')[-1])\\n output = test.readlines()\\n for a in range(6, len(output)):\\n files_list.append(output[a].replace('\\\\n', ''))\\n return files_list\",\n \"def get_changed_files(self, old_commit, new_commit):\\n if old_commit is not None and not self.pygit.descendant_of(\\n new_commit, old_commit\\n ):\\n raise ValueError(\\\"Second commit must be a descendant of first commit\\\")\\n\\n old_index = pygit2.Index()\\n new_index = pygit2.Index()\\n if old_commit is not None:\\n old_tree = self.pygit.get(old_commit).tree\\n old_index.read_tree(old_tree)\\n else:\\n # This is a special hash that represents an empty tree\\n old_tree = self.pygit.get(\\\"4b825dc642cb6eb9a060e54bf8d69288fbee4904\\\")\\n\\n new_tree = self.pygit.get(new_commit).tree\\n new_index.read_tree(new_tree)\\n\\n for patch in self.pygit.diff(old_tree, new_tree):\\n if patch.delta.status_char() != \\\"M\\\":\\n continue\\n\\n if not patch.delta.new_file.path.startswith(\\\"locales/\\\"):\\n continue\\n\\n old_file_oid = old_index[patch.delta.old_file.path].oid\\n new_file_oid = new_index[patch.delta.new_file.path].oid\\n old_file = self.pygit.get(old_file_oid)\\n new_file = self.pygit.get(new_file_oid)\\n yield patch.delta.new_file.path, old_file.data, new_file.data\",\n \"def scanForNewContent (self):\\n\\n self._logGeneral('scan-new-content-starting').debug2('starting to scan for new content')\\n \\n totalMetaFilesScanned = 0\\n totalNewFilesAdded = 0\\n\\n metaFilesPattern = None\\n if self._isPrediction is False:\\n metaFilesPattern = self._metaFilesPattern\\n else:\\n metaFilesPattern = self._metaPredictionFilesPattern\\n\\n mminParam = self._cfg.modificationCheckThresholdMinutes\\n if mminParam != None and mminParam == 0:\\n mminParam = 1\\n\\n findCommand = \\\"find -L . -type f -name '%(filePattern)s'\\\" % {'filePattern':metaFilesPattern}\\n if not self._isFirstScan and (mminParam != None):\\n findCommand = \\\"find -L . -mmin -%(minutes)s -type f -name '%(filePattern)s'\\\" % {'minutes':str(mminParam), 'filePattern':metaFilesPattern}\\n findCommand += \\\" | xargs --no-run-if-empty stat -c '%n %Y'\\\"\\n\\n self._logGeneral('new-content-find-command').debug2('going to search for new files. command: %s', findCommand)\\n\\n # We have set of meta file directories. go over each directory.\\n for disk in self._disksSet:\\n self._logGeneral('scan-new-files-starting-in-disk').debug3('starting to scan for new content in disk %02u', disk)\\n\\n metaRamRootDir = os.path.join(self._cfg.metaRamBaseDir, \\\"%02u\\\" % disk)\\n metaPersistentRootDir = os.path.join(self._cfg.metaPersistentBaseDir, \\\"%02u\\\" % disk)\\n data = None\\n try:\\n process = subprocess.Popen(findCommand, shell=True, stdout=subprocess.PIPE, cwd=metaRamRootDir, bufsize=65536)\\n data = process.communicate()[0]\\n except Exception as ex:\\n self._logGeneral(\\\"cant-run-find\\\").error(\\\"error trying to run 'find' command. exception: %s\\\", ex)\\n continue\\n \\n if data == None:\\n self._logGeneral(\\\"no-output-data\\\").error(\\\"data from 'find' command is None.\\\")\\n continue\\n\\n for line in data.split('\\\\n'):\\n if len(line)==0:\\n break \\n\\n # Split the output line to the relative path and the modification time\\n # Relative path is: aa/bb/aabbsdaa12231.meta / aa/bb/aabbsdaa12231.meta.prd\\n splittedLine = line.split(\\\" \\\")\\n metaFileRelativePath = splittedLine[0]\\n modificationTime = int(splittedLine[1])\\n\\n # Get the full meta path\\n metaFileFullNamePath = os.path.join(metaRamRootDir, metaFileRelativePath)\\n\\n # Get only the meta file name: aabbsdaa12231.meta / aabbsdaa12231.meta.prd\\n metaFileName = os.path.split(metaFileRelativePath)[1]\\n # Get the cid\\n cid = metaFileName.split('.')[0]\\n\\n # Get the meta persistent full name path\\n metaPersistentFileFullNamePath = os.path.join(metaPersistentRootDir, metaFileRelativePath)\\n \\n # We check if self._keepRunning is up before we scan the next folder\\n if not self._keepRunning:\\n return totalMetaFilesScanned, totalNewFilesAdded\\n\\n # Count total files scanned\\n self.counters['numTotalMetaFilesScanned'] += 1\\n totalMetaFilesScanned += 1\\n\\n if (totalMetaFilesScanned % DO_PERIODIC_WORK_INTERVAL_RECOREDS) == 0:\\n self._periodicWorkCallBack()\\n\\n updateLastAccess = False\\n updateLastModificationTime = False\\n isCounted = True\\n isExist = False\\n\\n # If in dict, check if updates are needed\\n if cid in self._cidLastAccessTimeDict:\\n isExist = True\\n # We check if we need to update modification time and bytes acquired.\\n if KEY_LAST_MODIFICATION_TIME in self._cidLastAccessTimeDict[cid]:\\n cidLastModificationTime = self._cidLastAccessTimeDict[cid][KEY_LAST_MODIFICATION_TIME]\\n if modificationTime > cidLastModificationTime:\\n updateLastModificationTime = True\\n else:\\n updateLastModificationTime = True\\n\\n if KEY_WAS_COUNTED not in self._cidLastAccessTimeDict[cid]:\\n isCounted = False\\n self._cidLastAccessTimeDict[cid][KEY_WAS_COUNTED] = 1\\n\\n cidCurrentLastAccessTime = self._cidLastAccessTimeDict[cid][KEY_LAST_ACCESS_TIME]\\n if cidCurrentLastAccessTime < modificationTime:\\n self._logGeneral('in-dict-update').debug2('cid=%s already exists in dict. updating its lastAccessTime from %d to %d', cid, cidCurrentLastAccessTime, modificationTime)\\n updateLastModificationTime = True\\n else:\\n self._logGeneral('in-dict-no-update').debug2('cid=%s already exists in dict. no need to update lastAccessTime', cid)\\n\\n\\n # If not exists or we need to update the modification time - we need to look for the bytes acquired inside the meta.\\n bytesDiskSize = 0\\n cgid = -1\\n \\n if (not isExist) or updateLastModificationTime or not isCounted or self._isFirstScan:\\n (cgid, bytesDiskSize) = self._getCgidAndBytesUsed(cid, metaFileFullNamePath) \\n\\n # Update or insert to data base\\n updateBytes = False\\n bytesToAdd = 0;\\n updateNumTitle = False\\n if isExist:\\n if not isCounted:\\n updateNumTitle = True\\n self._cidLastAccessTimeDict[cid][KEY_CGID]=cgid\\n if updateLastModificationTime:\\n updateBytes = True\\n if KEY_LAST_BYTES_ON_DISK in self._cidLastAccessTimeDict[cid]:\\n bytesToAdd = bytesDiskSize - self._cidLastAccessTimeDict[cid][KEY_LAST_BYTES_ON_DISK]\\n else:\\n bytesToAdd = bytesDiskSize\\n self._cidLastAccessTimeDict[cid][KEY_LAST_BYTES_ON_DISK] = bytesDiskSize\\n self._cidLastAccessTimeDict[cid][KEY_LAST_MODIFICATION_TIME] = modificationTime\\n\\n if updateLastAccess:\\n self._cidLastAccessTimeDict[cid][KEY_LAST_ACCESS_TIME] = modificationTime\\n else:\\n bytesToAdd = bytesDiskSize\\n updateBytes = True\\n updateNumTitle = True\\n self._cidLastAccessTimeDict[cid] = {KEY_LAST_ACCESS_TIME:modificationTime, KEY_META_FILE_FULL_NAME_PATH:metaFileFullNamePath,\\\\\\n KEY_META_PERSISTENT_FILE_FULL_NAME_PATH:metaPersistentFileFullNamePath, KEY_DISK_NUM:disk, \\\\\\n KEY_LAST_MODIFICATION_TIME:modificationTime, KEY_LAST_BYTES_ON_DISK: bytesDiskSize, KEY_WAS_COUNTED:1, \\\\\\n KEY_CGID:cgid}\\n self._logGeneral('new-content').debug2('new content has been found. cid=%s, metaFileFullNamePath=\\\"%s\\\"', cid, metaFileFullNamePath)\\n self.counters['numTotalNewFilesAdded'] += 1\\n totalNewFilesAdded += 1\\n \\n if self._isFirstScan:\\n updateBytes = True\\n bytesToAdd = bytesDiskSize;\\n updateNumTitle = True\\n\\n # Update counters\\n self._incrCounters(updateBytes, updateNumTitle, cgid, bytesToAdd)\\n\\n \\n if self._isFirstScan:\\n if len(self._cidLastAccessTimeDict) > totalMetaFilesScanned:\\n self._logGeneral('meta-count-too-large').notice('number meta on disk is less then on dict. going to remove junk entries. totalMetaFilesScanned=%d, len(Dict)=%d', totalMetaFilesScanned, len(self._cidLastAccessTimeDict))\\n self._scanLastAccessDictForJunk()\\n self._isFirstScan = False\\n\\n return totalMetaFilesScanned, totalNewFilesAdded\",\n \"def find_reference_files():\\n for root, _, files in os.walk(\\\"./tests/references/\\\"):\\n for basename in fnmatch.filter(files, \\\"*.tex\\\"):\\n yield os.path.join(root, basename)\",\n \"def find(self, file, version):\\n matches = []\\n for root, dirnames, filenames in os.walk(self.full_doc_path(version)):\\n for filename in fnmatch.filter(filenames, file):\\n matches.append(os.path.join(root, filename))\\n return matches\",\n \"def _get_files(repo, patterns, options):\\n ctx = repo[None]\\n match = match_func(repo, ctx, patterns, options)\\n try:\\n status = ctx.status(listclean=True, listignored=True, listunknown=True)\\n except TypeError:\\n # Compatibility with older Mercurial versions.\\n status = ctx.status(clean=True, ignored=True, unknown=True)\\n modified = status[0]\\n added = status[1]\\n unknown = status[4]\\n ignored = status[5]\\n clean = status[6]\\n files = []\\n for file_list in [clean, modified, added]:\\n for filename in file_list:\\n if match(filename):\\n files.append(filename)\\n for file_list in [ignored, unknown]:\\n for filename in file_list:\\n if match.exact(filename):\\n files.append(filename)\\n return files\",\n \"def find_files(directory, extension, magictext):\\n global files_logged\\n global found_text\\n dir_path = os.path.abspath(directory)\\n dir_files = os.listdir(dir_path)\\n for file in dir_files:\\n if file.endswith(extension) and file not in files_logged:\\n logger.info('New file found: {}'.format(file))\\n files_logged.append(file)\\n if file.endswith(extension):\\n file_path = os.path.join(dir_path, file)\\n if find_string_in_files(file_path, magictext):\\n break\\n for file in files_logged:\\n if file not in dir_files:\\n logger.info('File deleted: {}'.format(file))\\n files_logged.remove(file)\\n found_text[file] = 0\",\n \"def getFiles(folderToProcess,filter):\\n\\n print(f\\\"Parsing {folderToProcess} for {filter} files\\\")\\n\\n if debug:\\n for path in Path(folderToProcess).rglob(filter):\\n print(f\\\"Found {path}\\\")\\n\\n all_files = [str(x) for x in Path(folderToProcess).rglob(filter)] \\n\\n return all_files\",\n \"def calc_lang_features(commits, author):\\n\\tlang_features = ['/\\\\*\\\\*', '\\\\\\\\\\\"\\\\\\\\\\\"\\\\\\\\\\\"', '///', # documentation\\n\\t\\t\\t'^\\\\s*@', 'def.+:.+->', 'using\\\\s+System\\\\.ComponentModel\\\\.DataAnnotations', # assertion\\n\\t\\t\\t'assert', 'TODO', 'lambda']\\n\\n\\t# delete contents\\n\\topen('lang_features.csv', 'w').close()\\n\\t\\n\\tfor count, commit in enumerate(commits):\\n\\t\\t# status update\\n\\t\\tif (count + 1) % 5 == 0:\\n\\t\\t\\tprint commit, '.. ..', count + 1, ' / ', len(commits)\\n\\n\\n\\t\\t\\t# for each blob modified\\n\\t\\tquery = (\\\"for x in $(echo \\\" + commit + \\\" | ssh da4 ~/lookup/cmputeDiff2.perl); do \\\" +\\n\\t\\t\\t\\t# get the chold and parent blob\\n\\t\\t\\t\\t\\\"diff_blobs=$(echo $x | awk -v RS=';' 1 | sed -n '3,4 p');\\\" +\\n\\t\\t\\t\\t# if a parent blob does not exist, the author authored all of the content of the file\\n\\t\\t\\t\\t\\\"if [ $(echo $diff_blobs|wc -w) -eq 1 ]; then \\\" +\\n\\t\\t\\t\\t\\t\\\"echo $diff_blobs | ~/lookup/showCnt blob 2> /dev/null; \\\" +\\n\\t\\t\\t\\t# if a parent blob exists, find the diff, in order to search only the modified lines\\n\\t\\t\\t\\t\\\"elif [ $(echo $diff_blobs|wc -w) -eq 2 ]; then \\\" +\\n\\t\\t\\t\\t\\t\\\"vars=( $diff_blobs );\\\" +\\n\\t\\t\\t\\t\\t# using bash instead of sh in order to use the process substitution,\\n\\t\\t\\t\\t\\t# to get the modified lines\\n\\t\\t\\t\\t\\t\\\"/bin/bash -c \\\\\\\"diff <(echo ${vars[0]} | ~/lookup/showCnt blob)\\\" +\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\" <(echo ${vars[1]} | ~/lookup/showCnt blob)\\\\\\\";\\\" +\\n\\t\\t\\t\\t\\\"fi;\\\" +\\n\\t\\t\\t# grep the above practices and discard the lines that were deleted from the parent blob\\n\\t\\t\\t# (they start with \\\">\\\" in diff)\\n\\t\\t\\t\\\"done | egrep \\\\\\\"\\\" + \\\"|\\\".join(lang_features) + \\\"\\\\\\\" | grep -v '^>' | wc -l \\\")\\n\\t\\tcount_uses = int(bash(query).strip())\\n\\t\\tif count_uses > 0: # good practice feature is used\\n\\t\\t\\tout = bash('echo ' + commit + ' | ~/lookup/getValues c2P')\\n\\t\\t\\tmain_proj = out.strip().split(';')[1]\\n\\t\\t\\ttime = search(commit, 'commit')[2]\\n\\n\\t\\t\\tf = open(\\\"lang_features.csv\\\", \\\"a\\\")\\n\\t\\t\\tprint 'lang_f'\\n\\t\\t\\tf.write(author + ', ' + 'LANG_F' + ', ' + str(time) + ', ' + main_proj + ', ' + str(count_uses) + '\\\\n')\\n\\t\\t\\tf.close()\\n\\t\\t\\tprint 'wrote: -->', commit\",\n \"def find(pattern):\\n files = config.index.files(path_glob=\\\"*%s*\\\" % pattern)\\n print_files(files)\",\n \"def find_files(\\n base,\\n languages=all_languages,\\n dir_ignore=default_dir_ignore,\\n file_ignore=default_file_ignore,\\n):\\n if base[-1] != \\\"/\\\":\\n base += \\\"/\\\"\\n\\n def update_dirs(dirs):\\n \\\"\\\"\\\"strip the ignored directories out of the provided list\\\"\\\"\\\"\\n index = len(dirs) - 1\\n for i, d in enumerate(reversed(dirs)):\\n if d in dir_ignore:\\n del dirs[index - i]\\n\\n # walk over base\\n for root, dirs, files in os.walk(base):\\n root = root.replace(base, \\\"\\\", 1)\\n\\n # strip ignored directories from the list\\n update_dirs(dirs)\\n\\n for filename in files:\\n if filename in file_ignore:\\n # skip ignored files\\n continue\\n\\n # try to figure out the language of the specified file\\n fullpath = os.path.join(base, root, filename)\\n language = lang_type(fullpath)\\n\\n # if the file is one of the langauges that we want return\\n # its name and the language\\n if language in languages:\\n yield fullpath, language\",\n \"def _select_search_file(self, change):\\n self.file = change.get(\\\"new\\\")\",\n \"def check_file_references(name):\\n references = []\\n for root, dirs, files in walk(Path(\\\".\\\")):\\n for file_name in files:\\n if is_notebook(file_name):\\n nb_markdown_cells = markdown_cells(os.path.join(root, file_name))\\n for cell in nb_markdown_cells:\\n for line in cell:\\n if name in line:\\n references.append(file_name)\\n else:\\n with open(os.path.join(root, file_name), encoding=\\\"utf8\\\", errors='ignore') as non_nb_file:\\n if name in non_nb_file.read():\\n references.append(file_name)\\n return references\",\n \"def find(query):\\n acc = []\\n for root, dirs, files in os.walk(query, topdown=False):\\n for name in files:\\n acc += [os.path.join(root, name)]\\n return acc\",\n \"def test_case_1():\\n print(\\\"*********Test_case_1***********\\\")\\n path = os.path.join(os.path.dirname(__file__), 'testdir')\\n result = find_files('.c', path)\\n for file in result:\\n print(file)\",\n \"def files_in_folder(self):\\n non_til = set()\\n filesInFolder = []\\n for f in self.find_all_files():\\n newstr = f.replace(\\\"~\\\", \\\"\\\") \\n if newstr in self.find_all_files():\\n non_til.add(newstr)\\n for fs in non_til:\\n filesInFolder.append(fs)\\n return filesInFolder\",\n \"def FindCheckerFiles(path):\\n if not path:\\n Logger.fail(\\\"No source path provided\\\")\\n elif os.path.isfile(path):\\n return [ path ]\\n elif os.path.isdir(path):\\n foundFiles = []\\n for root, dirs, files in os.walk(path):\\n for file in files:\\n extension = os.path.splitext(file)[1]\\n if extension in [\\\".java\\\", \\\".smali\\\"]:\\n foundFiles.append(os.path.join(root, file))\\n return foundFiles\\n else:\\n Logger.fail(\\\"Source path \\\\\\\"\\\" + path + \\\"\\\\\\\" not found\\\")\",\n \"def _search(self, log, progressbar):\\n self._urls = []\\n for filename in os.listdir(self._path):\\n url = 'file:////' + filename\\n self._urls.append(url)\\n self._urls.sort()\",\n \"def detect_changed_files(self) -> list[Path]:\\n repos = [(self.open_repo(), self.git_directory)]\\n # Check server and api dirs too\\n # Normally these are ignored but we need to check these\\n if (server_repo_path := Path(self.git_directory, \\\"Paper-Server\\\")).exists():\\n repos.append((pygit2.Repository(str(server_repo_path)), server_repo_path))\\n if (api_repo_path := Path(self.git_directory, \\\"Paper-API\\\")).exists():\\n repos.append((pygit2.Repository(str(api_repo_path)), api_repo_path))\\n changed = []\\n for repo, repo_path in repos:\\n changed.extend(p.relative_to(self.git_directory) for p in detect_changed_files(repo, repo_path))\\n changed.sort()\\n return changed\",\n \"def search_translation(file_name,line_original_language,line_new_language,to_translate):\\n csv_file = open(file_name, 'rb')\\n rd = csv.reader(csv_file, delimiter=';',quoting=csv.QUOTE_ALL)\\n nb_row = 0\\n for row in rd:\\n nb_row += 1\\n if line_original_language >= 0 and line_new_language >= 0:\\n if row[line_original_language] == to_translate:\\n to_translate = row[line_new_language]\\n break\\n return to_translate\",\n \"def search_for_files(\\n run_name: str,\\n raw_extension: Optional[str] = None,\\n cellpy_file_extension: Optional[str] = None,\\n raw_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n project_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n cellpy_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n prm_filename: Union[pathlib.Path, str, None] = None,\\n file_name_format: Optional[str] = None,\\n reg_exp: Optional[str] = None,\\n sub_folders: Optional[bool] = True,\\n file_list: Optional[List[str]] = None,\\n with_prefix: Optional[bool] = True,\\n pre_path: Union[OtherPath, pathlib.Path, str, None] = None,\\n) -> Tuple[List[str], str]:\\n\\n # TODO: @jepe - use reg_exp\\n # TODO: @jepe - allow for searching in cloud\\n # TODO: @jepe - how to implement searching in db?\\n # TODO: update prms and conf file to allow for setting if search should be done in\\n # sub-folders, several folders, db, cloud etc\\n # TODO: @jepe - find a way to implement automatic file_list creation in a top level func.\\n logging.debug(f\\\"searching for {run_name}\\\")\\n version = 0.4\\n t0 = time.time()\\n\\n if reg_exp is None:\\n reg_exp = prms.FileNames.reg_exp\\n\\n if raw_extension is None:\\n raw_extension = prms.FileNames.raw_extension\\n\\n if raw_extension is None:\\n raw_extension = prms.FileNames.raw_extension\\n\\n if cellpy_file_extension is None:\\n cellpy_file_extension = prms.FileNames.cellpy_file_extension\\n\\n # backward compatibility check:\\n if cellpy_file_extension.startswith(\\\".\\\"):\\n warnings.warn(\\n \\\"Deprecation warning: cellpy_file_extension should not include the '.'\\\"\\n )\\n cellpy_file_extension = cellpy_file_extension[1:]\\n\\n if raw_file_dir is None:\\n raw_file_dir = prms.Paths.rawdatadir\\n\\n if file_name_format is None:\\n file_name_format = prms.FileNames.file_name_format\\n\\n # 'dressing' the raw_file_dir in a list in case we want to\\n # search in several folders (not implemented yet):\\n if not isinstance(raw_file_dir, (list, tuple)):\\n raw_file_dir = [OtherPath(raw_file_dir)]\\n else:\\n raw_file_dir = [OtherPath(d) for d in raw_file_dir]\\n\\n if project_dir is not None:\\n raw_file_dir = [r / project_dir for r in raw_file_dir]\\n\\n if reg_exp:\\n logging.warning(f\\\"Got reg_exp: {reg_exp}\\\")\\n logging.warning(\\\"Sorry, but using reg exp is not implemented yet.\\\")\\n\\n if prm_filename is not None:\\n logging.debug(\\\"Sorry, reading prm file is not implemented yet.\\\")\\n\\n if cellpy_file_dir is None:\\n cellpy_file_dir = OtherPath(prms.Paths.cellpydatadir)\\n else:\\n cellpy_file_dir = OtherPath(cellpy_file_dir)\\n\\n if file_name_format is None and reg_exp is None:\\n try:\\n # To be implemented in version 0.5:\\n file_name_format = prms.FileNames.file_name_format\\n except AttributeError:\\n file_name_format = \\\"YYYYMMDD_[name]EEE_CC_TT_RR\\\"\\n\\n if file_name_format.upper() == \\\"YYYYMMDD_[NAME]EEE_CC_TT_RR\\\":\\n # backward compatibility check\\n if raw_extension.startswith(\\\".\\\"):\\n warnings.warn(\\n \\\"Deprecation warning: raw_extension should not include the '.'\\\"\\n )\\n raw_extension = raw_extension[1:]\\n warnings.warn(f\\\"Removing it -> {raw_extension}\\\")\\n # TODO: give warning/error-message if run_name contains more than one file (due to duplicate in db)\\n glob_text_raw = f\\\"{run_name}*.{raw_extension}\\\"\\n else:\\n glob_text_raw = file_name_format\\n\\n logging.debug(f\\\"searching for raw files in: {raw_file_dir}\\\")\\n logging.debug(f\\\"searching for {run_name}\\\")\\n logging.debug(f\\\"using glob {glob_text_raw}\\\")\\n\\n cellpy_file = f\\\"{run_name}.{cellpy_file_extension}\\\"\\n cellpy_file = cellpy_file_dir / cellpy_file\\n # Not sure if for example pandas can handle OtherPath objects:\\n cellpy_file = cellpy_file.full_path\\n\\n logging.debug(f\\\"generated cellpy filename {cellpy_file}\\\")\\n\\n if file_list is not None:\\n if len(raw_file_dir) > 1:\\n logging.info(\\\"you provided several raw file directories\\\")\\n logging.debug(\\\"searching within provided list of files\\\")\\n\\n if with_prefix:\\n glob_text_raw_for_list = f\\\"*{glob_text_raw}\\\"\\n else:\\n glob_text_raw_for_list = glob_text_raw\\n\\n run_files = fnmatch.filter(file_list, glob_text_raw_for_list)\\n\\n if pre_path is not None:\\n pre_path = OtherPath(pre_path)\\n run_files = list(map(lambda x: pre_path / x, run_files))\\n\\n else:\\n run_files = []\\n for d in raw_file_dir:\\n if d.is_external:\\n logging.debug(\\\"external file\\\")\\n if not d.is_dir():\\n warnings.warn(\\\"your raw file directory cannot be accessed!\\\")\\n # raise cellpy.exceptions.IOError(\\\"your raw file directory cannot be accessed!\\\")\\n _run_files = []\\n else:\\n logging.debug(f\\\"checking in folder {d}\\\")\\n logging.debug(f\\\"{sub_folders=}\\\")\\n if sub_folders:\\n _run_files = d.rglob(glob_text_raw)\\n\\n else:\\n _run_files = d.glob(glob_text_raw)\\n\\n _run_files = [str(_f.resolve()) for _f in _run_files]\\n _run_files.sort()\\n run_files.extend(_run_files)\\n\\n return run_files, cellpy_file\",\n \"def find_enc(self,en_code):\\n encode = \\\"(.\\\"+en_code+\\\")\\\"\\n file_of_type = []\\n for file_en in self.files_in_folder():\\n found = re.search(encode, file_en)\\n if found:\\n file_of_type.append(file_en)\\n return file_of_type\",\n \"def _get_changed_files():\\n if not ci_diff_helper:\\n return None\\n\\n try:\\n config = ci_diff_helper.get_config()\\n except OSError: # Not on CI.\\n return None\\n\\n changed_files = ci_diff_helper.get_changed_files('HEAD', config.base)\\n\\n changed_files = set([\\n './{}'.format(filename) for filename in changed_files])\\n\\n return changed_files\",\n \"def __searchFiles(self):\\n self.ui.showFindFilesDialog(self.textForFind())\",\n \"def luke_filewalker(Malware_bin):\\n file_analyses=[]\\n for (dirpath, dirname, filenames) in os.walk(Malware_bin):\\n for f in filenames:\\n file_analyses.append(os.path.join(Malware_bin,f))\\n #print \\\"Added : \\\"+'\\\\033[1m'+f+'\\\\033[0m'\\n return file_analyses\",\n \"def filter_changed_files(changed_files, path_to_repository, file_encoding):\\n filtered_files = []\\n for changed_file in changed_files:\\n file_is_valid = True\\n if (os.path.isdir(os.path.join(path_to_repository, changed_file))):\\n #ignore directories (e.g., git submodule folders)\\n continue\\n if os.path.getsize(os.path.join(path_to_repository, changed_file)) > 1 * 1024 * 1024:\\n print('File too large for precommit analysis. Ignoring: %s' % changed_file)\\n file_is_valid = False\\n\\n try:\\n with open(os.path.join(path_to_repository, changed_file), encoding=file_encoding) as file:\\n file.read()\\n except UnicodeDecodeError:\\n encoding_string = file_encoding\\n if encoding_string is None:\\n encoding_string = locale.getpreferredencoding() + ' (system encoding)'\\n\\n print(\\n 'File at %s is not encoded in %s. Try using the --file-encoding option.' % (\\n changed_file, encoding_string))\\n file_is_valid = False\\n\\n if file_is_valid:\\n filtered_files.append(changed_file)\\n\\n return filtered_files\",\n \"def get_changed_files_and_content(path_to_repository, file_encoding, ignore_subrepositories):\\n changed_files = filter_changed_files(get_changed_files(path_to_repository, ignore_subrepositories),\\n path_to_repository, file_encoding)\\n return {filename: open(os.path.join(path_to_repository, filename), encoding=file_encoding).read() for filename in\\n changed_files}\",\n \"def _findFiles(self, inputfolder):\\n protofile, caffemodel = None, None\\n files = os.listdir(inputfolder)\\n for f in files:\\n name, ext = splitext(f)\\n if ext == '.caffemodel':\\n caffemodel = join(inputfolder, f)\\n elif f == 'deploy.prototxt':\\n protofile = join(inputfolder, f)\\n return protofile, caffemodel\",\n \"def _find_changes(self):\\n added = set()\\n modified = set()\\n existing_files = set()\\n for dirpath_str, _, filenames in walk(str(self.path)):\\n dirpath = Path(dirpath_str)\\n for filename in filenames:\\n if filename == DB_FILENAME:\\n continue\\n abs_filename = (dirpath / filename).absolute()\\n if abs_filename in self.entries:\\n entry = self.entries[abs_filename]\\n existing_files.add(entry)\\n st = lstat(str(abs_filename))\\n if entry != st:\\n modified.add(entry)\\n else:\\n try:\\n entry = HashEntry(abs_filename)\\n entry.update_attrs()\\n added.add(entry)\\n except FileNotFoundError:\\n # If file was removed between listing and processing,\\n # just treat it as if it never existed\\n # We have nothing to compare it to anyway\\n pass\\n removed = set(self.entries.values()) - existing_files\\n return added, removed, modified\",\n \"def process_files(file_location, day):\\n # construct file path\\n file_dir = PREFIX+file_location\\n file_pattern = file_dir+'lz_'+day+'*_raw.root'\\n # print(file_pattern)\\n file_list = glob.glob(file_pattern)\\n print(\\\"There are %s MC files in the requested directory (%s).\\\" %(len(file_list), file_dir))\\n file_names = []\\n for f in file_list:\\n file_name_only = f.split('/')\\n file_names.append(file_name_only[-1])\\n return file_names\",\n \"def get_list_of_copied_sample_files(self, repo, changeset_revision, dir):\\n deleted_sample_files = []\\n sample_files = []\\n for changeset in hg_util.reversed_upper_bounded_changelog(repo, changeset_revision):\\n changeset_ctx = repo.changectx(changeset)\\n for ctx_file in changeset_ctx.files():\\n ctx_file_name = basic_util.strip_path(ctx_file)\\n # If we decide in the future that files deleted later in the changelog should\\n # not be used, we can use the following if statement. if ctx_file_name.endswith( '.sample' )\\n # and ctx_file_name not in sample_files and ctx_file_name not in deleted_sample_files:\\n if ctx_file_name.endswith('.sample') and ctx_file_name not in sample_files:\\n fctx = hg_util.get_file_context_from_ctx(changeset_ctx, ctx_file)\\n if fctx in ['DELETED']:\\n # Since the possibly future used if statement above is commented out, the\\n # same file that was initially added will be discovered in an earlier changeset\\n # in the change log and fall through to the else block below. In other words,\\n # if a file named blast2go.loc.sample was added in change set 0 and then deleted\\n # in changeset 3, the deleted file in changeset 3 will be handled here, but the\\n # later discovered file in changeset 0 will be handled in the else block below.\\n # In this way, the file contents will always be found for future tools even though\\n # the file was deleted.\\n if ctx_file_name not in deleted_sample_files:\\n deleted_sample_files.append(ctx_file_name)\\n else:\\n sample_files.append(ctx_file_name)\\n tmp_ctx_file_name = os.path.join(dir, ctx_file_name.replace('.sample', ''))\\n fh = open(tmp_ctx_file_name, 'wb')\\n fh.write(fctx.data())\\n fh.close()\\n return sample_files, deleted_sample_files\",\n \"def get_changed_files():\\n upstream = \\\"origin/master\\\"\\n local_commit = subprocess.check_output(\\n \\\"git rev-list HEAD ^{} -- 2>/dev/null | tail -1\\\".format(upstream),\\n shell=True).strip().decode()\\n diff_base = subprocess.check_output(\\n ['git', 'rev-parse', local_commit +\\n '^']).strip().decode() if local_commit else \\\"HEAD\\\"\\n files = subprocess.check_output(['git', 'diff', '--name-only',\\n diff_base]).strip().decode().split('\\\\n')\\n\\n repo = subprocess.check_output(['git', 'rev-parse',\\n '--show-toplevel']).strip().decode()\\n # add prefixes so that all and targets can be specified relative to FUCHSIA_DIR\\n if repo.endswith('topaz'):\\n files = [os.path.join('topaz', p) for p in files]\\n elif repo.endswith('third_party/go'):\\n files = [os.path.join('third_party/go', p) for p in files]\\n\\n return files\",\n \"def find_todo():\\n remove_pyc_files()\\n local('grep -ir \\\"TODO\\\" *')\",\n \"def search(self):\\n files = os.listdir(self.filePath)\\n txt_file = []\\n for f in files:\\n f_ext = f.split('.')[-1]\\n if f_ext == self.flag:\\n if self.flag == 'txt':\\n txt_file.append(FileTxt(os.sep.join([self.filePath, f])))\\n\\n if self.flag == 'csv':\\n txt_file.append(FileCsv(os.sep.join([self.filePath, f])))\\n\\n return txt_file\",\n \"def _search(self, btn):\\n del btn\\n if self.txt_search.value:\\n found_files: Optional[List[Path]] = None\\n while found_files is None:\\n try:\\n found_files = list(self.current_folder.rglob(self.txt_search.value))\\n except FileNotFoundError:\\n pass\\n self.select_search.options = [\\n str(file) for file in found_files if file.exists()\\n ]\",\n \"def list_all_files(self, branch_name) -> dict:\\n try:\\n files_response = self.repo.get_contents(self.repo_folder,\\n ref=branch_name)\\n files_list = []\\n for file in files_response:\\n files_list.append(file.raw_data.get('path'))\\n return make_success_response(200, files_list)\\n except GithubException as github_exc:\\n return make_error_response(github_exc.status, github_exc.data)\",\n \"def ImportRepo(\\n session: orm.session.Session,\\n language: scrape_repos_pb2.LanguageToClone,\\n metafile: pathlib.Path,\\n) -> None:\\n meta = pbutil.FromFile(metafile, scrape_repos_pb2.GitHubRepoMetadata())\\n clone_dir = metafile.parent / f\\\"{meta.owner}_{meta.name}\\\"\\n repo = contentfiles.GitHubRepository.GetOrAdd(session, meta)\\n repo.language = language.language\\n session.flush()\\n\\n for importer in language.importer:\\n if not importer.source_code_pattern:\\n app.Error(\\\"No source_code_pattern specified! Stopping now.\\\")\\n return\\n\\n pat = importer.source_code_pattern\\n pat = f\\\"{clone_dir}/{pat[1:]}\\\" if pat[0] == \\\"^\\\" else f\\\"{clone_dir}/{pat}\\\"\\n cmd = [\\n \\\"find\\\",\\n str(clone_dir),\\n \\\"-type\\\",\\n \\\"f\\\",\\n \\\"-regex\\\",\\n pat,\\n \\\"-not\\\",\\n \\\"-path\\\",\\n \\\"*/.git/*\\\",\\n ]\\n app.Log(2, \\\"$ %s\\\", \\\" \\\".join(cmd))\\n paths = (\\n subprocess.check_output(cmd, universal_newlines=True).rstrip().split(\\\"\\\\n\\\")\\n )\\n if len(paths) == 1 and not paths[0]:\\n app.Log(2, \\\"No files to import from %s\\\", clone_dir)\\n return\\n app.Log(\\n 1,\\n \\\"Importing %s '%s' files from %s ...\\\",\\n humanize.Commas(len(paths)),\\n importer.source_code_pattern,\\n clone_dir.name,\\n )\\n all_files_relpaths = public.GetAllFilesRelativePaths(clone_dir)\\n\\n # Use progress bar if there's a lot of files to process.\\n if len(paths) > 1000:\\n bar = progressbar.ProgressBar(max_value=len(paths))\\n else:\\n bar = lambda x: x\\n\\n for path in bar(paths):\\n job = scrape_repos_pb2.ImportWorker(\\n clone_from_url=repo.clone_from_url,\\n clone_dir=str(clone_dir),\\n abspath=path,\\n all_files_relpaths=all_files_relpaths,\\n preprocessors=importer.preprocessor,\\n )\\n for output in ImportWorker(job):\\n session.add(output)\",\n \"def test_013_find_files(self):\\n HEADING()\\n db = self.db\\n\\n db.connect()\\n\\n # Clear all jobs currently in the database to ensure a correct final assertion\\n db.clear()\\n\\n # Add the jobs outlined in the YAML file\\n db.add_from_yaml(\\\"etc/jobs.yaml\\\")\\n inputs, outputs = db.find_jobs_with_file(\\\"in1.txt\\\")\\n\\n # Assert that the lengths of the inputs and outputs arrays are correct\\n count_fgrep = len(Shell.fgrep(\\\"in1.txt\\\", \\\"etc/jobs.yaml\\\").strip().split(\\\"\\\\n\\\"))\\n assert(len(inputs) == count_fgrep)\",\n \"def wingrep(self):\\n for folder, files_ in self.walk():\\n listed_files = self.list_appro_files(folder, files_)\\n for file_o in self.open_files(listed_files=listed_files):\\n self.search_in(file_o)\",\n \"def changelog(self, branch, since=None):\\n walker = Walker(self.repo, [self.latest_branch_revision(branch)])\\n for entry in walker:\\n if since is not None and entry.commit.id == since:\\n break\\n commit = entry.commit\\n files = Command(\\n 'git show --pretty=\\\"format:\\\" --name-only %s' % commit.id,\\n cwd=self.path,\\n ).out.split()\\n yield Commit(\\n commit.id,\\n commit.committer,\\n datetime.fromtimestamp(commit.commit_time),\\n commit.message,\\n files,\\n )\",\n \"def get_changed_files_from(old_commit_sha, new_commit_sha):\\n return check_output(\\n \\\"git diff-tree --no-commit-id --name-only -r {0}..{1}\\\".format(\\n old_commit_sha,\\n new_commit_sha\\n ).split(\\\" \\\")\\n ).decode('utf-8').strip()\",\n \"def findRepositoryByBackTracking():\\n \\n cLookBack = '.'\\n while(True):\\n cDir = os.path.abspath(cLookBack)\\n print(\\\"Searching in %s\\\" % cDir)\\n if os.path.isdir( os.path.join(cDir, DB_SUBFOLDER) ):\\n return cDir\\n else:\\n if os.path.abspath(cLookBack) == os.path.abspath(cLookBack + '/..'):\\n return os.path.abspath('.')\\n cLookBack = cLookBack + '/..'\\n \\n return cDir\",\n \"def _on_path(self, change):\\n if change.new:\\n self._git = G.Repo.init(change.new)\\n ignore = Path(change.new) / \\\".gitignore\\\"\\n if not ignore.exists():\\n ignore.write_text(\\\".ipynb_checkpoints/\\\")\\n self.commit(\\\"initial commit\\\")\\n\\n self._initialize_watcher()\\n self._update_head_history()\",\n \"def search_file(repos, path_parts, rev, search_re):\\n\\n # Read in each line of a checked-out file, and then use re.search to\\n # search line.\\n fp = repos.openfile(path_parts, rev, {})[0]\\n matches = 0\\n while 1:\\n line = fp.readline()\\n if not line:\\n break\\n # FIXME: Is there what can we do about file encoding?\\n if search_re.search(line.decode(\\\"utf-8\\\", \\\"surrogateescape\\\")):\\n matches = 1\\n fp.close()\\n break\\n return matches\",\n \"def find_build(branch, tag, build_path=None, old_build_path=None):\\n for directory_format in [build_path, old_build_path]:\\n if directory_format is None:\\n continue\\n loc = directory_format % (branch, tag)\\n if isdir(loc):\\n return loc\\n raise BuildNotFound(branch, tag)\",\n \"def filter_files(files, lang=EXTES):\\n\\n lang_specific = []\\n\\n for f in files:\\n if f.endswith(lang):\\n lang_specific.append(f)\\n return lang_specific\",\n \"def search_file(filename, term):\\n try:\\n filepath = root + filename\\n with open(filepath) as file:\\n return term in file.read().splitlines()\\n except FileNotFoundError:\\n with open(filepath,\\\"w+\\\") as file:\\n return (search_file(filename, term))\",\n \"def find_files(directory, patterns):\\n for root, dirs, files in os.walk(directory):\\n for basename in files:\\n if \\\".pyc\\\" not in basename and \\\"__pycache__\\\" not in basename:\\n for pattern in patterns:\\n if fnmatch.fnmatch(basename, pattern):\\n filename = os.path.join(root, basename)\\n yield filename\",\n \"def jjb_files(project, release):\\n files, skipped = set(), set()\\n file_ending = re.compile(r'ya?ml$')\\n search_string = re.compile(r'^\\\\s+stream:')\\n release_string = re.compile(r'- %s:' % release)\\n jjb_path = os.path.join('jjb', project)\\n\\n if not os.path.isdir(jjb_path):\\n logging.warn(\\\"JJB directory does not exist at %s, skipping job \\\"\\n \\\"creation\\\", jjb_path)\\n return (files, skipped)\\n\\n for file_name in os.listdir(jjb_path):\\n file_path = os.path.join(jjb_path, file_name)\\n if os.path.isfile(file_path) and file_ending.search(file_path):\\n if has_string(file_path, release_string):\\n skipped.add(file_path)\\n elif has_string(file_path, search_string):\\n files.add(file_path)\\n return (files, skipped)\",\n \"def find_FEfiles(self):\\n \\n\\t\\t# take each item in the list of files to eve.\\n for i in self.files_to_find:\\n filename1 = i[0] \\n file1_dye = i[1]\\n filename2 = i[2]\\n file2_dye = i[3]\\n out_file_prefix=i[4]\\n\\n # set filename as none to help identify when no match has been found below\\n file1_filename = None\\n file2_filename = None\\n\\n # search for a FE file which matches the filename pattern\\n for afile in os.listdir(self.chosenfolder):\\n # file 1\\n if fnmatch.fnmatch(afile, filename1):\\n file1_filename = afile\\n \\n # file 2\\n if fnmatch.fnmatch(afile, filename2):\\n file2_filename = afile\\n \\n # if both files have been identified add this to a new list\\n if file1_filename and file2_filename:\\n self.list_of_files.append((file1_filename, file1_dye, file2_filename, file2_dye,out_file_prefix))\\n\\t\\t\\t# if either file could not be identified report this.\\n else:\\n raise ValueError(\\\"no match for \\\" + filename1 + \\\" and \\\" + filename2)\",\n \"def _bbcomponentwalk(self):\\n for path, subdirs, files in os.walk(self.oedir()):\\n try:\\n del subdirs[subdirs.index('.svn')]\\n\\n except ValueError:\\n pass\\n \\n for file in files:\\n pf, ext = os.path.splitext(file)\\n if ext == '.bb':\\n yield pf.partition('_')[0]\",\n \"def searchfiles(pattern='C:\\\\\\\\RoboDK\\\\\\\\Library\\\\\\\\*.rdk'):\\n import glob\\n return glob.glob(pattern)\",\n \"def check_branch(\\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\\n ) -> None:\\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\\n\\n if not RE_BRANCH_NAME.match(branch_name):\\n res.append(f\\\"{comp_name}: Invalid branch name: {branch_name}\\\")\\n\\n for ver, version in sorted(branch.items()):\\n if not RE_VERSION_STRING.match(ver):\\n res.append(f\\\"{comp_name}/{branch_name}: Invalid version string: {ver}\\\")\\n\\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\\n if version.outdated:\\n update_to = [\\n o_version\\n for o_version in branch.values()\\n if not o_version.outdated\\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\\n == other_cksums\\n ]\\n if len(update_to) != 1:\\n res.append(\\n f\\\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \\\"\\n f\\\"versions to update to instead of exactly one\\\"\\n )\\n else:\\n bad_files = sorted(\\n relpath\\n for relpath, (path, fdata) in driver_cksums.items()\\n if util.file_sha256sum(path) != fdata.sha256\\n )\\n if bad_files:\\n res.append(f\\\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\\\")\\n\\n if not uptodate_files:\\n uptodate_files = driver_cksums\\n elif uptodate_files != driver_cksums:\\n res.append(\\n f\\\"{comp_name}/{branch_name}: All the up-to-date versions should \\\"\\n f\\\"define the same set of files with the same checksums\\\"\\n )\\n\\n if not any(not version.outdated for version in branch.values()):\\n res.append(f\\\"{comp_name}/{branch_name}: No non-outdated versions\\\")\",\n \"def findMayaFiles(directory):\\n\\n pass\",\n \"def match_files(gold_folder, sys_folder):\\n\\n print \\\"Compiling files...\\\"\\n # Get a list of files in the folders supplied.\\n gold_files = compile_files(gold_folder) # nnnnG.xml\\n sys_files = compile_files(sys_folder) # nnnnXXN.xml\\n\\n print \\\"%d gold files found in %s\\\" % (len(gold_files), base_name(gold_folder))\\n print \\\"%d system files found in %s\\\\n\\\" % (len(sys_files), base_name(sys_folder))\\n\\n print \\\"Matching system files to gold files...\\\"\\n # Match them up, where nnnn must be common in a pair.\\n pairs = [(f1, f2) for f1 in gold_files for f2 in sys_files\\n if base_name(f2).startswith(base_name(f1).split(\\\"GE.\\\")[0])]\\n\\n return pairs\",\n \"def __gitBranchList(self):\\n self.vcs.gitListTagBranch(self.project.getProjectPath(), False)\",\n \"def coffeescript_files():\\r\\n dirs = \\\" \\\".join(THEME_COFFEE_PATHS + [Env.REPO_ROOT / coffee_dir for coffee_dir in COFFEE_DIRS])\\r\\n return cmd('find', dirs, '-type f', '-name \\\\\\\"*.coffee\\\\\\\"')\",\n \"def get_relevant_files(self):\\n relevant_files = []\\n\\n if self.tree_cache is None:\\n return relevant_files\\n\\n tree = json.loads(self.tree_cache)\\n if \\\"commit\\\" in tree:\\n commit_data = tree[\\\"commit\\\"]\\n master_commit_files = commit_data[\\\"files\\\"]\\n\\n for patched_files in master_commit_files:\\n relevant_file_path = \\\"./\\\" + patched_files[\\\"path\\\"]\\n relevant_files.append(relevant_file_path)\\n\\n return relevant_files\",\n \"def find_files(suffix, path):\\n returnList = []\\n find_file_recursive(suffix, path, returnList)\\n return returnList\",\n \"def unix_find(pathin):\\n return [os.path.join(path, file)\\n for (path, dirs, files) in os.walk(pathin, followlinks=False)\\n for file in files]\",\n \"def getFiles(folder, pattern, labelfile):\\n # read labelfile\\n with open(labelfile, 'r') as f:\\n all_lines = f.readlines()\\n \\n # get filenames from labelfile\\n all_files = []\\n labels = []\\n check = True\\n for line in all_lines:\\n # using shlex we also allow spaces in filenames when escaped w. \\\"\\\"\\n splits = shlex.split(line)\\n file_name = splits[0]\\n class_id = splits[1]\\n\\n # strip all known endings, note: os.path.splitext() doesnt work for\\n # '.' in the filenames, so let's do it this way...\\n for p in ['.pkl.gz', '.txt', '.png', '.jpg', '.tif', '.ocvmb','.csv']:\\n if file_name.endswith(p):\\n file_name = file_name.replace(p,'')\\n\\n # get now new file name\\n true_file_name = os.path.join(folder, file_name + pattern)\\n all_files.append(true_file_name)\\n labels.append(class_id)\\n\\n return all_files, labels\",\n \"def flake8_scan_file(commit_sha, owner, repo, parent_sha=None):\\n if parent_sha is None:\\n parent_sha = get_commit_parent(commit_sha, owner, repo)\\n diff_url = GIT_COMPARE_URL.format(base=parent_sha,\\n head=commit_sha,\\n owner=owner,\\n repo=repo,\\n host=host_api)\\n diff_info = get(diff_url, auth=auth).json()\\n diff_content = get(diff_url,\\n auth=auth,\\n headers={\\\"Accept\\\": \\\"application/vnd.github.v3.diff\\\"}\\n ).content.decode('utf8')\\n patch_set = PatchSet(diff_content)\\n comments_per_file = {}\\n for file in diff_info['files']:\\n content = get(file['contents_url'], auth=auth).json()\\n file_content = get(content['download_url']).content\\n with open(\\\"flake8_tmp_file.py\\\", 'wb') as test_file:\\n test_file.write(file_content)\\n style_guide = flake8.get_style_guide(ignore=['E24', 'W503'])\\n style_guide.input_file('./flake8_tmp_file.py', )\\n results = style_guide._application.file_checker_manager.checkers[\\n 0].results\\n comments_per_line = {}\\n for code, line_n, offset, text, src in results:\\n if changed_in_diff(get_file_by_name(patch_set,\\n file['filename']), line_n):\\n comments = comments_per_line.get(line_n, [])\\n comments.append((file['filename'], line_n, offset, code, text))\\n comments_per_line[line_n] = comments\\n comments_per_file[file['filename']] = comments_per_line\\n return comments_per_file\",\n \"def get_files(self):\\n\\n cur = self.app.conn.cursor()\\n sql = \\\"select distinct case_text.fid, source.name from case_text join source on case_text.fid=source.id where \\\"\\n sql += \\\"caseid=? order by lower(source.name) asc\\\"\\n cur.execute(sql, [self.case['caseid'], ])\\n self.casefiles = cur.fetchall()\\n sql = \\\"select id, name, fulltext, mediapath, memo, owner, date, av_text_id from source order by source.name asc\\\"\\n cur.execute(sql)\\n self.allfiles = cur.fetchall()\\n msg = _(\\\"Files linked: \\\") + str(len(self.casefiles)) + \\\" / \\\" + str(len(self.allfiles))\\n self.ui.label_files_linked.setText(msg)\",\n \"def changed_files(conn, scan0, scan1):\\n c = conn.cursor()\\n c.execute(\\\"SELECT a.pathid as pathid, a.hashid, b.hashid FROM \\\" +\\n \\\"( SELECT pathid, hashid, scanid FROM files WHERE scanid=?) AS 'a' \\\" +\\n \\\"JOIN (SELECT pathid, hashid, scanid FROM FILES WHERE scanid=?) as 'b' \\\" +\\n \\\"ON a.pathid=b.pathid WHERE a.hashid != b.hashid\\\",\\n (scan0, scan1))\\n return (DBFile(f) for f in c)\",\n \"def touch_files_dependent_on_changes(kymera_path, dirs, suffixes, changes):\\n for dir in dirs:\\n if dir[0] != '/':\\n # This is a relative path to kymera root\\n dir = kymera_path + dir\\n if not os.path.exists(dir):\\n print \\\"Directory %s included in ALL_SRCDIRS, ALL_INCDIRS or CFG_LIBS doesn't exist, continuing...\\\" % dir\\n else:\\n for file_name in os.listdir(dir):\\n full_file_path= os.path.join(dir, file_name)\\n # Filter a list of filenames down to those with one of the given suffixes\\\"\\n if matching_file(suffixes, full_file_path):\\n # Find all the files from a set with one of a list of suffices\\n # containing one of the changed definitions\\n if grep_words(changes, full_file_path):\\n print \\\"Mark file for rebuild:\\\", full_file_path\\n touch_file(full_file_path)\",\n \"def get_list_of_comitted_files():\\n files = []\\n output = []\\n try:\\n output = subprocess.check_output(['git','diff-index', '--name-status', '--cached','HEAD']\\n ).decode(\\\"utf-8\\\")\\n except subprocess.CalledProcessError:\\n print(\\\"Error diff files get: trace %s\\\" % subprocess.CalledProcessError)\\n return files\\n\\n for result in output.split(\\\"\\\\n\\\"):\\n logging.info(result)\\n if result != '':\\n match = modified.match(result)\\n if match:\\n files.append(match.group('name'))\\n\\n return files\",\n \"def get_files_from_of_type(path: str, ext: str) -> List[str]:\\n files = []\\n for root, dirnames, filenames in os.walk(path):\\n for filename in fnmatch.filter(filenames, \\\"*.\\\" + str(ext)):\\n files.append(os.path.join(root, filename))\\n if not files:\\n logging.error(\\\"No language files found in folder: \\\" + str(os.sep.join([convert_vars.BASE_PATH, \\\"source\\\"])))\\n logging.debug(f\\\" --- found {len(files)} files of type {ext}. Showing first few:\\\\n* \\\" + str(\\\"\\\\n* \\\".join(files[:3])))\\n return files\",\n \"def cb_filelist(args):\\n req = args[\\\"request\\\"]\\n\\n pyhttp = req.getHttp()\\n config = req.getConfiguration()\\n pathinfo = pyhttp[\\\"PATH_INFO\\\"]\\n\\n if not pathinfo.startswith(\\\"/\\\" + TRIGGER):\\n return\\n\\n logger = tools.getLogger()\\n\\n data = req.getData()\\n data[INIT_KEY] = 1\\n datadir = config[\\\"datadir\\\"]\\n data['root_datadir'] = config['datadir']\\n wikidir = config.get(\\\"wikidir\\\", config['datadir'])\\n\\n # convert the / to os.sep so that we can use os.path stuff.\\n wikidir = wikidir.replace(\\\"/\\\", os.sep)\\n if not wikidir.endswith(os.sep):\\n wikidir = wikidir + os.sep\\n\\n page_name = pathinfo[len(\\\"/\\\" + TRIGGER)+1:]\\n\\n if not page_name:\\n return\\n\\n page_name = page_name.replace(\\\"/\\\", os.sep)\\n\\n if not page_name:\\n return\\n\\n if page_name.endswith(os.sep):\\n page_name = page_name[:-1]\\n\\n # if the page has a flavour, we use that. otherwise\\n # we default to the wiki flavour\\n page_name, flavour = os.path.splitext(page_name)\\n if flavour:\\n data[\\\"flavour\\\"] = flavour[1:]\\n\\n # wikifile should hold the absolute path on the file system to\\n # the wiki file we're looking at. if it's in a parent directory\\n # of wikidir, then we abort. \\n wikifile = os.path.normpath(os.path.join(wikidir, page_name))\\n if not wikifile.startswith(wikidir):\\n logger.info(\\\"wiki file requested '%s' is not in wikidir.\\\" % wikifile)\\n return []\\n\\n # we build our own config dict for the fileentry to kind of\\n # fake it into loading this file correctly rather than\\n # one of the entries.\\n newdatadir = wikidir\\n\\n ext = tools.what_ext(data[\\\"extensions\\\"].keys(), wikifile)\\n\\n if not ext:\\n logger.info(\\\"wiki file '%s' does not exist.\\\" % wikifile)\\n return []\\n\\n data['root_datadir'] = page_name + '.' + ext\\n data['bl_type'] = 'file'\\n wikifile = wikifile + \\\".\\\" + ext\\n\\n if not os.path.isfile(wikifile):\\n return []\\n\\n fe = FileEntry(req, wikifile, wikidir)\\n\\n # now we evaluate python code blocks\\n body = fe.getData()\\n body = eval_python_blocks(req, body)\\n body = \\\"<!-- STATIC PAGE START -->\\\\n\\\\n%s\\\\n<!-- STATIC PAGE END -->\\\\n\\\" % body\\n\\n # now we evaluate for wikilinks\\n body = connect_links(config[\\\"base_url\\\"],\\n data[\\\"extensions\\\"].keys(),\\n wikidir,\\n body)\\n\\n fe.setData(body)\\n\\n fe[\\\"absolute_path\\\"] = TRIGGER\\n fe[\\\"fn\\\"] = page_name\\n fe[\\\"file_path\\\"] = TRIGGER + \\\"/\\\" + page_name\\n fe[\\\"template_name\\\"] = \\\"wiki\\\"\\n\\n data['blog_title_with_path'] = \\\"%s : %s\\\" % \\\\\\n (config.get(\\\"blog_title\\\", \\\"\\\"), fe.get(\\\"title_escaped\\\", \\\"\\\"))\\n\\n # set the datadir back\\n config[\\\"datadir\\\"] = datadir\\n\\n return [fe]\",\n \"def test_case_2():\\n print(\\\"*********Test_case_2***********\\\")\\n path = os.path.join(os.path.dirname(__file__), 'testdir')\\n result = find_files(None, path)\\n print(result)\",\n \"def findthis(self):\\n db = DbWork()\\n searched_files = 0\\n for root, dir, files in os.walk(self.path):\\n for f in files:\\n searched_files += 1\\n try:\\n absolute = os.path.join(root, f)\\n if os.path.isfile(absolute):\\n size = os.path.getsize(absolute)\\n if size > self.size:\\n readfile = open(absolute).read(7500)\\n sh = hashlib.sha1(readfile).hexdigest()\\n db.insert(absolute, size, sh)\\n \\n except IOError:\\n pass\\n db.insert_opts(searched_files, self.size)\\n db.export()\",\n \"def main(self, *directories):\\n if not self.git and len(directories) == 0:\\n print (\\\"ERROR: At least one directory must be provided (or the \\\"\\n \\\"--git-precommit flag must be passed.\\\\n\\\")\\n self.help()\\n return\\n\\n if len(directories) > 0:\\n find = local['find']\\n files = []\\n for directory in directories:\\n real = os.path.expanduser(directory)\\n if not os.path.exists(real):\\n raise ValueError(\\\"{0} does not exist\\\".format(directory))\\n files.extend(find(real, '-not', '-name', '._*', '-name', '*.py').strip().split('\\\\n'))\\n else:\\n status = local['git']('status', '--porcelain', '-uno')\\n root = local['git']('rev-parse', '--show-toplevel').strip()\\n\\n # get all modified or added python files\\n modified = re.findall(r\\\"^\\\\s[AM]\\\\s+(\\\\S+\\\\.py)$\\\", status, re.MULTILINE)\\n\\n # now just get the path part, which all should be relative to the\\n # root\\n files = [os.path.join(root, line.split(' ', 1)[-1].strip())\\n for line in modified]\\n\\n if len(files) > 0:\\n print \\\"Linting {0} python files.\\\\n\\\".format(len(files))\\n lint(files)\\n else:\\n print \\\"No python files found to lint.\\\\n\\\"\",\n \"def ci_lookup(tree_hash):\\t\\n\\tquery = 'echo ' + tree_hash + ' | ~/lookup/showCnt tree | egrep \\\"' + '|'.join(ci_files) +'\\\"'\\n\\tout = bash(query)\\n\\t\\n\\t\\\"\\\"\\\"\\n\\t# alternate method\\n\\tblobs = search(tree_hash, 'tree')\\n\\tindex = {'mode':1, 'hash':1, 'name':2}\\n\\tci = False\\n\\tfor blob in blobs:\\n\\t\\tname = blob[index['name']]\\n\\t\\thash = blob[index['hash']]\\n\\t\\tif ((name in ci_files) or\\n\\t\\t (name in ci_config_dir and ';'+ci_config_dir[name] in search(hash, 'tree'))):\\n\\t\\t\\tci = True\\n\\t\\t\\tbreak\\n\\t\\\"\\\"\\\"\\n\\treturn bool(out)\",\n \"def FileGen(TargetDir, extension = \\\".gbk\\\",subset_tag = \\\"final\\\"):\\n \\n for F in next(os.walk(TargetDir))[2]:\\n print(F)\\n if F[-1*len(extension):] == extension:\\n if F.split(\\\".\\\")[-2][:len(subset_tag)] == subset_tag:\\n yield(TargetDir + '/' + F)\",\n \"def buildListOfFiles(searchGlob):\\n return [fpath for fpath in glob2.iglob(searchGlob) if os.path.isfile(fpath)]\",\n \"def flake8():\\n py_files = subprocess.check_output(\\\"git ls-files\\\")\\n if PY3:\\n py_files = py_files.decode()\\n py_files = [x for x in py_files.split() if x.endswith('.py')]\\n py_files = ' '.join(py_files)\\n sh(\\\"%s -m flake8 %s\\\" % (PYTHON, py_files), nolog=True)\",\n \"def append_subfiles(self, di, lines, lang):\\n if lang==\\\"en\\\":lang_index=1\\n elif lang==\\\"it\\\":lang_index=2\\n try:\\n #if di[0] in self.name_stripped:\\n for key in TREE.keys():\\n if di[0]==key and di[0] in self.name_stripped:\\n deco=[\\\" |- \\\" for i in range(len(TREE[key])-1)]\\n deco.append(\\\" `- \\\")\\n for d, fi in zip(deco, TREE[key]):\\n bough=\\\"%s%s\\\" % (d,fi[lang_index])\\n path=\\\"%s%s/%s/%s\\\" % (self.backstring, lang, di[0], fi[0])\\n lines.append(\\\"%s %s\\\" % (path, bough))\\n except IndexError:\\n pass\\n return lines\",\n \"def scan_root(str_ri, lst_rx, lst_rxe, db):\\n print(f\\\"scan_root(); Scanning: {str_ri, lst_rx, lst_rxe}\\\")\\n # get db info on this dir\\n dic_db = dict() # dic by ffn of files known to the db\\n str_sql = f\\\"SELECT * FROM files where filename like '{str_ri}%'\\\"\\n for row in db.execute(str_sql):\\n dic_db[row[0]] = row\\n # Remove files that no longer exist\\n lst_del_this = list()\\n for str_ffn_db in dic_db.keys():\\n if not os.path.isfile(str_ffn_db):\\n lst_del_this.append(str_ffn_db)\\n str_ffn_db__sql = str_ffn_db.replace(\\\"'\\\", \\\"''\\\")\\n str_sql = f\\\"DELETE FROM files WHERE filename='{str_ffn_db__sql}';\\\"\\n db.execute(str_sql)\\n db.commit()\\n for itm in lst_del_this: # can't change iterable from inside loop\\n del dic_db[itm]\\n # Walk the root-dir\\n num_cntfil = 0\\n for root, dirs, files in os.walk(str_ri):\\n for str_fn in files:\\n # if str_fn.lower().endswith('.jpg'):\\n # print(str_fn)\\n num_cntfil += 1\\n if not any([str_fn.endswith(e) for e in lst_rxe]):\\n str_ffn = os.path.join(root, str_fn)\\n if not any([str_ffn.startswith(x) for x in lst_rx]): # if the file is not excluded\\n if str_ffn in dic_db.keys(): # db knows this file\\n obj_bdg = dic_db[str_ffn]\\n tim, siz = timeandsize(str_ffn)\\n if tim == dic_db[str_ffn][1] and siz == dic_db[str_ffn][2]:\\n pass # print(f\\\" - skipping known file: {str_ffn} == {dic_db[str_ffn]}\\\") #\\n else:\\n ## print(f\\\"WTF: tim? {tim == dic_db[str_ffn][1]} siz? {siz == dic_db[str_ffn][2]} @ ffn: {str_ffn}\\\")\\n # time or date have changed - so re-scanning file, and update DB.\\n str_sql = f\\\"DELETE FROM files WHERE filename='{str_ffn}';\\\"\\n db.execute(str_sql)\\n db.commit()\\n add_file2db(str_ffn, db)\\n else: # db don't know this file - add it.\\n add_file2db(str_ffn, db)\\n if num_cntfil % 1000000 == 0:\\n print(f\\\"Count: {num_cntfil}: {str_ffn}\\\")\",\n \"def search_file(path, f):\\n output = []\\n for root, dirs, files in os.walk(path, topdown=True):\\n for file in files:\\n if file == f:\\n path = os.path.join(root, file)\\n output.append(path)\\n\\n return output\",\n \"def get_changed_packages(blob_name1, blob_name2, package_list):\\n changed_files = check_output(\\n 'git', 'diff', '--name-only', blob_name1, blob_name2)\\n changed_files = changed_files.split('\\\\n')\\n\\n result = set()\\n for filename in changed_files:\\n file_root = rootname(filename)\\n if file_root in package_list:\\n result.add(file_root)\\n\\n return sorted(result)\",\n \"def file_changes(self):\\n new = []\\n changed = []\\n deleted = []\\n parent = self.parent_tag\\n # Loop through the files and find the ones that have changed\\n for relative_path, file_dict in self.checksum[\\\"files\\\"].items():\\n if relative_path not in parent[\\\"files\\\"]:\\n new.append(relative_path)\\n elif file_dict[\\\"checksum\\\"] != parent[\\\"files\\\"][relative_path][\\\"checksum\\\"]:\\n changed.append(relative_path)\\n # Loop through the parent files and see which files have been deleted\\n for relative_path in parent[\\\"files\\\"].keys():\\n if relative_path not in self.checksum[\\\"files\\\"]:\\n deleted.append(relative_path)\\n return {\\\"new\\\": new, \\\"changed\\\": changed, \\\"deleted\\\": deleted}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5788485","0.57070714","0.5683101","0.5395942","0.53917253","0.5343768","0.5339315","0.52852684","0.5283768","0.52731115","0.5249437","0.52391535","0.5239085","0.52318555","0.5212088","0.51945984","0.5188822","0.5180843","0.51768464","0.5143592","0.5139872","0.5090148","0.50438267","0.50418264","0.5040352","0.5039161","0.50326693","0.50287473","0.5026828","0.5003503","0.4992478","0.49773446","0.49708512","0.4961746","0.4933851","0.49226496","0.49113563","0.49033433","0.49013212","0.48964918","0.48902372","0.48881724","0.48844287","0.48807895","0.4879628","0.48788998","0.48780409","0.48765004","0.48664775","0.48650795","0.48646685","0.4862391","0.4855286","0.48498434","0.48469374","0.48311272","0.48209134","0.48187009","0.48139954","0.48101065","0.4807108","0.4806235","0.48058963","0.48021102","0.48018914","0.4797633","0.47935084","0.4787061","0.47857562","0.4783492","0.47768685","0.47693795","0.47618544","0.47568163","0.4755934","0.47430146","0.47424626","0.47385088","0.47374403","0.47362146","0.47350404","0.47281298","0.47277483","0.47204146","0.47167444","0.4716413","0.47154626","0.47054595","0.46970397","0.46885562","0.46860027","0.46792564","0.46596184","0.46486035","0.46469405","0.46459088","0.4644607","0.46403793","0.4637744","0.46258026"],"string":"[\n \"0.5788485\",\n \"0.57070714\",\n \"0.5683101\",\n \"0.5395942\",\n \"0.53917253\",\n \"0.5343768\",\n \"0.5339315\",\n \"0.52852684\",\n \"0.5283768\",\n \"0.52731115\",\n \"0.5249437\",\n \"0.52391535\",\n \"0.5239085\",\n \"0.52318555\",\n \"0.5212088\",\n \"0.51945984\",\n \"0.5188822\",\n \"0.5180843\",\n \"0.51768464\",\n \"0.5143592\",\n \"0.5139872\",\n \"0.5090148\",\n \"0.50438267\",\n \"0.50418264\",\n \"0.5040352\",\n \"0.5039161\",\n \"0.50326693\",\n \"0.50287473\",\n \"0.5026828\",\n \"0.5003503\",\n \"0.4992478\",\n \"0.49773446\",\n \"0.49708512\",\n \"0.4961746\",\n \"0.4933851\",\n \"0.49226496\",\n \"0.49113563\",\n \"0.49033433\",\n \"0.49013212\",\n \"0.48964918\",\n \"0.48902372\",\n \"0.48881724\",\n \"0.48844287\",\n \"0.48807895\",\n \"0.4879628\",\n \"0.48788998\",\n \"0.48780409\",\n \"0.48765004\",\n \"0.48664775\",\n \"0.48650795\",\n \"0.48646685\",\n \"0.4862391\",\n \"0.4855286\",\n \"0.48498434\",\n \"0.48469374\",\n \"0.48311272\",\n \"0.48209134\",\n \"0.48187009\",\n \"0.48139954\",\n \"0.48101065\",\n \"0.4807108\",\n \"0.4806235\",\n \"0.48058963\",\n \"0.48021102\",\n \"0.48018914\",\n \"0.4797633\",\n \"0.47935084\",\n \"0.4787061\",\n \"0.47857562\",\n \"0.4783492\",\n \"0.47768685\",\n \"0.47693795\",\n \"0.47618544\",\n \"0.47568163\",\n \"0.4755934\",\n \"0.47430146\",\n \"0.47424626\",\n \"0.47385088\",\n \"0.47374403\",\n \"0.47362146\",\n \"0.47350404\",\n \"0.47281298\",\n \"0.47277483\",\n \"0.47204146\",\n \"0.47167444\",\n \"0.4716413\",\n \"0.47154626\",\n \"0.47054595\",\n \"0.46970397\",\n \"0.46885562\",\n \"0.46860027\",\n \"0.46792564\",\n \"0.46596184\",\n \"0.46486035\",\n \"0.46469405\",\n \"0.46459088\",\n \"0.4644607\",\n \"0.46403793\",\n \"0.4637744\",\n \"0.46258026\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64677894"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74595,"cells":{"query":{"kind":"string","value":"Find updated files from a branch for a language."},"document":{"kind":"string","value":"def find_updated_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n updated_files = trans.find_updated_files(REPOSITORY_NAME, branch, TARGET_LANG)\n if create_issue == 0:\n yield (\"\\n\".join(limit_result(list(updated_files.keys()))))\n else:\n yield (\"Processing....\")\n new_count, skip_count = build_update_issue(trans, branch, updated_files)\n yield (\"{} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n yield (\"Please cache issues again.\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def read_files(folder):\n print_header(\"READING FILES FROM FOLDER (RECURSIVE)\", \"=\")\n files = []\n for dirpath, dirnames, filenames in os.walk(folder):\n if not dirpath.endswith(\"updates\"):\n for filename in filenames:\n root, ext = os.path.splitext(filename)\n if ext.lower() == \".sql\":\n full_path = os.path.join(dirpath, filename)\n with open(full_path, \"r\") as f:\n sql = f.read()\n sql = sql.decode(\"latin-1\")\n\n files.append((filename, sql))\n return files","def get_files_changed():\n files_list = []\n test = os.popen('git show --name-only')\n repo_location = os.popen('git rev-parse --show-toplevel')\n repo_location = repo_location.readlines()\n repo_location = repo_location[0]\n repo_location = repo_location.replace('\\n', '')\n if \"Not a git repository\" in repo_location:\n files_list.append(\"Not a git repository\")\n return files_list\n files_list.append(repo_location.split('/')[-1])\n output = test.readlines()\n for a in range(6, len(output)):\n files_list.append(output[a].replace('\\n', ''))\n return files_list","def check_branch(\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\n ) -> None:\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\n\n if not RE_BRANCH_NAME.match(branch_name):\n res.append(f\"{comp_name}: Invalid branch name: {branch_name}\")\n\n for ver, version in sorted(branch.items()):\n if not RE_VERSION_STRING.match(ver):\n res.append(f\"{comp_name}/{branch_name}: Invalid version string: {ver}\")\n\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\n if version.outdated:\n update_to = [\n o_version\n for o_version in branch.values()\n if not o_version.outdated\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\n == other_cksums\n ]\n if len(update_to) != 1:\n res.append(\n f\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \"\n f\"versions to update to instead of exactly one\"\n )\n else:\n bad_files = sorted(\n relpath\n for relpath, (path, fdata) in driver_cksums.items()\n if util.file_sha256sum(path) != fdata.sha256\n )\n if bad_files:\n res.append(f\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\")\n\n if not uptodate_files:\n uptodate_files = driver_cksums\n elif uptodate_files != driver_cksums:\n res.append(\n f\"{comp_name}/{branch_name}: All the up-to-date versions should \"\n f\"define the same set of files with the same checksums\"\n )\n\n if not any(not version.outdated for version in branch.values()):\n res.append(f\"{comp_name}/{branch_name}: No non-outdated versions\")","def full_find(self, file, version):\n matches = []\n for root, dirnames, filenames in os.walk(self.checkout_path(version)):\n for filename in fnmatch.filter(filenames, file):\n matches.append(os.path.join(root, filename))\n return matches","def get_changed_files():\n upstream = \"origin/master\"\n local_commit = subprocess.check_output(\n \"git rev-list HEAD ^{} -- 2>/dev/null | tail -1\".format(upstream),\n shell=True).strip().decode()\n diff_base = subprocess.check_output(\n ['git', 'rev-parse', local_commit +\n '^']).strip().decode() if local_commit else \"HEAD\"\n files = subprocess.check_output(['git', 'diff', '--name-only',\n diff_base]).strip().decode().split('\\n')\n\n repo = subprocess.check_output(['git', 'rev-parse',\n '--show-toplevel']).strip().decode()\n # add prefixes so that all and targets can be specified relative to FUCHSIA_DIR\n if repo.endswith('topaz'):\n files = [os.path.join('topaz', p) for p in files]\n elif repo.endswith('third_party/go'):\n files = [os.path.join('third_party/go', p) for p in files]\n\n return files","def _get_changed_files():\n if not ci_diff_helper:\n return None\n\n try:\n config = ci_diff_helper.get_config()\n except OSError: # Not on CI.\n return None\n\n changed_files = ci_diff_helper.get_changed_files('HEAD', config.base)\n\n changed_files = set([\n './{}'.format(filename) for filename in changed_files])\n\n return changed_files","def _find_changes(self):\n added = set()\n modified = set()\n existing_files = set()\n for dirpath_str, _, filenames in walk(str(self.path)):\n dirpath = Path(dirpath_str)\n for filename in filenames:\n if filename == DB_FILENAME:\n continue\n abs_filename = (dirpath / filename).absolute()\n if abs_filename in self.entries:\n entry = self.entries[abs_filename]\n existing_files.add(entry)\n st = lstat(str(abs_filename))\n if entry != st:\n modified.add(entry)\n else:\n try:\n entry = HashEntry(abs_filename)\n entry.update_attrs()\n added.add(entry)\n except FileNotFoundError:\n # If file was removed between listing and processing,\n # just treat it as if it never existed\n # We have nothing to compare it to anyway\n pass\n removed = set(self.entries.values()) - existing_files\n return added, removed, modified","def detect_changed_files(self) -> list[Path]:\n repos = [(self.open_repo(), self.git_directory)]\n # Check server and api dirs too\n # Normally these are ignored but we need to check these\n if (server_repo_path := Path(self.git_directory, \"Paper-Server\")).exists():\n repos.append((pygit2.Repository(str(server_repo_path)), server_repo_path))\n if (api_repo_path := Path(self.git_directory, \"Paper-API\")).exists():\n repos.append((pygit2.Repository(str(api_repo_path)), api_repo_path))\n changed = []\n for repo, repo_path in repos:\n changed.extend(p.relative_to(self.git_directory) for p in detect_changed_files(repo, repo_path))\n changed.sort()\n return changed","def find_old_files(directory):\n # go to the directory\n os.chdir(directory)\n \n # construct the find command to interogate the directory\n find_cmd = [\"find\"]\n find_cmd.append(\"*\")\n find_cmd.append(\"-atime\")\n find_cmd.append(\"+\"+str(DAYS_OLD))\n \n # run the find command\n try:\n find = subprocess.Popen(\" \".join(find_cmd),shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\n except:\n raise\n # get the standard output\n out, err = find.communicate() # get the standard output\n found = out.decode().split() # split the text into a list\n \n # go back to our starting directory \n os.chdir(iwd)\n \n return(found)","def _findChangedFiles(self):\n changedFiles = []\n # calculate and update checksums always for ALL files\n for observedFile in self.observedFiles:\n if os.path.isfile(observedFile.filePath):\n currentChecksum = checksumFile(observedFile.filePath)\n else:\n currentChecksum = None\n # different values with None value checking\n if ((observedFile.lastChecksum is None\n and currentChecksum is not None)\n or observedFile.lastChecksum != currentChecksum):\n changedFiles.append(observedFile) # notify change\n observedFile.lastChecksum = currentChecksum # update checksum\n\n return changedFiles","def tracked_files(self, branch):\n d = {}\n ls_tree = self.git_cmd('ls-tree -r --name-only --full-tree %s' %\n branch)\n for rpath in ls_tree.splitlines():\n if rpath == '.gitignore':\n continue\n if branch.startswith('timestamps'):\n d[rpath] = pathlib.PosixPath(self.repodir, rpath)\n else:\n if not rpath.startswith(ROOT_SUBDIR):\n continue\n d[rpath] = EtcPath(self.repodir, rpath)\n return d","def download_history_for_files(self, folder, globstring, dropbox_location, recursive=True):\n if recursive:\n files = self._recursive_file_search(folder, globstring)\n else:\n files = glob(folder + globstring)\n\n #print(files)\n for f in files:\n #print(f)\n dropboxpath = os.path.relpath(f, dropbox_location).replace(\"\\\\\", \"/\")\n dropboxpath = '/' + (dropboxpath)\n self.download_revisions(dropboxpath)","def get_update_file_list(directory):\n update_files_list = set(UPDATE_FILES_STATIC)\n update_files_exclude = set(UPDATE_FILES_EXCLUDE)\n\n for root, dirs, files in os.walk(path.join(PATH_ROOT, directory)):\n for filen in files:\n if UPDATE_FILES_RE.match(filen):\n filep = path.join(root, filen)\n update_files_list.add(path.relpath(filep, PATH_ROOT))\n \n return update_files_list - update_files_exclude","def find_new_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n new_file_list = trans.find_new_files(\n REPOSITORY_NAME, branch, TARGET_LANG)\n\n if create_issue == 0:\n yield (\"\\n\".join(limit_result(new_file_list)))\n else:\n yield (\"Processing....\")\n new_count, skip_count = build_new_issue(trans, branch, new_file_list)\n yield (\"{} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n yield (\"Please cache issues again.\")","def checkGit(directory):","def pull(browser):\n fetched = 0\n table = browser.get_template_table()\n\n print('Checking all letters for changes...')\n for letter in table.rows:\n letter.console_msg()\n if letter.remote_modified():\n old_chk = letter.checksum\n letter.pull()\n if old_chk is None:\n letter.console_msg('fetched new letter @ {}'.format(letter.checksum[0:7]))\n else:\n letter.console_msg('updated from {} to {}'.format(old_chk[0:7], letter.checksum[0:7]))\n fetched += 1\n else:\n letter.console_msg('no changes')\n\n sys.stdout.write('\\n')\n\n sys.stdout.write(Fore.GREEN + '{} of {} files contained new modifications\\n'.format(fetched, len(table.rows)) + Style.RESET_ALL)\n\n # status['last_pull_date'] = datetime.now()\n table.status.save()","def git_callback(files): # type: (t.List[t.Tuple[str, str]]) -> None\n for dirpath, _dirnames, filenames in os.walk(os.path.join(data_context().content.root, '.git')):\n paths = [os.path.join(dirpath, filename) for filename in filenames]\n files.extend((path, os.path.relpath(path, data_context().content.root)) for path in paths)","def get_relevant_files(self):\n relevant_files = []\n\n if self.tree_cache is None:\n return relevant_files\n\n tree = json.loads(self.tree_cache)\n if \"commit\" in tree:\n commit_data = tree[\"commit\"]\n master_commit_files = commit_data[\"files\"]\n\n for patched_files in master_commit_files:\n relevant_file_path = \"./\" + patched_files[\"path\"]\n relevant_files.append(relevant_file_path)\n\n return relevant_files","def get_changed_packages(blob_name1, blob_name2, package_list):\n changed_files = check_output(\n 'git', 'diff', '--name-only', blob_name1, blob_name2)\n changed_files = changed_files.split('\\n')\n\n result = set()\n for filename in changed_files:\n file_root = rootname(filename)\n if file_root in package_list:\n result.add(file_root)\n\n return sorted(result)","def get_tracked(wcdir):\n wcdir = abspath(wcdir)\n if \".svn\" in os.listdir(wcdir):\n svn_ls = Popen([\"svn\", \"ls\", \"-R\", wcdir], stdout=PIPE).stdout.read()\n return [normpath(l) for l in svn_ls.splitlines()]\n else:\n allfiles = []\n for root, dirs, files in os.walk(wcdir):\n allfiles += [normpath(join(root, p)[len(wcdir)+1:]) for p in files+dirs]\n return allfiles","def changelog_updated(target_branch):\n\n output = subprocess.getoutput(['git diff HEAD origin/{}'.format(target_branch)])\n return 'a/changelog.md b/changelog.md' in output.lower()","def _on_watch_changes(self, *changes):\n self.dirty = self._git.is_dirty()\n if self._watcher:\n for change in self._watcher.changes:\n for tracker in self._trackers:\n tracked_path = Path(self._git.working_dir) / change[\"path\"]\n if tracker.path.resolve() == tracked_path.resolve():\n tracker._on_file_change(None)\n return [\n dict(a_path=diff.a_path, b_path=diff.b_path, change_type=diff.change_type)\n for diff in self._git.index.diff(None)\n ] + [\n dict(a_path=None, b_path=ut, change_type=\"U\")\n for ut in self._git.untracked_files\n ]","def get_changed_files(path_to_repository, ignore_subrepositories):\n diff = _get_diff_to_last_commit(path_to_repository, ignore_subrepositories)\n return [item.b_path for item in diff if item.change_type in _CHANGE_TYPES_CONSIDERED_FOR_PRECOMMIT]","def touch_files_dependent_on_changes(kymera_path, dirs, suffixes, changes):\n for dir in dirs:\n if dir[0] != '/':\n # This is a relative path to kymera root\n dir = kymera_path + dir\n if not os.path.exists(dir):\n print \"Directory %s included in ALL_SRCDIRS, ALL_INCDIRS or CFG_LIBS doesn't exist, continuing...\" % dir\n else:\n for file_name in os.listdir(dir):\n full_file_path= os.path.join(dir, file_name)\n # Filter a list of filenames down to those with one of the given suffixes\"\n if matching_file(suffixes, full_file_path):\n # Find all the files from a set with one of a list of suffices\n # containing one of the changed definitions\n if grep_words(changes, full_file_path):\n print \"Mark file for rebuild:\", full_file_path\n touch_file(full_file_path)","def file_changes(self):\n new = []\n changed = []\n deleted = []\n parent = self.parent_tag\n # Loop through the files and find the ones that have changed\n for relative_path, file_dict in self.checksum[\"files\"].items():\n if relative_path not in parent[\"files\"]:\n new.append(relative_path)\n elif file_dict[\"checksum\"] != parent[\"files\"][relative_path][\"checksum\"]:\n changed.append(relative_path)\n # Loop through the parent files and see which files have been deleted\n for relative_path in parent[\"files\"].keys():\n if relative_path not in self.checksum[\"files\"]:\n deleted.append(relative_path)\n return {\"new\": new, \"changed\": changed, \"deleted\": deleted}","def get_list_of_comitted_files():\n files = []\n output = []\n try:\n output = subprocess.check_output(['git','diff-index', '--name-status', '--cached','HEAD']\n ).decode(\"utf-8\")\n except subprocess.CalledProcessError:\n print(\"Error diff files get: trace %s\" % subprocess.CalledProcessError)\n return files\n\n for result in output.split(\"\\n\"):\n logging.info(result)\n if result != '':\n match = modified.match(result)\n if match:\n files.append(match.group('name'))\n\n return files","def get_changed_files_and_content(path_to_repository, file_encoding, ignore_subrepositories):\n changed_files = filter_changed_files(get_changed_files(path_to_repository, ignore_subrepositories),\n path_to_repository, file_encoding)\n return {filename: open(os.path.join(path_to_repository, filename), encoding=file_encoding).read() for filename in\n changed_files}","def get_modified_files(repo, args):\n commit = repo.commit(args.commit)\n return commit.stats.files","def calc_lang_features(commits, author):\n\tlang_features = ['/\\*\\*', '\\\\\"\\\\\"\\\\\"', '///', # documentation\n\t\t\t'^\\s*@', 'def.+:.+->', 'using\\s+System\\.ComponentModel\\.DataAnnotations', # assertion\n\t\t\t'assert', 'TODO', 'lambda']\n\n\t# delete contents\n\topen('lang_features.csv', 'w').close()\n\t\n\tfor count, commit in enumerate(commits):\n\t\t# status update\n\t\tif (count + 1) % 5 == 0:\n\t\t\tprint commit, '.. ..', count + 1, ' / ', len(commits)\n\n\n\t\t\t# for each blob modified\n\t\tquery = (\"for x in $(echo \" + commit + \" | ssh da4 ~/lookup/cmputeDiff2.perl); do \" +\n\t\t\t\t# get the chold and parent blob\n\t\t\t\t\"diff_blobs=$(echo $x | awk -v RS=';' 1 | sed -n '3,4 p');\" +\n\t\t\t\t# if a parent blob does not exist, the author authored all of the content of the file\n\t\t\t\t\"if [ $(echo $diff_blobs|wc -w) -eq 1 ]; then \" +\n\t\t\t\t\t\"echo $diff_blobs | ~/lookup/showCnt blob 2> /dev/null; \" +\n\t\t\t\t# if a parent blob exists, find the diff, in order to search only the modified lines\n\t\t\t\t\"elif [ $(echo $diff_blobs|wc -w) -eq 2 ]; then \" +\n\t\t\t\t\t\"vars=( $diff_blobs );\" +\n\t\t\t\t\t# using bash instead of sh in order to use the process substitution,\n\t\t\t\t\t# to get the modified lines\n\t\t\t\t\t\"/bin/bash -c \\\"diff <(echo ${vars[0]} | ~/lookup/showCnt blob)\" +\n\t\t\t\t\t\t\t\t\" <(echo ${vars[1]} | ~/lookup/showCnt blob)\\\";\" +\n\t\t\t\t\"fi;\" +\n\t\t\t# grep the above practices and discard the lines that were deleted from the parent blob\n\t\t\t# (they start with \">\" in diff)\n\t\t\t\"done | egrep \\\"\" + \"|\".join(lang_features) + \"\\\" | grep -v '^>' | wc -l \")\n\t\tcount_uses = int(bash(query).strip())\n\t\tif count_uses > 0: # good practice feature is used\n\t\t\tout = bash('echo ' + commit + ' | ~/lookup/getValues c2P')\n\t\t\tmain_proj = out.strip().split(';')[1]\n\t\t\ttime = search(commit, 'commit')[2]\n\n\t\t\tf = open(\"lang_features.csv\", \"a\")\n\t\t\tprint 'lang_f'\n\t\t\tf.write(author + ', ' + 'LANG_F' + ', ' + str(time) + ', ' + main_proj + ', ' + str(count_uses) + '\\n')\n\t\t\tf.close()\n\t\t\tprint 'wrote: -->', commit","def find(self, file, version):\n matches = []\n for root, dirnames, filenames in os.walk(self.full_doc_path(version)):\n for filename in fnmatch.filter(filenames, file):\n matches.append(os.path.join(root, filename))\n return matches","def get_changed_files_from(old_commit_sha, new_commit_sha):\n return check_output(\n \"git diff-tree --no-commit-id --name-only -r {0}..{1}\".format(\n old_commit_sha,\n new_commit_sha\n ).split(\" \")\n ).decode('utf-8').strip()","def find_langs(args):\n infile = args.source\n langs = {}\n for line in infile:\n name_and_files = line.split()\n name = name_and_files[0]\n if name not in langs:\n langs[name] = []\n langs[name] += read_files.filter_files(name_and_files[1:])\n langs[args.unknown] += read_files.filter_files(args.classify)\n return langs","def find_cache(hw, r):\n\n cmd = \"find . -name CMakeCache.txt\"\n p = Popen(cmd, shell=True, stdout=PIPE)\n out = p.stdout.readlines()\n p.stdout.close()\n p.wait()\n \n # Transform the output into something readable.\n for i in out:\n found = i.split(':')\n \n # Scrub the path name\n path = os.path.dirname(found[0])[2:]\n if path == \"__build__\":\n continue\n r.note(\"found build files in '{0}'\".format(path))","def do_pull_file(dbsync, bibkey):\n pass","def find_build(branch, tag, build_path=None, old_build_path=None):\n for directory_format in [build_path, old_build_path]:\n if directory_format is None:\n continue\n loc = directory_format % (branch, tag)\n if isdir(loc):\n return loc\n raise BuildNotFound(branch, tag)","def all_changed_files(self):\n return [path_to_file_type(os.path.join(self.path, p)) for p in self.changed_paths() if p]","def svn_fs_paths_changed(*args):\r\n return _fs.svn_fs_paths_changed(*args)","def retrieve_modified_files(self):\n result = [(diff_obj.a_path, diff_obj.b_path)\n for diff_obj in self.repo.index.diff(None)]\n\n return result","def collect():\n\n # Get database.\n with open(local_directory(path='file_diffs/packages.json'), 'r') as f:\n store = json.load(f)\n\n # UI.\n print('Checking files for differences...\\n')\n\n # Iterate database.\n for package_name in store:\n # Package variables.\n package_dir = os.path.join(package_directory, package_name)\n package = store[package_name]\n\n # Recursive (lazy) package searching.\n if type(package) == str:\n package = os.path.expanduser(package)\n for dirpath, dirnames, filenames in os.walk(package):\n for filename in filenames:\n sub_package_dir = package_dir + dirpath.replace(package, '')\n if not os.path.exists(sub_package_dir):\n os.makedirs(sub_package_dir)\n\n fp_local = os.path.join(dirpath, filename)\n fp_remote = os.path.join(sub_package_dir, filename)\n\n cs_local = file_checksum(fp=fp_local)\n cs_remote = file_checksum(fp=fp_remote)\n\n if cs_remote != cs_local:\n print('Found: {}/{}'.format(package_name, filename))\n shutil.copyfile(src=fp_local, dst=fp_remote)\n\n # Manual package searching.\n if type(package) == list:\n for fp in package:\n fn_local = fp['local']\n fn_remote = fp['remote']\n\n fp_local = os.path.expanduser(fn_local)\n fp_remote = os.path.join(package_dir, fn_remote)\n\n cs_local = file_checksum(fp=fp_local)\n cs_remote = file_checksum(fp=fp_remote)\n\n if cs_remote != cs_local:\n print('Found: {}/{}'.format(package_name, fn_remote))\n\n remote_dir_path = '/'.join(fp_remote.split('/')[:-1])\n if not os.path.exists(remote_dir_path):\n os.makedirs(remote_dir_path)\n shutil.copyfile(src=fp_local, dst=fp_remote)","def main(self, *directories):\n if not self.git and len(directories) == 0:\n print (\"ERROR: At least one directory must be provided (or the \"\n \"--git-precommit flag must be passed.\\n\")\n self.help()\n return\n\n if len(directories) > 0:\n find = local['find']\n files = []\n for directory in directories:\n real = os.path.expanduser(directory)\n if not os.path.exists(real):\n raise ValueError(\"{0} does not exist\".format(directory))\n files.extend(find(real, '-not', '-name', '._*', '-name', '*.py').strip().split('\\n'))\n else:\n status = local['git']('status', '--porcelain', '-uno')\n root = local['git']('rev-parse', '--show-toplevel').strip()\n\n # get all modified or added python files\n modified = re.findall(r\"^\\s[AM]\\s+(\\S+\\.py)$\", status, re.MULTILINE)\n\n # now just get the path part, which all should be relative to the\n # root\n files = [os.path.join(root, line.split(' ', 1)[-1].strip())\n for line in modified]\n\n if len(files) > 0:\n print \"Linting {0} python files.\\n\".format(len(files))\n lint(files)\n else:\n print \"No python files found to lint.\\n\"","def findRepositoryByBackTracking():\n \n cLookBack = '.'\n while(True):\n cDir = os.path.abspath(cLookBack)\n print(\"Searching in %s\" % cDir)\n if os.path.isdir( os.path.join(cDir, DB_SUBFOLDER) ):\n return cDir\n else:\n if os.path.abspath(cLookBack) == os.path.abspath(cLookBack + '/..'):\n return os.path.abspath('.')\n cLookBack = cLookBack + '/..'\n \n return cDir","def get_changed_files(self, old_commit, new_commit):\n if old_commit is not None and not self.pygit.descendant_of(\n new_commit, old_commit\n ):\n raise ValueError(\"Second commit must be a descendant of first commit\")\n\n old_index = pygit2.Index()\n new_index = pygit2.Index()\n if old_commit is not None:\n old_tree = self.pygit.get(old_commit).tree\n old_index.read_tree(old_tree)\n else:\n # This is a special hash that represents an empty tree\n old_tree = self.pygit.get(\"4b825dc642cb6eb9a060e54bf8d69288fbee4904\")\n\n new_tree = self.pygit.get(new_commit).tree\n new_index.read_tree(new_tree)\n\n for patch in self.pygit.diff(old_tree, new_tree):\n if patch.delta.status_char() != \"M\":\n continue\n\n if not patch.delta.new_file.path.startswith(\"locales/\"):\n continue\n\n old_file_oid = old_index[patch.delta.old_file.path].oid\n new_file_oid = new_index[patch.delta.new_file.path].oid\n old_file = self.pygit.get(old_file_oid)\n new_file = self.pygit.get(new_file_oid)\n yield patch.delta.new_file.path, old_file.data, new_file.data","def filter_changed_files(changed_files, path_to_repository, file_encoding):\n filtered_files = []\n for changed_file in changed_files:\n file_is_valid = True\n if (os.path.isdir(os.path.join(path_to_repository, changed_file))):\n #ignore directories (e.g., git submodule folders)\n continue\n if os.path.getsize(os.path.join(path_to_repository, changed_file)) > 1 * 1024 * 1024:\n print('File too large for precommit analysis. Ignoring: %s' % changed_file)\n file_is_valid = False\n\n try:\n with open(os.path.join(path_to_repository, changed_file), encoding=file_encoding) as file:\n file.read()\n except UnicodeDecodeError:\n encoding_string = file_encoding\n if encoding_string is None:\n encoding_string = locale.getpreferredencoding() + ' (system encoding)'\n\n print(\n 'File at %s is not encoded in %s. Try using the --file-encoding option.' % (\n changed_file, encoding_string))\n file_is_valid = False\n\n if file_is_valid:\n filtered_files.append(changed_file)\n\n return filtered_files","def ListChangedFiles(self, request, global_params=None):\n config = self.GetMethodConfig('ListChangedFiles')\n return self._RunMethod(\n config, request, global_params=global_params)","def find_files(directory, extension, magictext):\n global files_logged\n global found_text\n dir_path = os.path.abspath(directory)\n dir_files = os.listdir(dir_path)\n for file in dir_files:\n if file.endswith(extension) and file not in files_logged:\n logger.info('New file found: {}'.format(file))\n files_logged.append(file)\n if file.endswith(extension):\n file_path = os.path.join(dir_path, file)\n if find_string_in_files(file_path, magictext):\n break\n for file in files_logged:\n if file not in dir_files:\n logger.info('File deleted: {}'.format(file))\n files_logged.remove(file)\n found_text[file] = 0","def list_all_files(self, branch_name) -> dict:\n try:\n files_response = self.repo.get_contents(self.repo_folder,\n ref=branch_name)\n files_list = []\n for file in files_response:\n files_list.append(file.raw_data.get('path'))\n return make_success_response(200, files_list)\n except GithubException as github_exc:\n return make_error_response(github_exc.status, github_exc.data)","def _submodule_pull_ff(path, branch):\n slab_logger.log(15, 'Fast forward pull of all ccs-data submodules')\n # Note: Branch defaults to master in the click application\n # TODO: Do more error checking here --> after debugging, definitely\n # TODO: checkout a branch ifexists in origin only--> not replacing git\n # or setup a tracking branch if there's nothing local or fail.\n path_to_reporoot = os.path.split(path)\n path_to_reporoot = os.path.split(path_to_reporoot[0])\n path_to_reporoot = path_to_reporoot[0]\n returncode, myinfo = run_this('git submodule foreach git pull --ff-only origin %s' %\n (branch), path_to_reporoot)\n return(returncode, myinfo)","def git_get_modified_files(\n paths: Iterable[Path], revrange: RevisionRange, cwd: Path\n) -> Set[Path]:\n relative_paths = {p.resolve().relative_to(cwd) for p in paths}\n str_paths = [path.as_posix() for path in relative_paths]\n if revrange.use_common_ancestor:\n rev2 = \"HEAD\" if revrange.rev2 == WORKTREE else revrange.rev2\n merge_base_cmd = [\"merge-base\", revrange.rev1, rev2]\n rev1 = _git_check_output_lines(merge_base_cmd, cwd)[0]\n else:\n rev1 = revrange.rev1\n diff_cmd = [\n \"diff\",\n \"--name-only\",\n \"--relative\",\n rev1,\n # revrange.rev2 is inserted here if not WORKTREE\n \"--\",\n *str_paths,\n ]\n if revrange.rev2 != WORKTREE:\n diff_cmd.insert(diff_cmd.index(\"--\"), revrange.rev2)\n lines = _git_check_output_lines(diff_cmd, cwd)\n if revrange.rev2 == WORKTREE:\n ls_files_cmd = [\n \"ls-files\",\n \"--others\",\n \"--exclude-standard\",\n \"--\",\n *str_paths,\n ]\n lines.extend(_git_check_output_lines(ls_files_cmd, cwd))\n changed_paths = (Path(line) for line in lines)\n return {path for path in changed_paths if should_reformat_file(cwd / path)}","def update_date(dest=dest):\n for root, _, files in os.walk(dest):\n ignore = [\"README.md\",\"SUMMARY.md\"]\n _ = [edit_files(root + \"/\" + file) for file in files if (file not in ignore and file.endswith(\".md\"))]","def FindCheckerFiles(path):\n if not path:\n Logger.fail(\"No source path provided\")\n elif os.path.isfile(path):\n return [ path ]\n elif os.path.isdir(path):\n foundFiles = []\n for root, dirs, files in os.walk(path):\n for file in files:\n extension = os.path.splitext(file)[1]\n if extension in [\".java\", \".smali\"]:\n foundFiles.append(os.path.join(root, file))\n return foundFiles\n else:\n Logger.fail(\"Source path \\\"\" + path + \"\\\" not found\")","def _git_diff_files(ref=\"master\"):\n result = []\n command = [\"git\", \"diff\", \"--name-status\", \"%s\" % (ref)]\n exit_code, output = _execute(command)\n if exit_code != 0:\n print(\"Failed to diff files.\")\n sys.exit(1)\n\n for line in output.decode(\"utf-8\").splitlines():\n parts = line.split(\"\\t\")\n action = parts[0]\n name = parts[-1]\n action = action.lower()\n result.append((action, name))\n\n return result","def svn_fs_paths_changed2(*args):\r\n return _fs.svn_fs_paths_changed2(*args)","def get_files_from_git() -> Sequence[Path]:\n\n def get_files(cmd: str) -> Sequence[str]:\n output = subprocess.check_output(cmd, shell=True)\n return [os.fsdecode(x) for x in output.splitlines()]\n\n root = os.fsdecode(subprocess.check_output(\"git rev-parse --show-toplevel\", shell=True).strip())\n result: Set[str] = set()\n result.update(get_files(\"git diff --name-only --diff-filter=ACM --staged\"))\n result.update(get_files(\"git diff --name-only --diff-filter=ACM\"))\n result.update(get_files(\"git ls-files -o --full-name --exclude-standard\"))\n return sorted(Path(root, x) for x in result)","def files_changed(revish: Text,\n ignore_rules: Optional[Sequence[Text]] = None,\n include_uncommitted: bool = False,\n include_new: bool = False\n ) -> Tuple[List[Text], List[Text]]:\n files = repo_files_changed(revish,\n include_uncommitted=include_uncommitted,\n include_new=include_new)\n if not files:\n return [], []\n\n return exclude_ignored(files, ignore_rules)","def changed_files(conn, scan0, scan1):\n c = conn.cursor()\n c.execute(\"SELECT a.pathid as pathid, a.hashid, b.hashid FROM \" +\n \"( SELECT pathid, hashid, scanid FROM files WHERE scanid=?) AS 'a' \" +\n \"JOIN (SELECT pathid, hashid, scanid FROM FILES WHERE scanid=?) as 'b' \" +\n \"ON a.pathid=b.pathid WHERE a.hashid != b.hashid\",\n (scan0, scan1))\n return (DBFile(f) for f in c)","def filesearch(word=\"\"):\n logger.info('Starting filesearch')\n file = []\n for f in glob.glob(\"*\"):\n if word[0] == \".\":\n if f.endswith(word):\n file.append(f)\n\n elif word in f:\n file.append(f)\n #return file\n logger.debug(file)\n return file","def update_files(self):\n try:\n db_files = self.dbc.get_file_list(self.remote_directory)\n except rest.ErrorResponse as e:\n print str(datetime.datetime.now()) \\\n + \": Could not get remote file list.\"\n print e.reason\n return False\n new_files = set(db_files) - self.file_set\n old_files = self.file_set - set(db_files)\n if new_files != set() or old_files != set():\n self.file_set = set(db_files)\n for filename in new_files:\n try:\n self.dbc.get_file(filename)\n except rest.ErrorResponse as e:\n print str(datetime.datetime.now()) + e.reason\n for filename in old_files:\n try:\n os.remove(self.local_directory + \"/\" + filename)\n except OSError:\n pass\n print str(datetime.datetime.now()) + \": Fileset changed:\"\n print self.file_set\n email_changes(new_files, old_files)\n print str(datetime.datetime.now()) \\\n + \": Email sent from update_files().\"\n return True\n return False","def test_case_6():\n print(\"*********Test_case_6***********\")\n path = os.path.join(os.path.dirname(__file__), 'testdir', 't1.c')\n result = find_files('.c', path)\n print(result)","def search_file(repos, path_parts, rev, search_re):\n\n # Read in each line of a checked-out file, and then use re.search to\n # search line.\n fp = repos.openfile(path_parts, rev, {})[0]\n matches = 0\n while 1:\n line = fp.readline()\n if not line:\n break\n # FIXME: Is there what can we do about file encoding?\n if search_re.search(line.decode(\"utf-8\", \"surrogateescape\")):\n matches = 1\n fp.close()\n break\n return matches","def refresh():\n git.fetch()\n output = str(git.merge('--ff-only')).strip()\n if output != 'Already up to date.':\n print(output)\n git.fetch('--tags')","def validate(cfg: defs.Config) -> List[str]: # noqa: C901\n res: List[str] = []\n\n def check_branch(\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\n ) -> None:\n \"\"\"Validate versions within a single branch.\"\"\"\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\n\n if not RE_BRANCH_NAME.match(branch_name):\n res.append(f\"{comp_name}: Invalid branch name: {branch_name}\")\n\n for ver, version in sorted(branch.items()):\n if not RE_VERSION_STRING.match(ver):\n res.append(f\"{comp_name}/{branch_name}: Invalid version string: {ver}\")\n\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\n if version.outdated:\n update_to = [\n o_version\n for o_version in branch.values()\n if not o_version.outdated\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\n == other_cksums\n ]\n if len(update_to) != 1:\n res.append(\n f\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \"\n f\"versions to update to instead of exactly one\"\n )\n else:\n bad_files = sorted(\n relpath\n for relpath, (path, fdata) in driver_cksums.items()\n if util.file_sha256sum(path) != fdata.sha256\n )\n if bad_files:\n res.append(f\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\")\n\n if not uptodate_files:\n uptodate_files = driver_cksums\n elif uptodate_files != driver_cksums:\n res.append(\n f\"{comp_name}/{branch_name}: All the up-to-date versions should \"\n f\"define the same set of files with the same checksums\"\n )\n\n if not any(not version.outdated for version in branch.values()):\n res.append(f\"{comp_name}/{branch_name}: No non-outdated versions\")\n\n def check_component(comp_name: str, comp: defs.Component) -> None:\n \"\"\"Validate the definition of a single component.\"\"\"\n if not RE_COMP_NAME.match(comp_name):\n res.append(f\"Invalid component name: {comp_name}\")\n\n for branch_name, branch in sorted(comp.branches.items()):\n check_branch(comp_name, branch_name, branch)\n\n for comp_name, comp in sorted(cfg.all_components.components.items()):\n check_component(comp_name, comp)\n\n return res","def search_for_files(\n run_name: str,\n raw_extension: Optional[str] = None,\n cellpy_file_extension: Optional[str] = None,\n raw_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n project_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n cellpy_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n prm_filename: Union[pathlib.Path, str, None] = None,\n file_name_format: Optional[str] = None,\n reg_exp: Optional[str] = None,\n sub_folders: Optional[bool] = True,\n file_list: Optional[List[str]] = None,\n with_prefix: Optional[bool] = True,\n pre_path: Union[OtherPath, pathlib.Path, str, None] = None,\n) -> Tuple[List[str], str]:\n\n # TODO: @jepe - use reg_exp\n # TODO: @jepe - allow for searching in cloud\n # TODO: @jepe - how to implement searching in db?\n # TODO: update prms and conf file to allow for setting if search should be done in\n # sub-folders, several folders, db, cloud etc\n # TODO: @jepe - find a way to implement automatic file_list creation in a top level func.\n logging.debug(f\"searching for {run_name}\")\n version = 0.4\n t0 = time.time()\n\n if reg_exp is None:\n reg_exp = prms.FileNames.reg_exp\n\n if raw_extension is None:\n raw_extension = prms.FileNames.raw_extension\n\n if raw_extension is None:\n raw_extension = prms.FileNames.raw_extension\n\n if cellpy_file_extension is None:\n cellpy_file_extension = prms.FileNames.cellpy_file_extension\n\n # backward compatibility check:\n if cellpy_file_extension.startswith(\".\"):\n warnings.warn(\n \"Deprecation warning: cellpy_file_extension should not include the '.'\"\n )\n cellpy_file_extension = cellpy_file_extension[1:]\n\n if raw_file_dir is None:\n raw_file_dir = prms.Paths.rawdatadir\n\n if file_name_format is None:\n file_name_format = prms.FileNames.file_name_format\n\n # 'dressing' the raw_file_dir in a list in case we want to\n # search in several folders (not implemented yet):\n if not isinstance(raw_file_dir, (list, tuple)):\n raw_file_dir = [OtherPath(raw_file_dir)]\n else:\n raw_file_dir = [OtherPath(d) for d in raw_file_dir]\n\n if project_dir is not None:\n raw_file_dir = [r / project_dir for r in raw_file_dir]\n\n if reg_exp:\n logging.warning(f\"Got reg_exp: {reg_exp}\")\n logging.warning(\"Sorry, but using reg exp is not implemented yet.\")\n\n if prm_filename is not None:\n logging.debug(\"Sorry, reading prm file is not implemented yet.\")\n\n if cellpy_file_dir is None:\n cellpy_file_dir = OtherPath(prms.Paths.cellpydatadir)\n else:\n cellpy_file_dir = OtherPath(cellpy_file_dir)\n\n if file_name_format is None and reg_exp is None:\n try:\n # To be implemented in version 0.5:\n file_name_format = prms.FileNames.file_name_format\n except AttributeError:\n file_name_format = \"YYYYMMDD_[name]EEE_CC_TT_RR\"\n\n if file_name_format.upper() == \"YYYYMMDD_[NAME]EEE_CC_TT_RR\":\n # backward compatibility check\n if raw_extension.startswith(\".\"):\n warnings.warn(\n \"Deprecation warning: raw_extension should not include the '.'\"\n )\n raw_extension = raw_extension[1:]\n warnings.warn(f\"Removing it -> {raw_extension}\")\n # TODO: give warning/error-message if run_name contains more than one file (due to duplicate in db)\n glob_text_raw = f\"{run_name}*.{raw_extension}\"\n else:\n glob_text_raw = file_name_format\n\n logging.debug(f\"searching for raw files in: {raw_file_dir}\")\n logging.debug(f\"searching for {run_name}\")\n logging.debug(f\"using glob {glob_text_raw}\")\n\n cellpy_file = f\"{run_name}.{cellpy_file_extension}\"\n cellpy_file = cellpy_file_dir / cellpy_file\n # Not sure if for example pandas can handle OtherPath objects:\n cellpy_file = cellpy_file.full_path\n\n logging.debug(f\"generated cellpy filename {cellpy_file}\")\n\n if file_list is not None:\n if len(raw_file_dir) > 1:\n logging.info(\"you provided several raw file directories\")\n logging.debug(\"searching within provided list of files\")\n\n if with_prefix:\n glob_text_raw_for_list = f\"*{glob_text_raw}\"\n else:\n glob_text_raw_for_list = glob_text_raw\n\n run_files = fnmatch.filter(file_list, glob_text_raw_for_list)\n\n if pre_path is not None:\n pre_path = OtherPath(pre_path)\n run_files = list(map(lambda x: pre_path / x, run_files))\n\n else:\n run_files = []\n for d in raw_file_dir:\n if d.is_external:\n logging.debug(\"external file\")\n if not d.is_dir():\n warnings.warn(\"your raw file directory cannot be accessed!\")\n # raise cellpy.exceptions.IOError(\"your raw file directory cannot be accessed!\")\n _run_files = []\n else:\n logging.debug(f\"checking in folder {d}\")\n logging.debug(f\"{sub_folders=}\")\n if sub_folders:\n _run_files = d.rglob(glob_text_raw)\n\n else:\n _run_files = d.glob(glob_text_raw)\n\n _run_files = [str(_f.resolve()) for _f in _run_files]\n _run_files.sort()\n run_files.extend(_run_files)\n\n return run_files, cellpy_file","def check_for_translation_updates_with_repo_and_legalcodes(\n self, repo: git.Repo, legalcodes: Iterable[\"licenses.models.LegalCode\"]\n ):\n self.say(3, \"check if repo is dirty\")\n if repo.is_dirty():\n raise Exception(\n f\"Git repo at {settings.TRANSLATION_REPOSITORY_DIRECTORY} is\"\n \" dirty. We cannot continue.\"\n )\n self.say(2, \"Fetch to update repo\")\n repo.remotes.origin.fetch()\n resource_slugs_on_transifex = self.stats.keys()\n\n # We only have the BY* 4.0 licenses in our database so far.\n # We'd like to process one potential translation branch at a time.\n # For the BY* 4.0 licenses, there's a single translation branch for\n # each language. So identify all the languages and iterate over those.\n # (Except English)\n\n # Gather the files we need to update in git.\n # This is a dict with keys = branch names, and values dictionaries\n # mapping relative paths of files to update, to their contents\n # (bytes).\n self.branches_to_update = defaultdict(_empty_branch_object)\n self.legalcodes_to_update = []\n self.branch_objects_to_update = []\n\n legalcodes_with_updated_translations = []\n\n for legalcode in legalcodes:\n language_code = legalcode.language_code\n resource_slug = legalcode.license.resource_slug\n if resource_slug not in resource_slugs_on_transifex:\n self.say(\n 2, f\"Transifex has no resource {resource_slug}. Creating.\"\n )\n\n # Create the resource\n english_pofile = legalcode.get_english_pofile()\n pofile_content = get_pofile_content(english_pofile)\n self.create_resource(\n resource_slug=resource_slug,\n resource_name=legalcode.license.fat_code(),\n pofilename=os.path.basename(\n legalcode.translation_filename()\n ),\n pofile_content=pofile_content,\n )\n self.clear_transifex_stats()\n\n if language_code not in self.stats[resource_slug]:\n self.say(\n 2,\n f\"Transifex has no {language_code} translation for\"\n f\" {resource_slug}\",\n ) # pragma: no cover\n\n # Upload the language\n self.upload_messages_to_transifex(legalcode)\n self.clear_transifex_stats()\n\n # We have a translation in this language for this license on\n # Transifex.\n # When was it last updated?\n last_activity = self.stats[resource_slug][language_code][\n \"translated\"\n ][\"last_activity\"]\n last_tx_update = (\n iso8601.parse_date(last_activity) if last_activity else None\n )\n\n if legalcode.translation_last_update is None:\n # First time: initialize, don't create branch\n legalcode.translation_last_update = last_tx_update\n legalcode.save()\n self.say(2, f\"Initialized last update time for {legalcode}\")\n continue\n\n if last_tx_update <= legalcode.translation_last_update:\n # No change\n self.say(3, f\"No changes for {legalcode}\")\n continue\n\n # Translation has changed!\n self.say(2, f\"Translation has changed for {legalcode}\")\n legalcodes_with_updated_translations.append(legalcode)\n\n return self.handle_legalcodes_with_updated_translations(\n repo, legalcodes_with_updated_translations\n )","def get_changed_paths(*args, globs=None):\n if globs:\n args = list(args) + [\"--\", *globs]\n diff_output = git(\"diff\", \"--name-only\", *args)\n\n return set([line.strip() for line in diff_output.splitlines()])","def check_file_references(name):\n references = []\n for root, dirs, files in walk(Path(\".\")):\n for file_name in files:\n if is_notebook(file_name):\n nb_markdown_cells = markdown_cells(os.path.join(root, file_name))\n for cell in nb_markdown_cells:\n for line in cell:\n if name in line:\n references.append(file_name)\n else:\n with open(os.path.join(root, file_name), encoding=\"utf8\", errors='ignore') as non_nb_file:\n if name in non_nb_file.read():\n references.append(file_name)\n return references","def initialise_languages():\n is_language_folder = r\"^[^\\\\\\.]*\" # Cannot have backslash or dot.\n language_folder_path = os.path.join(definitions.ROOT_DIR, \"languages\")\n\n for root, dirs, files in os.walk(language_folder_path):\n for name in files:\n if name.startswith(\"_NEW_\") or name.startswith(\"_CHANGED_\"):\n # Files that are auto-generated will not be added.\n continue\n\n full_dir = os.path.join(root, name)\n relative_dir = full_dir.replace(language_folder_path + \"\\\\\", \"\")\n\n match = re.match(is_language_folder, relative_dir)\n language_id = match.group(0)\n\n language = None\n if language_id != \"cache\":\n language = definitions.LANGUAGES[language_id]\n else:\n language = definitions.CACHE_LANGUAGE\n\n if relative_dir == \"{id}\\\\commands.json\".format(id=language_id):\n # Take the commands.\n definitions.COMMANDS.add_command_localisation(full_dir, language_id)\n\n elif relative_dir == \"{id}\\\\languages.json\".format(id=language_id):\n # Take the languages.\n language.add_languages(full_dir)\n\n elif relative_dir == \"{id}\\\\meta.json\".format(id=language_id):\n # Take the metadata.\n language.add_meta(full_dir)\n\n elif relative_dir == \"{id}\\\\permissions.json\".format(id=language_id):\n # Take the permissions.\n language.add_permission_names(full_dir)\n\n elif relative_dir == \"{id}\\\\units.json\".format(id=language_id):\n language.add_units(full_dir)\n\n else:\n # Take the keys\n language.add_keys_from_path(full_dir)\n\n for name in dirs:\n full_dir = os.path.join(root, name)\n relative_dir = full_dir.replace(language_folder_path + \"\\\\\", \"\")\n\n match = re.fullmatch(is_language_folder, relative_dir)\n if match is not None:\n if name != \"cache\":\n definitions.LANGUAGES[name] = Language(name)\n else:\n definitions.CACHE_LANGUAGE = Language(name)","def _get_changes_to_be_committed(wit_path, current_id):\n\n if current_id != 'None':\n files = {os.path.relpath(file, os.path.join(wit_path, '.wit', 'images', current_id)): file\n for file in _get_all_files_names(\n wit_path, dir_name=os.path.join(wit_path, '.wit', 'images', current_id))}\n\n for file in _get_staging_area_files(wit_path, plus_root=True):\n if os.path.relpath(file, wit_path) in files:\n yield {os.path.basename(file): _compare_file(os.path.join(wit_path, '.wit', 'staging_area', file),\n files[os.path.relpath(file, wit_path)])}\n else:\n yield ''","def _bbcomponentwalk(self):\n for path, subdirs, files in os.walk(self.oedir()):\n try:\n del subdirs[subdirs.index('.svn')]\n\n except ValueError:\n pass\n \n for file in files:\n pf, ext = os.path.splitext(file)\n if ext == '.bb':\n yield pf.partition('_')[0]","def flake8_scan_file(commit_sha, owner, repo, parent_sha=None):\n if parent_sha is None:\n parent_sha = get_commit_parent(commit_sha, owner, repo)\n diff_url = GIT_COMPARE_URL.format(base=parent_sha,\n head=commit_sha,\n owner=owner,\n repo=repo,\n host=host_api)\n diff_info = get(diff_url, auth=auth).json()\n diff_content = get(diff_url,\n auth=auth,\n headers={\"Accept\": \"application/vnd.github.v3.diff\"}\n ).content.decode('utf8')\n patch_set = PatchSet(diff_content)\n comments_per_file = {}\n for file in diff_info['files']:\n content = get(file['contents_url'], auth=auth).json()\n file_content = get(content['download_url']).content\n with open(\"flake8_tmp_file.py\", 'wb') as test_file:\n test_file.write(file_content)\n style_guide = flake8.get_style_guide(ignore=['E24', 'W503'])\n style_guide.input_file('./flake8_tmp_file.py', )\n results = style_guide._application.file_checker_manager.checkers[\n 0].results\n comments_per_line = {}\n for code, line_n, offset, text, src in results:\n if changed_in_diff(get_file_by_name(patch_set,\n file['filename']), line_n):\n comments = comments_per_line.get(line_n, [])\n comments.append((file['filename'], line_n, offset, code, text))\n comments_per_line[line_n] = comments\n comments_per_file[file['filename']] = comments_per_line\n return comments_per_file","def get_and_update_versions ():\n\n try:\n get_comp_versions (\"ACE\")\n get_comp_versions (\"TAO\")\n\n if opts.update:\n files = []\n files += update_version_files (\"ACE\")\n files += update_version_files (\"TAO\")\n files += create_changelog (\"ACE\")\n files += create_changelog (\"TAO\")\n files += update_spec_file ()\n files += update_debianbuild ()\n\n commit (files)\n\n except:\n print (\"Fatal error in get_and_update_versions.\")\n raise","def find_files(\n base,\n languages=all_languages,\n dir_ignore=default_dir_ignore,\n file_ignore=default_file_ignore,\n):\n if base[-1] != \"/\":\n base += \"/\"\n\n def update_dirs(dirs):\n \"\"\"strip the ignored directories out of the provided list\"\"\"\n index = len(dirs) - 1\n for i, d in enumerate(reversed(dirs)):\n if d in dir_ignore:\n del dirs[index - i]\n\n # walk over base\n for root, dirs, files in os.walk(base):\n root = root.replace(base, \"\", 1)\n\n # strip ignored directories from the list\n update_dirs(dirs)\n\n for filename in files:\n if filename in file_ignore:\n # skip ignored files\n continue\n\n # try to figure out the language of the specified file\n fullpath = os.path.join(base, root, filename)\n language = lang_type(fullpath)\n\n # if the file is one of the langauges that we want return\n # its name and the language\n if language in languages:\n yield fullpath, language","def get_all_changes_for_package(\n provider_package_id: str,\n verbose: bool,\n base_branch: str,\n force: bool,\n) -> tuple[bool, list[list[Change]] | Change | None, str]:\n provider_details = get_provider_details(provider_package_id)\n current_version = provider_details.versions[0]\n current_tag_no_suffix = get_version_tag(current_version, provider_package_id)\n if verbose:\n console.print(f\"Checking if tag '{current_tag_no_suffix}' exist.\")\n if not force and not subprocess.call(\n get_git_tag_check_command(current_tag_no_suffix),\n cwd=provider_details.source_provider_package_path,\n stderr=subprocess.DEVNULL,\n ):\n if verbose:\n console.print(f\"The tag {current_tag_no_suffix} exists.\")\n # The tag already exists\n changes = subprocess.check_output(\n get_git_log_command(verbose, f\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\", current_tag_no_suffix),\n cwd=provider_details.source_provider_package_path,\n text=True,\n )\n if changes:\n provider_details = get_provider_details(provider_package_id)\n doc_only_change_file = os.path.join(\n provider_details.source_provider_package_path, \".latest-doc-only-change.txt\"\n )\n if os.path.exists(doc_only_change_file):\n with open(doc_only_change_file) as f:\n last_doc_only_hash = f.read().strip()\n try:\n changes_since_last_doc_only_check = subprocess.check_output(\n get_git_log_command(\n verbose, f\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\", last_doc_only_hash\n ),\n cwd=provider_details.source_provider_package_path,\n text=True,\n )\n if not changes_since_last_doc_only_check:\n console.print()\n console.print(\n \"[yellow]The provider has doc-only changes since the last release. Skipping[/]\"\n )\n # Returns 66 in case of doc-only changes\n sys.exit(66)\n if len(changes) > len(changes_since_last_doc_only_check):\n # if doc-only was released after previous release - use it as starting point\n # but if before - stay with the releases from last tag.\n changes = changes_since_last_doc_only_check\n except subprocess.CalledProcessError:\n # ignore when the commit mentioned as last doc-only change is obsolete\n pass\n\n console.print(f\"[yellow]The provider {provider_package_id} has changes since last release[/]\")\n console.print()\n console.print(f\"[bright_blue]Provider: {provider_package_id}[/]\\n\")\n changes_table, array_of_changes = convert_git_changes_to_table(\n f\"NEXT VERSION AFTER + {provider_details.versions[0]}\",\n changes,\n base_url=\"https://github.com/apache/airflow/commit/\",\n markdown=False,\n )\n print_changes_table(changes_table)\n return False, array_of_changes[0], changes_table\n else:\n console.print(f\"No changes for {provider_package_id}\")\n return False, None, \"\"\n if verbose:\n console.print(\"The tag does not exist. \")\n if len(provider_details.versions) == 1:\n console.print(\n f\"The provider '{provider_package_id}' has never been released but it is ready to release!\\n\"\n )\n else:\n console.print(f\"New version of the '{provider_package_id}' package is ready to be released!\\n\")\n next_version_tag = f\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\"\n changes_table = \"\"\n current_version = provider_details.versions[0]\n list_of_list_of_changes: list[list[Change]] = []\n for version in provider_details.versions[1:]:\n version_tag = get_version_tag(version, provider_package_id)\n changes = subprocess.check_output(\n get_git_log_command(verbose, next_version_tag, version_tag),\n cwd=provider_details.source_provider_package_path,\n text=True,\n )\n changes_table_for_version, array_of_changes_for_version = convert_git_changes_to_table(\n current_version, changes, base_url=\"https://github.com/apache/airflow/commit/\", markdown=False\n )\n changes_table += changes_table_for_version\n list_of_list_of_changes.append(array_of_changes_for_version)\n next_version_tag = version_tag\n current_version = version\n changes = subprocess.check_output(\n get_git_log_command(verbose, next_version_tag),\n cwd=provider_details.source_provider_package_path,\n text=True,\n )\n changes_table_for_version, array_of_changes_for_version = convert_git_changes_to_table(\n current_version, changes, base_url=\"https://github.com/apache/airflow/commit/\", markdown=False\n )\n changes_table += changes_table_for_version\n if verbose:\n print_changes_table(changes_table)\n return True, list_of_list_of_changes or None, changes_table","def push(browser, files):\n table = browser.get_template_table()\n table.open()\n\n if len(files) == 0:\n modified = []\n for letter in table.rows:\n if letter.local_modified():\n modified.append(letter)\n\n if len(modified) == 0:\n sys.stdout.write(Fore.GREEN + 'No files contained local modifications.' + Style.RESET_ALL + '\\n')\n return\n\n sys.stdout.write(Fore.GREEN + 'The following {} file(s) contains local modifications.'.format(len(modified)) + Style.RESET_ALL + '\\n')\n for letter in modified:\n print(' - {}'.format(letter.filename))\n files.append(letter.filename)\n\n msg = 'Push updates to Alma? '\n if input(\"%s (y/N) \" % msg).lower() != 'y':\n print('Aborting')\n return\n\n for filename in files:\n letter = table.get_letter(filename)\n\n old_chk = letter.checksum\n if letter.push():\n letter.console_msg('updated from {} to {}'.format(old_chk[0:7], letter.checksum[0:7]))\n sys.stdout.write('\\n')","def changelog(self, branch, since=None):\n walker = Walker(self.repo, [self.latest_branch_revision(branch)])\n for entry in walker:\n if since is not None and entry.commit.id == since:\n break\n commit = entry.commit\n files = Command(\n 'git show --pretty=\"format:\" --name-only %s' % commit.id,\n cwd=self.path,\n ).out.split()\n yield Commit(\n commit.id,\n commit.committer,\n datetime.fromtimestamp(commit.commit_time),\n commit.message,\n files,\n )","def get_patches_for_repo(patch_dir, repo, branch):\n # Sometimes patch_dir isn't given!\n if patch_dir is None:\n return []\n patch_path_pattern = os.path.join(patch_dir, branch, repo, '*.patch')\n return sorted(glob.glob(patch_path_pattern))","def up_to_date(db, args, pattern):\n\n any_out_of_date = False\n num_found_files = 0\n for arg in args:\n find_data = None\n try:\n find_data = eugene_sys.FindFiles(pattern(arg))\n\n num_found_files = num_found_files+len(find_data)\n except ValueError:\n #print(f\"couldn't open directory {arg} or no suitable files found within...\")\n continue\n\n for file in find_data:\n key = file['cFileName']\n record = file['ftLastWriteTime']\n\n if db.compareWithRecord(key, record): continue # Up-to-date\n\n db_record = db.readRecord(key)\n db.writeRecord(key, record)\n\n old = eugene_sys.GetDateTimeFormat(db_record) if db_record > 0 else \"(null)\"\n new = eugene_sys.GetDateTimeFormat(record)\n\n msg = f\"`{key}' not up to date\"\n msg += _PAD[len(msg):]\n\n print(f\"{msg} ({old} -> {new})...\")\n any_out_of_date = True\n\n # For the query to return a 'True' result\n # - No database record can be out-of-date\n # - At LEAST one file in the directories\n # given in 'args' must've matched the\n # pattern\n result = (num_found_files > 0) and not any_out_of_date\n\n if result: print(\" ...up to date!\")\n return result","def find_new_files(self):\r\n in_file_system = self._find_data_files(self.basepath,\r\n self.data_file_extension, [])\r\n status_msg('Search for data files. Found: {0}'.format(\r\n len(in_file_system)), True\r\n )\r\n status_msg('Check that there are no duplicate filenames', True)\r\n in_db = self._get_files_in_db()\r\n status_msg('Get list of files in data base. Found: {0}'.format(\r\n len(in_db)), True\r\n )\r\n for filename in in_file_system:\r\n # Filename has been added to the database both where a part of the\r\n # path was a part of the unique name and where it was not,\r\n # therefore we need to check for both AND we ignore filenames that\r\n # start with IGNORE\r\n if form_unique_name(filename) not in in_db and\\\r\n os.path.basename(filename) not in in_db and not\\\r\n os.path.basename(filename).startswith('IGNORE'):\r\n self.new_data_files.append(filename)\r\n\r\n print # Adds a new line to the statusses before parsing\r\n status_msg('Collecting new files. Found: {0}'.format(\r\n len(self.new_data_files)), True\r\n )","def fileCheck(path):\n print('[+] Checking For File patching ')\n for url in check_files:\n try:\n #File Rereive\n data = query(url)\n file_name = url.split(\"/\")[-1]\n _,tmp_file = tempfile.mkstemp(prefix=\"exitmap_%s_\" % file_name)\n with open(tmp_file, \"wb\") as fd:\n fd.write(data)\n for i in check_files_patch_results:\n if str(i.url) == str(url):\n if str(i.filehash) != str(sha512_file(tmp_file)):\n print('[+] ALERT File Patch FOUND !')\n print(' | exitnode : %s' % str(i.exitnode) )\n print(' |_________> url: %s' % str(i.url) )\n print(' |_________> filePath: %s' % str(i.filepath) )\n print(' |_________> fileHash: %s' % str(i.filehash) )\n #check_files_patch_results.append( File_Check_Results(url, file_name, tmp_file, path, sha512_file(tmp_file)) )\n else :\n print('[+] File (%s) seems to be ok' % i.url)\n break\n\n except Exception as err:\n print('[-] Error ! %s' % err)\n traceback.print_exc()\n pass\n return time.time()","def test_case_3():\n print(\"*********Test_case_3***********\")\n result = find_files('.c', None)\n print(result)","def edited_file_locations(self):","def apiFiles(language):\n if language in [\"Python3\", \"Python2\"]:\n apisDir = \\\n os.path.join(os.path.dirname(__file__), \"ProjectDjango\", \"APIs\")\n apis = glob.glob(os.path.join(apisDir, '*.api'))\n else:\n apis = []\n return apis","def find_todo():\n remove_pyc_files()\n local('grep -ir \"TODO\" *')","def _get_files(repo, patterns, options):\n ctx = repo[None]\n match = match_func(repo, ctx, patterns, options)\n try:\n status = ctx.status(listclean=True, listignored=True, listunknown=True)\n except TypeError:\n # Compatibility with older Mercurial versions.\n status = ctx.status(clean=True, ignored=True, unknown=True)\n modified = status[0]\n added = status[1]\n unknown = status[4]\n ignored = status[5]\n clean = status[6]\n files = []\n for file_list in [clean, modified, added]:\n for filename in file_list:\n if match(filename):\n files.append(filename)\n for file_list in [ignored, unknown]:\n for filename in file_list:\n if match.exact(filename):\n files.append(filename)\n return files","def _update_code_urls(self):\n\n to_ignore = [\".gitignore\", \".keep\", \"test_converter_internal_url.py\"]\n\n for root, _, files in walk(\n PyFunceble.CONFIG_DIRECTORY\n + directory_separator\n + \"PyFunceble\"\n + directory_separator\n ):\n # We loop through every directories and files in the `PyFunceble` directory.\n\n for file in files:\n # We loop through the list of files of the currently read directory.\n\n if file not in to_ignore and \"__pycache__\" not in root:\n # * The filename is not into the list of file to ignore.\n # and\n # * The directory we are reading is not `__pycache__`.\n\n if root.endswith(directory_separator):\n # The root directory ends with the directory separator.\n\n # We fix the path in the currently read file.\n self._update_docs(root + file)\n else:\n # The root directory does not ends with the directory separator.\n\n # We fix the path in the currently read file.\n # (after appending the directory separator between the root and file)\n self._update_docs(root + directory_separator + file)\n\n for root, _, files in walk(\n PyFunceble.CONFIG_DIRECTORY\n + directory_separator\n + \"tests\"\n + directory_separator\n ):\n # We loop through every directories and files in the `tests` directory.\n for file in files:\n # We loop through the list of files of the currently read directory.\n\n if file not in to_ignore and \"__pycache__\" not in root:\n # * The filename is not into the list of file to ignore.\n # and\n # * The directory we are reading is not `__pycache__`.\n\n if root.endswith(directory_separator):\n # The root directory ends with the directory separator.\n\n # We fix the path in the currently read file.\n self._update_docs(root + file)\n else:\n # The root directory does not ends with the directory separator.\n\n # We fix the path in the currently read file.\n # (after appending the directory separator between the root and file)\n self._update_docs(root + directory_separator + file)","def find_reference_files():\n for root, _, files in os.walk(\"./tests/references/\"):\n for basename in fnmatch.filter(files, \"*.tex\"):\n yield os.path.join(root, basename)","def find_deleted_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n deleted_file_list = trans.find_deleted_files(\n REPOSITORY_NAME, branch, TARGET_LANG)\n\n moved_files = trans.find_moved_files_in_deleted_files(REPOSITORY_NAME, branch, TARGET_LANG, deleted_file_list)\n\n deleted_files = list(sorted(set(deleted_file_list) - set(moved_files.values())))\n\n if create_issue == 0:\n yield (\"move: \\n\" + \"\\n\".join(limit_result(list(sorted(moved_files.values())))))\n yield (\"delete: \\n\" + \"\\n\".join(limit_result(deleted_file_list)))\n else:\n yield (\"Processing....\")\n client = self._github_operator(msg)\n new_count, skip_count = build_move_issue(trans, client, branch, moved_files)\n yield (\"move: {} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n new_count, skip_count = build_delete_issue(trans, client, branch, deleted_file_list)\n yield (\"delete: {} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n yield (\"Please cache issues again.\")","def verify_up_to_date(path, branch=\"master\"):\n\n sys.stdout.write(\" - Verifying your branch up to date:\")\n run_in_component(path, ['git', 'remote', 'update'])\n\n result = run_in_component(path, ['git', 'rev-list', 'HEAD...origin/%s' % branch, '--count'])\n count = int(result.strip())\n\n if count == 0:\n print(\" OKAY\")\n return\n\n print(\" FAILED\")\n\n raise GenericError(\"You branch is not up-to-date with remote branch: %d different commits\" % count)","def query(self):\r\n reports = self.get_relevant_reports()\r\n new_files = self.construct_report_dict(reports)\r\n updated, new_reports = self.is_updated(new_files, self.old_files)\r\n if len(self.old_files) != 0 and updated:\r\n self.process_changes(new_reports)\r\n self.old_files = new_files","def test_case_5():\n print(\"*********Test_case_5***********\")\n result = find_files('.c', \"\")\n print(result)","def coffeescript_files():\r\n dirs = \" \".join(THEME_COFFEE_PATHS + [Env.REPO_ROOT / coffee_dir for coffee_dir in COFFEE_DIRS])\r\n return cmd('find', dirs, '-type f', '-name \\\"*.coffee\\\"')","def on_dir_changed(self, event):\r\n\r\n if not self.searchin_update:\r\n pth = event.directory\r\n if pth is not None and exists(pth):\r\n self.searchin_update = True\r\n self.m_searchin_text.safe_set_value(pth)\r\n self.searchin_update = False\r\n event.Skip()","def svn_fs_contents_changed(*args):\r\n return _fs.svn_fs_contents_changed(*args)","def files_in_folder(self):\n non_til = set()\n filesInFolder = []\n for f in self.find_all_files():\n newstr = f.replace(\"~\", \"\") \n if newstr in self.find_all_files():\n non_til.add(newstr)\n for fs in non_til:\n filesInFolder.append(fs)\n return filesInFolder","def find_files():\n \n p = re.compile(REGEX_PART_NUMBER)\n job_files = []\n \n for root, dirs, files in os.walk(project_path): # r at start of string need to prevent unicode error\n for filename in files:\n re_part_number = p.match(filename)\n if re_part_number:\n file_ext = filename.split(\".\")[-1].lower() # extract file extension \n file_size = os.path.getsize((os.path.join(root, filename))) # filesize in bytes \n \n part_number = re_part_number.group() # extract part number from regular expression match\n part_code = part_number.split(\"-\")[0]\n \n destinations = [] # destinations is a list in case a filetype is both a source and output filetype\n \n if (file_ext in EXTS_SOURCE_FILES) and flag_find_source_files:\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \n \n if (file_ext in EXTS_OUTPUT_FILES) and flag_find_output_files:\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \n \n if destinations: \n job_files.append(File(filename,root,file_size,destinations,part_number,part_code))\n print(f\"Found: {filename}\")\n \n return job_files","def compare_old_and_new_change_status_files():\n rdict=dict()\n mastcontrol=dirutil.get_mast_control_path()\n mastscratch=dirutil.get_mast_scratch_path()\n recipedirs=dirutil.immediate_subdirs(os.path.join(mastcontrol,\"changestatusfiles\"))\n for recipedir in recipedirs:\n mystatus=\"unknown\"\n rdict[recipedir]=dict()\n changelist=list()\n if not os.path.exists(os.path.join(mastscratch,recipedir)):\n mystatus=\"archived\"\n else:\n ingreddirs = dirutil.immediate_subdirs(os.path.join(mastcontrol,\"changestatusfiles\",recipedir))\n for ingreddir in ingreddirs:\n scratchstatusfile = MASTFile(os.path.join(mastscratch,recipedir,ingreddir,\"change_status.txt\"))\n controlstatusfile = MASTFile(os.path.join(mastcontrol,\"changestatusfiles\",recipedir,ingreddir,\"change_status.txt\"))\n if scratchstatusfile.data == controlstatusfile.data:\n mystatus=\"unchanged\"\n else:\n mystatus=\"changed\"\n rdict[recipedir][ingreddir]=\"send\"\n rdict[recipedir][\"MAIN\"]=mystatus\n return rdict","def get_list_of_copied_sample_files(self, repo, changeset_revision, dir):\n deleted_sample_files = []\n sample_files = []\n for changeset in hg_util.reversed_upper_bounded_changelog(repo, changeset_revision):\n changeset_ctx = repo.changectx(changeset)\n for ctx_file in changeset_ctx.files():\n ctx_file_name = basic_util.strip_path(ctx_file)\n # If we decide in the future that files deleted later in the changelog should\n # not be used, we can use the following if statement. if ctx_file_name.endswith( '.sample' )\n # and ctx_file_name not in sample_files and ctx_file_name not in deleted_sample_files:\n if ctx_file_name.endswith('.sample') and ctx_file_name not in sample_files:\n fctx = hg_util.get_file_context_from_ctx(changeset_ctx, ctx_file)\n if fctx in ['DELETED']:\n # Since the possibly future used if statement above is commented out, the\n # same file that was initially added will be discovered in an earlier changeset\n # in the change log and fall through to the else block below. In other words,\n # if a file named blast2go.loc.sample was added in change set 0 and then deleted\n # in changeset 3, the deleted file in changeset 3 will be handled here, but the\n # later discovered file in changeset 0 will be handled in the else block below.\n # In this way, the file contents will always be found for future tools even though\n # the file was deleted.\n if ctx_file_name not in deleted_sample_files:\n deleted_sample_files.append(ctx_file_name)\n else:\n sample_files.append(ctx_file_name)\n tmp_ctx_file_name = os.path.join(dir, ctx_file_name.replace('.sample', ''))\n fh = open(tmp_ctx_file_name, 'wb')\n fh.write(fctx.data())\n fh.close()\n return sample_files, deleted_sample_files","def find_legacy_log_files(xcresult_path):\n\n result = []\n\n for root, dirs, files in os.walk(xcresult_path, topdown=True):\n for file in files:\n if file.endswith('.txt'):\n file = os.path.join(root, file)\n result.append(file)\n\n # Sort the files by creation time.\n result.sort(key=lambda f: os.stat(f).st_ctime)\n return result","def find_legacy_log_files(xcresult_path):\n\n result = []\n\n for root, dirs, files in os.walk(xcresult_path, topdown=True):\n for file in files:\n if file.endswith('.txt'):\n file = os.path.join(root, file)\n result.append(file)\n\n # Sort the files by creation time.\n result.sort(key=lambda f: os.stat(f).st_ctime)\n return result","def get_new_change_status_files():\n rdict=dict()\n mastcontrol=dirutil.get_mast_control_path()\n mastscratch=dirutil.get_mast_scratch_path()\n olddirs=list()\n recipedirs=dirutil.immediate_subdirs(os.path.join(mastcontrol,\"changestatusfiles\"))\n for recipedir in recipedirs:\n ingreddirs = dirutil.immediate_subdirs(os.path.join(mastcontrol,\"changestatusfiles\",recipedir))\n for ingreddir in ingreddirs:\n olddirs.append(\"%s/%s\" % (recipedir, ingreddir))\n srecdirs = dirutil.immediate_subdirs(mastscratch)\n for srecdir in srecdirs:\n singreddirs = dirutil.immediate_subdirs(os.path.join(mastscratch,srecdir))\n for singreddir in singreddirs:\n csfile = os.path.join(mastscratch, srecdir, singreddir, \"change_status.txt\")\n if os.path.isfile(csfile):\n if not \"%s/%s\" % (srecdir, singreddir) in olddirs:\n if not srecdir in rdict.keys():\n rdict[srecdir]=dict()\n rdict[srecdir][\"MAIN\"]=\"changed\"\n rdict[srecdir][singreddir]=\"send\"\n return rdict","def getFiles(folderToProcess,filter):\n\n print(f\"Parsing {folderToProcess} for {filter} files\")\n\n if debug:\n for path in Path(folderToProcess).rglob(filter):\n print(f\"Found {path}\")\n\n all_files = [str(x) for x in Path(folderToProcess).rglob(filter)] \n\n return all_files"],"string":"[\n \"def read_files(folder):\\n print_header(\\\"READING FILES FROM FOLDER (RECURSIVE)\\\", \\\"=\\\")\\n files = []\\n for dirpath, dirnames, filenames in os.walk(folder):\\n if not dirpath.endswith(\\\"updates\\\"):\\n for filename in filenames:\\n root, ext = os.path.splitext(filename)\\n if ext.lower() == \\\".sql\\\":\\n full_path = os.path.join(dirpath, filename)\\n with open(full_path, \\\"r\\\") as f:\\n sql = f.read()\\n sql = sql.decode(\\\"latin-1\\\")\\n\\n files.append((filename, sql))\\n return files\",\n \"def get_files_changed():\\n files_list = []\\n test = os.popen('git show --name-only')\\n repo_location = os.popen('git rev-parse --show-toplevel')\\n repo_location = repo_location.readlines()\\n repo_location = repo_location[0]\\n repo_location = repo_location.replace('\\\\n', '')\\n if \\\"Not a git repository\\\" in repo_location:\\n files_list.append(\\\"Not a git repository\\\")\\n return files_list\\n files_list.append(repo_location.split('/')[-1])\\n output = test.readlines()\\n for a in range(6, len(output)):\\n files_list.append(output[a].replace('\\\\n', ''))\\n return files_list\",\n \"def check_branch(\\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\\n ) -> None:\\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\\n\\n if not RE_BRANCH_NAME.match(branch_name):\\n res.append(f\\\"{comp_name}: Invalid branch name: {branch_name}\\\")\\n\\n for ver, version in sorted(branch.items()):\\n if not RE_VERSION_STRING.match(ver):\\n res.append(f\\\"{comp_name}/{branch_name}: Invalid version string: {ver}\\\")\\n\\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\\n if version.outdated:\\n update_to = [\\n o_version\\n for o_version in branch.values()\\n if not o_version.outdated\\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\\n == other_cksums\\n ]\\n if len(update_to) != 1:\\n res.append(\\n f\\\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \\\"\\n f\\\"versions to update to instead of exactly one\\\"\\n )\\n else:\\n bad_files = sorted(\\n relpath\\n for relpath, (path, fdata) in driver_cksums.items()\\n if util.file_sha256sum(path) != fdata.sha256\\n )\\n if bad_files:\\n res.append(f\\\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\\\")\\n\\n if not uptodate_files:\\n uptodate_files = driver_cksums\\n elif uptodate_files != driver_cksums:\\n res.append(\\n f\\\"{comp_name}/{branch_name}: All the up-to-date versions should \\\"\\n f\\\"define the same set of files with the same checksums\\\"\\n )\\n\\n if not any(not version.outdated for version in branch.values()):\\n res.append(f\\\"{comp_name}/{branch_name}: No non-outdated versions\\\")\",\n \"def full_find(self, file, version):\\n matches = []\\n for root, dirnames, filenames in os.walk(self.checkout_path(version)):\\n for filename in fnmatch.filter(filenames, file):\\n matches.append(os.path.join(root, filename))\\n return matches\",\n \"def get_changed_files():\\n upstream = \\\"origin/master\\\"\\n local_commit = subprocess.check_output(\\n \\\"git rev-list HEAD ^{} -- 2>/dev/null | tail -1\\\".format(upstream),\\n shell=True).strip().decode()\\n diff_base = subprocess.check_output(\\n ['git', 'rev-parse', local_commit +\\n '^']).strip().decode() if local_commit else \\\"HEAD\\\"\\n files = subprocess.check_output(['git', 'diff', '--name-only',\\n diff_base]).strip().decode().split('\\\\n')\\n\\n repo = subprocess.check_output(['git', 'rev-parse',\\n '--show-toplevel']).strip().decode()\\n # add prefixes so that all and targets can be specified relative to FUCHSIA_DIR\\n if repo.endswith('topaz'):\\n files = [os.path.join('topaz', p) for p in files]\\n elif repo.endswith('third_party/go'):\\n files = [os.path.join('third_party/go', p) for p in files]\\n\\n return files\",\n \"def _get_changed_files():\\n if not ci_diff_helper:\\n return None\\n\\n try:\\n config = ci_diff_helper.get_config()\\n except OSError: # Not on CI.\\n return None\\n\\n changed_files = ci_diff_helper.get_changed_files('HEAD', config.base)\\n\\n changed_files = set([\\n './{}'.format(filename) for filename in changed_files])\\n\\n return changed_files\",\n \"def _find_changes(self):\\n added = set()\\n modified = set()\\n existing_files = set()\\n for dirpath_str, _, filenames in walk(str(self.path)):\\n dirpath = Path(dirpath_str)\\n for filename in filenames:\\n if filename == DB_FILENAME:\\n continue\\n abs_filename = (dirpath / filename).absolute()\\n if abs_filename in self.entries:\\n entry = self.entries[abs_filename]\\n existing_files.add(entry)\\n st = lstat(str(abs_filename))\\n if entry != st:\\n modified.add(entry)\\n else:\\n try:\\n entry = HashEntry(abs_filename)\\n entry.update_attrs()\\n added.add(entry)\\n except FileNotFoundError:\\n # If file was removed between listing and processing,\\n # just treat it as if it never existed\\n # We have nothing to compare it to anyway\\n pass\\n removed = set(self.entries.values()) - existing_files\\n return added, removed, modified\",\n \"def detect_changed_files(self) -> list[Path]:\\n repos = [(self.open_repo(), self.git_directory)]\\n # Check server and api dirs too\\n # Normally these are ignored but we need to check these\\n if (server_repo_path := Path(self.git_directory, \\\"Paper-Server\\\")).exists():\\n repos.append((pygit2.Repository(str(server_repo_path)), server_repo_path))\\n if (api_repo_path := Path(self.git_directory, \\\"Paper-API\\\")).exists():\\n repos.append((pygit2.Repository(str(api_repo_path)), api_repo_path))\\n changed = []\\n for repo, repo_path in repos:\\n changed.extend(p.relative_to(self.git_directory) for p in detect_changed_files(repo, repo_path))\\n changed.sort()\\n return changed\",\n \"def find_old_files(directory):\\n # go to the directory\\n os.chdir(directory)\\n \\n # construct the find command to interogate the directory\\n find_cmd = [\\\"find\\\"]\\n find_cmd.append(\\\"*\\\")\\n find_cmd.append(\\\"-atime\\\")\\n find_cmd.append(\\\"+\\\"+str(DAYS_OLD))\\n \\n # run the find command\\n try:\\n find = subprocess.Popen(\\\" \\\".join(find_cmd),shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\\n except:\\n raise\\n # get the standard output\\n out, err = find.communicate() # get the standard output\\n found = out.decode().split() # split the text into a list\\n \\n # go back to our starting directory \\n os.chdir(iwd)\\n \\n return(found)\",\n \"def _findChangedFiles(self):\\n changedFiles = []\\n # calculate and update checksums always for ALL files\\n for observedFile in self.observedFiles:\\n if os.path.isfile(observedFile.filePath):\\n currentChecksum = checksumFile(observedFile.filePath)\\n else:\\n currentChecksum = None\\n # different values with None value checking\\n if ((observedFile.lastChecksum is None\\n and currentChecksum is not None)\\n or observedFile.lastChecksum != currentChecksum):\\n changedFiles.append(observedFile) # notify change\\n observedFile.lastChecksum = currentChecksum # update checksum\\n\\n return changedFiles\",\n \"def tracked_files(self, branch):\\n d = {}\\n ls_tree = self.git_cmd('ls-tree -r --name-only --full-tree %s' %\\n branch)\\n for rpath in ls_tree.splitlines():\\n if rpath == '.gitignore':\\n continue\\n if branch.startswith('timestamps'):\\n d[rpath] = pathlib.PosixPath(self.repodir, rpath)\\n else:\\n if not rpath.startswith(ROOT_SUBDIR):\\n continue\\n d[rpath] = EtcPath(self.repodir, rpath)\\n return d\",\n \"def download_history_for_files(self, folder, globstring, dropbox_location, recursive=True):\\n if recursive:\\n files = self._recursive_file_search(folder, globstring)\\n else:\\n files = glob(folder + globstring)\\n\\n #print(files)\\n for f in files:\\n #print(f)\\n dropboxpath = os.path.relpath(f, dropbox_location).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n dropboxpath = '/' + (dropboxpath)\\n self.download_revisions(dropboxpath)\",\n \"def get_update_file_list(directory):\\n update_files_list = set(UPDATE_FILES_STATIC)\\n update_files_exclude = set(UPDATE_FILES_EXCLUDE)\\n\\n for root, dirs, files in os.walk(path.join(PATH_ROOT, directory)):\\n for filen in files:\\n if UPDATE_FILES_RE.match(filen):\\n filep = path.join(root, filen)\\n update_files_list.add(path.relpath(filep, PATH_ROOT))\\n \\n return update_files_list - update_files_exclude\",\n \"def find_new_files_in(self, msg, branch, create_issue):\\n self._asset_bind(msg)\\n trans = self._translation_util(msg)\\n new_file_list = trans.find_new_files(\\n REPOSITORY_NAME, branch, TARGET_LANG)\\n\\n if create_issue == 0:\\n yield (\\\"\\\\n\\\".join(limit_result(new_file_list)))\\n else:\\n yield (\\\"Processing....\\\")\\n new_count, skip_count = build_new_issue(trans, branch, new_file_list)\\n yield (\\\"{} Issues had been created. {} Issues had been skipped.\\\".format(\\n new_count, skip_count))\\n yield (\\\"Please cache issues again.\\\")\",\n \"def checkGit(directory):\",\n \"def pull(browser):\\n fetched = 0\\n table = browser.get_template_table()\\n\\n print('Checking all letters for changes...')\\n for letter in table.rows:\\n letter.console_msg()\\n if letter.remote_modified():\\n old_chk = letter.checksum\\n letter.pull()\\n if old_chk is None:\\n letter.console_msg('fetched new letter @ {}'.format(letter.checksum[0:7]))\\n else:\\n letter.console_msg('updated from {} to {}'.format(old_chk[0:7], letter.checksum[0:7]))\\n fetched += 1\\n else:\\n letter.console_msg('no changes')\\n\\n sys.stdout.write('\\\\n')\\n\\n sys.stdout.write(Fore.GREEN + '{} of {} files contained new modifications\\\\n'.format(fetched, len(table.rows)) + Style.RESET_ALL)\\n\\n # status['last_pull_date'] = datetime.now()\\n table.status.save()\",\n \"def git_callback(files): # type: (t.List[t.Tuple[str, str]]) -> None\\n for dirpath, _dirnames, filenames in os.walk(os.path.join(data_context().content.root, '.git')):\\n paths = [os.path.join(dirpath, filename) for filename in filenames]\\n files.extend((path, os.path.relpath(path, data_context().content.root)) for path in paths)\",\n \"def get_relevant_files(self):\\n relevant_files = []\\n\\n if self.tree_cache is None:\\n return relevant_files\\n\\n tree = json.loads(self.tree_cache)\\n if \\\"commit\\\" in tree:\\n commit_data = tree[\\\"commit\\\"]\\n master_commit_files = commit_data[\\\"files\\\"]\\n\\n for patched_files in master_commit_files:\\n relevant_file_path = \\\"./\\\" + patched_files[\\\"path\\\"]\\n relevant_files.append(relevant_file_path)\\n\\n return relevant_files\",\n \"def get_changed_packages(blob_name1, blob_name2, package_list):\\n changed_files = check_output(\\n 'git', 'diff', '--name-only', blob_name1, blob_name2)\\n changed_files = changed_files.split('\\\\n')\\n\\n result = set()\\n for filename in changed_files:\\n file_root = rootname(filename)\\n if file_root in package_list:\\n result.add(file_root)\\n\\n return sorted(result)\",\n \"def get_tracked(wcdir):\\n wcdir = abspath(wcdir)\\n if \\\".svn\\\" in os.listdir(wcdir):\\n svn_ls = Popen([\\\"svn\\\", \\\"ls\\\", \\\"-R\\\", wcdir], stdout=PIPE).stdout.read()\\n return [normpath(l) for l in svn_ls.splitlines()]\\n else:\\n allfiles = []\\n for root, dirs, files in os.walk(wcdir):\\n allfiles += [normpath(join(root, p)[len(wcdir)+1:]) for p in files+dirs]\\n return allfiles\",\n \"def changelog_updated(target_branch):\\n\\n output = subprocess.getoutput(['git diff HEAD origin/{}'.format(target_branch)])\\n return 'a/changelog.md b/changelog.md' in output.lower()\",\n \"def _on_watch_changes(self, *changes):\\n self.dirty = self._git.is_dirty()\\n if self._watcher:\\n for change in self._watcher.changes:\\n for tracker in self._trackers:\\n tracked_path = Path(self._git.working_dir) / change[\\\"path\\\"]\\n if tracker.path.resolve() == tracked_path.resolve():\\n tracker._on_file_change(None)\\n return [\\n dict(a_path=diff.a_path, b_path=diff.b_path, change_type=diff.change_type)\\n for diff in self._git.index.diff(None)\\n ] + [\\n dict(a_path=None, b_path=ut, change_type=\\\"U\\\")\\n for ut in self._git.untracked_files\\n ]\",\n \"def get_changed_files(path_to_repository, ignore_subrepositories):\\n diff = _get_diff_to_last_commit(path_to_repository, ignore_subrepositories)\\n return [item.b_path for item in diff if item.change_type in _CHANGE_TYPES_CONSIDERED_FOR_PRECOMMIT]\",\n \"def touch_files_dependent_on_changes(kymera_path, dirs, suffixes, changes):\\n for dir in dirs:\\n if dir[0] != '/':\\n # This is a relative path to kymera root\\n dir = kymera_path + dir\\n if not os.path.exists(dir):\\n print \\\"Directory %s included in ALL_SRCDIRS, ALL_INCDIRS or CFG_LIBS doesn't exist, continuing...\\\" % dir\\n else:\\n for file_name in os.listdir(dir):\\n full_file_path= os.path.join(dir, file_name)\\n # Filter a list of filenames down to those with one of the given suffixes\\\"\\n if matching_file(suffixes, full_file_path):\\n # Find all the files from a set with one of a list of suffices\\n # containing one of the changed definitions\\n if grep_words(changes, full_file_path):\\n print \\\"Mark file for rebuild:\\\", full_file_path\\n touch_file(full_file_path)\",\n \"def file_changes(self):\\n new = []\\n changed = []\\n deleted = []\\n parent = self.parent_tag\\n # Loop through the files and find the ones that have changed\\n for relative_path, file_dict in self.checksum[\\\"files\\\"].items():\\n if relative_path not in parent[\\\"files\\\"]:\\n new.append(relative_path)\\n elif file_dict[\\\"checksum\\\"] != parent[\\\"files\\\"][relative_path][\\\"checksum\\\"]:\\n changed.append(relative_path)\\n # Loop through the parent files and see which files have been deleted\\n for relative_path in parent[\\\"files\\\"].keys():\\n if relative_path not in self.checksum[\\\"files\\\"]:\\n deleted.append(relative_path)\\n return {\\\"new\\\": new, \\\"changed\\\": changed, \\\"deleted\\\": deleted}\",\n \"def get_list_of_comitted_files():\\n files = []\\n output = []\\n try:\\n output = subprocess.check_output(['git','diff-index', '--name-status', '--cached','HEAD']\\n ).decode(\\\"utf-8\\\")\\n except subprocess.CalledProcessError:\\n print(\\\"Error diff files get: trace %s\\\" % subprocess.CalledProcessError)\\n return files\\n\\n for result in output.split(\\\"\\\\n\\\"):\\n logging.info(result)\\n if result != '':\\n match = modified.match(result)\\n if match:\\n files.append(match.group('name'))\\n\\n return files\",\n \"def get_changed_files_and_content(path_to_repository, file_encoding, ignore_subrepositories):\\n changed_files = filter_changed_files(get_changed_files(path_to_repository, ignore_subrepositories),\\n path_to_repository, file_encoding)\\n return {filename: open(os.path.join(path_to_repository, filename), encoding=file_encoding).read() for filename in\\n changed_files}\",\n \"def get_modified_files(repo, args):\\n commit = repo.commit(args.commit)\\n return commit.stats.files\",\n \"def calc_lang_features(commits, author):\\n\\tlang_features = ['/\\\\*\\\\*', '\\\\\\\\\\\"\\\\\\\\\\\"\\\\\\\\\\\"', '///', # documentation\\n\\t\\t\\t'^\\\\s*@', 'def.+:.+->', 'using\\\\s+System\\\\.ComponentModel\\\\.DataAnnotations', # assertion\\n\\t\\t\\t'assert', 'TODO', 'lambda']\\n\\n\\t# delete contents\\n\\topen('lang_features.csv', 'w').close()\\n\\t\\n\\tfor count, commit in enumerate(commits):\\n\\t\\t# status update\\n\\t\\tif (count + 1) % 5 == 0:\\n\\t\\t\\tprint commit, '.. ..', count + 1, ' / ', len(commits)\\n\\n\\n\\t\\t\\t# for each blob modified\\n\\t\\tquery = (\\\"for x in $(echo \\\" + commit + \\\" | ssh da4 ~/lookup/cmputeDiff2.perl); do \\\" +\\n\\t\\t\\t\\t# get the chold and parent blob\\n\\t\\t\\t\\t\\\"diff_blobs=$(echo $x | awk -v RS=';' 1 | sed -n '3,4 p');\\\" +\\n\\t\\t\\t\\t# if a parent blob does not exist, the author authored all of the content of the file\\n\\t\\t\\t\\t\\\"if [ $(echo $diff_blobs|wc -w) -eq 1 ]; then \\\" +\\n\\t\\t\\t\\t\\t\\\"echo $diff_blobs | ~/lookup/showCnt blob 2> /dev/null; \\\" +\\n\\t\\t\\t\\t# if a parent blob exists, find the diff, in order to search only the modified lines\\n\\t\\t\\t\\t\\\"elif [ $(echo $diff_blobs|wc -w) -eq 2 ]; then \\\" +\\n\\t\\t\\t\\t\\t\\\"vars=( $diff_blobs );\\\" +\\n\\t\\t\\t\\t\\t# using bash instead of sh in order to use the process substitution,\\n\\t\\t\\t\\t\\t# to get the modified lines\\n\\t\\t\\t\\t\\t\\\"/bin/bash -c \\\\\\\"diff <(echo ${vars[0]} | ~/lookup/showCnt blob)\\\" +\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\" <(echo ${vars[1]} | ~/lookup/showCnt blob)\\\\\\\";\\\" +\\n\\t\\t\\t\\t\\\"fi;\\\" +\\n\\t\\t\\t# grep the above practices and discard the lines that were deleted from the parent blob\\n\\t\\t\\t# (they start with \\\">\\\" in diff)\\n\\t\\t\\t\\\"done | egrep \\\\\\\"\\\" + \\\"|\\\".join(lang_features) + \\\"\\\\\\\" | grep -v '^>' | wc -l \\\")\\n\\t\\tcount_uses = int(bash(query).strip())\\n\\t\\tif count_uses > 0: # good practice feature is used\\n\\t\\t\\tout = bash('echo ' + commit + ' | ~/lookup/getValues c2P')\\n\\t\\t\\tmain_proj = out.strip().split(';')[1]\\n\\t\\t\\ttime = search(commit, 'commit')[2]\\n\\n\\t\\t\\tf = open(\\\"lang_features.csv\\\", \\\"a\\\")\\n\\t\\t\\tprint 'lang_f'\\n\\t\\t\\tf.write(author + ', ' + 'LANG_F' + ', ' + str(time) + ', ' + main_proj + ', ' + str(count_uses) + '\\\\n')\\n\\t\\t\\tf.close()\\n\\t\\t\\tprint 'wrote: -->', commit\",\n \"def find(self, file, version):\\n matches = []\\n for root, dirnames, filenames in os.walk(self.full_doc_path(version)):\\n for filename in fnmatch.filter(filenames, file):\\n matches.append(os.path.join(root, filename))\\n return matches\",\n \"def get_changed_files_from(old_commit_sha, new_commit_sha):\\n return check_output(\\n \\\"git diff-tree --no-commit-id --name-only -r {0}..{1}\\\".format(\\n old_commit_sha,\\n new_commit_sha\\n ).split(\\\" \\\")\\n ).decode('utf-8').strip()\",\n \"def find_langs(args):\\n infile = args.source\\n langs = {}\\n for line in infile:\\n name_and_files = line.split()\\n name = name_and_files[0]\\n if name not in langs:\\n langs[name] = []\\n langs[name] += read_files.filter_files(name_and_files[1:])\\n langs[args.unknown] += read_files.filter_files(args.classify)\\n return langs\",\n \"def find_cache(hw, r):\\n\\n cmd = \\\"find . -name CMakeCache.txt\\\"\\n p = Popen(cmd, shell=True, stdout=PIPE)\\n out = p.stdout.readlines()\\n p.stdout.close()\\n p.wait()\\n \\n # Transform the output into something readable.\\n for i in out:\\n found = i.split(':')\\n \\n # Scrub the path name\\n path = os.path.dirname(found[0])[2:]\\n if path == \\\"__build__\\\":\\n continue\\n r.note(\\\"found build files in '{0}'\\\".format(path))\",\n \"def do_pull_file(dbsync, bibkey):\\n pass\",\n \"def find_build(branch, tag, build_path=None, old_build_path=None):\\n for directory_format in [build_path, old_build_path]:\\n if directory_format is None:\\n continue\\n loc = directory_format % (branch, tag)\\n if isdir(loc):\\n return loc\\n raise BuildNotFound(branch, tag)\",\n \"def all_changed_files(self):\\n return [path_to_file_type(os.path.join(self.path, p)) for p in self.changed_paths() if p]\",\n \"def svn_fs_paths_changed(*args):\\r\\n return _fs.svn_fs_paths_changed(*args)\",\n \"def retrieve_modified_files(self):\\n result = [(diff_obj.a_path, diff_obj.b_path)\\n for diff_obj in self.repo.index.diff(None)]\\n\\n return result\",\n \"def collect():\\n\\n # Get database.\\n with open(local_directory(path='file_diffs/packages.json'), 'r') as f:\\n store = json.load(f)\\n\\n # UI.\\n print('Checking files for differences...\\\\n')\\n\\n # Iterate database.\\n for package_name in store:\\n # Package variables.\\n package_dir = os.path.join(package_directory, package_name)\\n package = store[package_name]\\n\\n # Recursive (lazy) package searching.\\n if type(package) == str:\\n package = os.path.expanduser(package)\\n for dirpath, dirnames, filenames in os.walk(package):\\n for filename in filenames:\\n sub_package_dir = package_dir + dirpath.replace(package, '')\\n if not os.path.exists(sub_package_dir):\\n os.makedirs(sub_package_dir)\\n\\n fp_local = os.path.join(dirpath, filename)\\n fp_remote = os.path.join(sub_package_dir, filename)\\n\\n cs_local = file_checksum(fp=fp_local)\\n cs_remote = file_checksum(fp=fp_remote)\\n\\n if cs_remote != cs_local:\\n print('Found: {}/{}'.format(package_name, filename))\\n shutil.copyfile(src=fp_local, dst=fp_remote)\\n\\n # Manual package searching.\\n if type(package) == list:\\n for fp in package:\\n fn_local = fp['local']\\n fn_remote = fp['remote']\\n\\n fp_local = os.path.expanduser(fn_local)\\n fp_remote = os.path.join(package_dir, fn_remote)\\n\\n cs_local = file_checksum(fp=fp_local)\\n cs_remote = file_checksum(fp=fp_remote)\\n\\n if cs_remote != cs_local:\\n print('Found: {}/{}'.format(package_name, fn_remote))\\n\\n remote_dir_path = '/'.join(fp_remote.split('/')[:-1])\\n if not os.path.exists(remote_dir_path):\\n os.makedirs(remote_dir_path)\\n shutil.copyfile(src=fp_local, dst=fp_remote)\",\n \"def main(self, *directories):\\n if not self.git and len(directories) == 0:\\n print (\\\"ERROR: At least one directory must be provided (or the \\\"\\n \\\"--git-precommit flag must be passed.\\\\n\\\")\\n self.help()\\n return\\n\\n if len(directories) > 0:\\n find = local['find']\\n files = []\\n for directory in directories:\\n real = os.path.expanduser(directory)\\n if not os.path.exists(real):\\n raise ValueError(\\\"{0} does not exist\\\".format(directory))\\n files.extend(find(real, '-not', '-name', '._*', '-name', '*.py').strip().split('\\\\n'))\\n else:\\n status = local['git']('status', '--porcelain', '-uno')\\n root = local['git']('rev-parse', '--show-toplevel').strip()\\n\\n # get all modified or added python files\\n modified = re.findall(r\\\"^\\\\s[AM]\\\\s+(\\\\S+\\\\.py)$\\\", status, re.MULTILINE)\\n\\n # now just get the path part, which all should be relative to the\\n # root\\n files = [os.path.join(root, line.split(' ', 1)[-1].strip())\\n for line in modified]\\n\\n if len(files) > 0:\\n print \\\"Linting {0} python files.\\\\n\\\".format(len(files))\\n lint(files)\\n else:\\n print \\\"No python files found to lint.\\\\n\\\"\",\n \"def findRepositoryByBackTracking():\\n \\n cLookBack = '.'\\n while(True):\\n cDir = os.path.abspath(cLookBack)\\n print(\\\"Searching in %s\\\" % cDir)\\n if os.path.isdir( os.path.join(cDir, DB_SUBFOLDER) ):\\n return cDir\\n else:\\n if os.path.abspath(cLookBack) == os.path.abspath(cLookBack + '/..'):\\n return os.path.abspath('.')\\n cLookBack = cLookBack + '/..'\\n \\n return cDir\",\n \"def get_changed_files(self, old_commit, new_commit):\\n if old_commit is not None and not self.pygit.descendant_of(\\n new_commit, old_commit\\n ):\\n raise ValueError(\\\"Second commit must be a descendant of first commit\\\")\\n\\n old_index = pygit2.Index()\\n new_index = pygit2.Index()\\n if old_commit is not None:\\n old_tree = self.pygit.get(old_commit).tree\\n old_index.read_tree(old_tree)\\n else:\\n # This is a special hash that represents an empty tree\\n old_tree = self.pygit.get(\\\"4b825dc642cb6eb9a060e54bf8d69288fbee4904\\\")\\n\\n new_tree = self.pygit.get(new_commit).tree\\n new_index.read_tree(new_tree)\\n\\n for patch in self.pygit.diff(old_tree, new_tree):\\n if patch.delta.status_char() != \\\"M\\\":\\n continue\\n\\n if not patch.delta.new_file.path.startswith(\\\"locales/\\\"):\\n continue\\n\\n old_file_oid = old_index[patch.delta.old_file.path].oid\\n new_file_oid = new_index[patch.delta.new_file.path].oid\\n old_file = self.pygit.get(old_file_oid)\\n new_file = self.pygit.get(new_file_oid)\\n yield patch.delta.new_file.path, old_file.data, new_file.data\",\n \"def filter_changed_files(changed_files, path_to_repository, file_encoding):\\n filtered_files = []\\n for changed_file in changed_files:\\n file_is_valid = True\\n if (os.path.isdir(os.path.join(path_to_repository, changed_file))):\\n #ignore directories (e.g., git submodule folders)\\n continue\\n if os.path.getsize(os.path.join(path_to_repository, changed_file)) > 1 * 1024 * 1024:\\n print('File too large for precommit analysis. Ignoring: %s' % changed_file)\\n file_is_valid = False\\n\\n try:\\n with open(os.path.join(path_to_repository, changed_file), encoding=file_encoding) as file:\\n file.read()\\n except UnicodeDecodeError:\\n encoding_string = file_encoding\\n if encoding_string is None:\\n encoding_string = locale.getpreferredencoding() + ' (system encoding)'\\n\\n print(\\n 'File at %s is not encoded in %s. Try using the --file-encoding option.' % (\\n changed_file, encoding_string))\\n file_is_valid = False\\n\\n if file_is_valid:\\n filtered_files.append(changed_file)\\n\\n return filtered_files\",\n \"def ListChangedFiles(self, request, global_params=None):\\n config = self.GetMethodConfig('ListChangedFiles')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def find_files(directory, extension, magictext):\\n global files_logged\\n global found_text\\n dir_path = os.path.abspath(directory)\\n dir_files = os.listdir(dir_path)\\n for file in dir_files:\\n if file.endswith(extension) and file not in files_logged:\\n logger.info('New file found: {}'.format(file))\\n files_logged.append(file)\\n if file.endswith(extension):\\n file_path = os.path.join(dir_path, file)\\n if find_string_in_files(file_path, magictext):\\n break\\n for file in files_logged:\\n if file not in dir_files:\\n logger.info('File deleted: {}'.format(file))\\n files_logged.remove(file)\\n found_text[file] = 0\",\n \"def list_all_files(self, branch_name) -> dict:\\n try:\\n files_response = self.repo.get_contents(self.repo_folder,\\n ref=branch_name)\\n files_list = []\\n for file in files_response:\\n files_list.append(file.raw_data.get('path'))\\n return make_success_response(200, files_list)\\n except GithubException as github_exc:\\n return make_error_response(github_exc.status, github_exc.data)\",\n \"def _submodule_pull_ff(path, branch):\\n slab_logger.log(15, 'Fast forward pull of all ccs-data submodules')\\n # Note: Branch defaults to master in the click application\\n # TODO: Do more error checking here --> after debugging, definitely\\n # TODO: checkout a branch ifexists in origin only--> not replacing git\\n # or setup a tracking branch if there's nothing local or fail.\\n path_to_reporoot = os.path.split(path)\\n path_to_reporoot = os.path.split(path_to_reporoot[0])\\n path_to_reporoot = path_to_reporoot[0]\\n returncode, myinfo = run_this('git submodule foreach git pull --ff-only origin %s' %\\n (branch), path_to_reporoot)\\n return(returncode, myinfo)\",\n \"def git_get_modified_files(\\n paths: Iterable[Path], revrange: RevisionRange, cwd: Path\\n) -> Set[Path]:\\n relative_paths = {p.resolve().relative_to(cwd) for p in paths}\\n str_paths = [path.as_posix() for path in relative_paths]\\n if revrange.use_common_ancestor:\\n rev2 = \\\"HEAD\\\" if revrange.rev2 == WORKTREE else revrange.rev2\\n merge_base_cmd = [\\\"merge-base\\\", revrange.rev1, rev2]\\n rev1 = _git_check_output_lines(merge_base_cmd, cwd)[0]\\n else:\\n rev1 = revrange.rev1\\n diff_cmd = [\\n \\\"diff\\\",\\n \\\"--name-only\\\",\\n \\\"--relative\\\",\\n rev1,\\n # revrange.rev2 is inserted here if not WORKTREE\\n \\\"--\\\",\\n *str_paths,\\n ]\\n if revrange.rev2 != WORKTREE:\\n diff_cmd.insert(diff_cmd.index(\\\"--\\\"), revrange.rev2)\\n lines = _git_check_output_lines(diff_cmd, cwd)\\n if revrange.rev2 == WORKTREE:\\n ls_files_cmd = [\\n \\\"ls-files\\\",\\n \\\"--others\\\",\\n \\\"--exclude-standard\\\",\\n \\\"--\\\",\\n *str_paths,\\n ]\\n lines.extend(_git_check_output_lines(ls_files_cmd, cwd))\\n changed_paths = (Path(line) for line in lines)\\n return {path for path in changed_paths if should_reformat_file(cwd / path)}\",\n \"def update_date(dest=dest):\\n for root, _, files in os.walk(dest):\\n ignore = [\\\"README.md\\\",\\\"SUMMARY.md\\\"]\\n _ = [edit_files(root + \\\"/\\\" + file) for file in files if (file not in ignore and file.endswith(\\\".md\\\"))]\",\n \"def FindCheckerFiles(path):\\n if not path:\\n Logger.fail(\\\"No source path provided\\\")\\n elif os.path.isfile(path):\\n return [ path ]\\n elif os.path.isdir(path):\\n foundFiles = []\\n for root, dirs, files in os.walk(path):\\n for file in files:\\n extension = os.path.splitext(file)[1]\\n if extension in [\\\".java\\\", \\\".smali\\\"]:\\n foundFiles.append(os.path.join(root, file))\\n return foundFiles\\n else:\\n Logger.fail(\\\"Source path \\\\\\\"\\\" + path + \\\"\\\\\\\" not found\\\")\",\n \"def _git_diff_files(ref=\\\"master\\\"):\\n result = []\\n command = [\\\"git\\\", \\\"diff\\\", \\\"--name-status\\\", \\\"%s\\\" % (ref)]\\n exit_code, output = _execute(command)\\n if exit_code != 0:\\n print(\\\"Failed to diff files.\\\")\\n sys.exit(1)\\n\\n for line in output.decode(\\\"utf-8\\\").splitlines():\\n parts = line.split(\\\"\\\\t\\\")\\n action = parts[0]\\n name = parts[-1]\\n action = action.lower()\\n result.append((action, name))\\n\\n return result\",\n \"def svn_fs_paths_changed2(*args):\\r\\n return _fs.svn_fs_paths_changed2(*args)\",\n \"def get_files_from_git() -> Sequence[Path]:\\n\\n def get_files(cmd: str) -> Sequence[str]:\\n output = subprocess.check_output(cmd, shell=True)\\n return [os.fsdecode(x) for x in output.splitlines()]\\n\\n root = os.fsdecode(subprocess.check_output(\\\"git rev-parse --show-toplevel\\\", shell=True).strip())\\n result: Set[str] = set()\\n result.update(get_files(\\\"git diff --name-only --diff-filter=ACM --staged\\\"))\\n result.update(get_files(\\\"git diff --name-only --diff-filter=ACM\\\"))\\n result.update(get_files(\\\"git ls-files -o --full-name --exclude-standard\\\"))\\n return sorted(Path(root, x) for x in result)\",\n \"def files_changed(revish: Text,\\n ignore_rules: Optional[Sequence[Text]] = None,\\n include_uncommitted: bool = False,\\n include_new: bool = False\\n ) -> Tuple[List[Text], List[Text]]:\\n files = repo_files_changed(revish,\\n include_uncommitted=include_uncommitted,\\n include_new=include_new)\\n if not files:\\n return [], []\\n\\n return exclude_ignored(files, ignore_rules)\",\n \"def changed_files(conn, scan0, scan1):\\n c = conn.cursor()\\n c.execute(\\\"SELECT a.pathid as pathid, a.hashid, b.hashid FROM \\\" +\\n \\\"( SELECT pathid, hashid, scanid FROM files WHERE scanid=?) AS 'a' \\\" +\\n \\\"JOIN (SELECT pathid, hashid, scanid FROM FILES WHERE scanid=?) as 'b' \\\" +\\n \\\"ON a.pathid=b.pathid WHERE a.hashid != b.hashid\\\",\\n (scan0, scan1))\\n return (DBFile(f) for f in c)\",\n \"def filesearch(word=\\\"\\\"):\\n logger.info('Starting filesearch')\\n file = []\\n for f in glob.glob(\\\"*\\\"):\\n if word[0] == \\\".\\\":\\n if f.endswith(word):\\n file.append(f)\\n\\n elif word in f:\\n file.append(f)\\n #return file\\n logger.debug(file)\\n return file\",\n \"def update_files(self):\\n try:\\n db_files = self.dbc.get_file_list(self.remote_directory)\\n except rest.ErrorResponse as e:\\n print str(datetime.datetime.now()) \\\\\\n + \\\": Could not get remote file list.\\\"\\n print e.reason\\n return False\\n new_files = set(db_files) - self.file_set\\n old_files = self.file_set - set(db_files)\\n if new_files != set() or old_files != set():\\n self.file_set = set(db_files)\\n for filename in new_files:\\n try:\\n self.dbc.get_file(filename)\\n except rest.ErrorResponse as e:\\n print str(datetime.datetime.now()) + e.reason\\n for filename in old_files:\\n try:\\n os.remove(self.local_directory + \\\"/\\\" + filename)\\n except OSError:\\n pass\\n print str(datetime.datetime.now()) + \\\": Fileset changed:\\\"\\n print self.file_set\\n email_changes(new_files, old_files)\\n print str(datetime.datetime.now()) \\\\\\n + \\\": Email sent from update_files().\\\"\\n return True\\n return False\",\n \"def test_case_6():\\n print(\\\"*********Test_case_6***********\\\")\\n path = os.path.join(os.path.dirname(__file__), 'testdir', 't1.c')\\n result = find_files('.c', path)\\n print(result)\",\n \"def search_file(repos, path_parts, rev, search_re):\\n\\n # Read in each line of a checked-out file, and then use re.search to\\n # search line.\\n fp = repos.openfile(path_parts, rev, {})[0]\\n matches = 0\\n while 1:\\n line = fp.readline()\\n if not line:\\n break\\n # FIXME: Is there what can we do about file encoding?\\n if search_re.search(line.decode(\\\"utf-8\\\", \\\"surrogateescape\\\")):\\n matches = 1\\n fp.close()\\n break\\n return matches\",\n \"def refresh():\\n git.fetch()\\n output = str(git.merge('--ff-only')).strip()\\n if output != 'Already up to date.':\\n print(output)\\n git.fetch('--tags')\",\n \"def validate(cfg: defs.Config) -> List[str]: # noqa: C901\\n res: List[str] = []\\n\\n def check_branch(\\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\\n ) -> None:\\n \\\"\\\"\\\"Validate versions within a single branch.\\\"\\\"\\\"\\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\\n\\n if not RE_BRANCH_NAME.match(branch_name):\\n res.append(f\\\"{comp_name}: Invalid branch name: {branch_name}\\\")\\n\\n for ver, version in sorted(branch.items()):\\n if not RE_VERSION_STRING.match(ver):\\n res.append(f\\\"{comp_name}/{branch_name}: Invalid version string: {ver}\\\")\\n\\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\\n if version.outdated:\\n update_to = [\\n o_version\\n for o_version in branch.values()\\n if not o_version.outdated\\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\\n == other_cksums\\n ]\\n if len(update_to) != 1:\\n res.append(\\n f\\\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \\\"\\n f\\\"versions to update to instead of exactly one\\\"\\n )\\n else:\\n bad_files = sorted(\\n relpath\\n for relpath, (path, fdata) in driver_cksums.items()\\n if util.file_sha256sum(path) != fdata.sha256\\n )\\n if bad_files:\\n res.append(f\\\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\\\")\\n\\n if not uptodate_files:\\n uptodate_files = driver_cksums\\n elif uptodate_files != driver_cksums:\\n res.append(\\n f\\\"{comp_name}/{branch_name}: All the up-to-date versions should \\\"\\n f\\\"define the same set of files with the same checksums\\\"\\n )\\n\\n if not any(not version.outdated for version in branch.values()):\\n res.append(f\\\"{comp_name}/{branch_name}: No non-outdated versions\\\")\\n\\n def check_component(comp_name: str, comp: defs.Component) -> None:\\n \\\"\\\"\\\"Validate the definition of a single component.\\\"\\\"\\\"\\n if not RE_COMP_NAME.match(comp_name):\\n res.append(f\\\"Invalid component name: {comp_name}\\\")\\n\\n for branch_name, branch in sorted(comp.branches.items()):\\n check_branch(comp_name, branch_name, branch)\\n\\n for comp_name, comp in sorted(cfg.all_components.components.items()):\\n check_component(comp_name, comp)\\n\\n return res\",\n \"def search_for_files(\\n run_name: str,\\n raw_extension: Optional[str] = None,\\n cellpy_file_extension: Optional[str] = None,\\n raw_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n project_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n cellpy_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n prm_filename: Union[pathlib.Path, str, None] = None,\\n file_name_format: Optional[str] = None,\\n reg_exp: Optional[str] = None,\\n sub_folders: Optional[bool] = True,\\n file_list: Optional[List[str]] = None,\\n with_prefix: Optional[bool] = True,\\n pre_path: Union[OtherPath, pathlib.Path, str, None] = None,\\n) -> Tuple[List[str], str]:\\n\\n # TODO: @jepe - use reg_exp\\n # TODO: @jepe - allow for searching in cloud\\n # TODO: @jepe - how to implement searching in db?\\n # TODO: update prms and conf file to allow for setting if search should be done in\\n # sub-folders, several folders, db, cloud etc\\n # TODO: @jepe - find a way to implement automatic file_list creation in a top level func.\\n logging.debug(f\\\"searching for {run_name}\\\")\\n version = 0.4\\n t0 = time.time()\\n\\n if reg_exp is None:\\n reg_exp = prms.FileNames.reg_exp\\n\\n if raw_extension is None:\\n raw_extension = prms.FileNames.raw_extension\\n\\n if raw_extension is None:\\n raw_extension = prms.FileNames.raw_extension\\n\\n if cellpy_file_extension is None:\\n cellpy_file_extension = prms.FileNames.cellpy_file_extension\\n\\n # backward compatibility check:\\n if cellpy_file_extension.startswith(\\\".\\\"):\\n warnings.warn(\\n \\\"Deprecation warning: cellpy_file_extension should not include the '.'\\\"\\n )\\n cellpy_file_extension = cellpy_file_extension[1:]\\n\\n if raw_file_dir is None:\\n raw_file_dir = prms.Paths.rawdatadir\\n\\n if file_name_format is None:\\n file_name_format = prms.FileNames.file_name_format\\n\\n # 'dressing' the raw_file_dir in a list in case we want to\\n # search in several folders (not implemented yet):\\n if not isinstance(raw_file_dir, (list, tuple)):\\n raw_file_dir = [OtherPath(raw_file_dir)]\\n else:\\n raw_file_dir = [OtherPath(d) for d in raw_file_dir]\\n\\n if project_dir is not None:\\n raw_file_dir = [r / project_dir for r in raw_file_dir]\\n\\n if reg_exp:\\n logging.warning(f\\\"Got reg_exp: {reg_exp}\\\")\\n logging.warning(\\\"Sorry, but using reg exp is not implemented yet.\\\")\\n\\n if prm_filename is not None:\\n logging.debug(\\\"Sorry, reading prm file is not implemented yet.\\\")\\n\\n if cellpy_file_dir is None:\\n cellpy_file_dir = OtherPath(prms.Paths.cellpydatadir)\\n else:\\n cellpy_file_dir = OtherPath(cellpy_file_dir)\\n\\n if file_name_format is None and reg_exp is None:\\n try:\\n # To be implemented in version 0.5:\\n file_name_format = prms.FileNames.file_name_format\\n except AttributeError:\\n file_name_format = \\\"YYYYMMDD_[name]EEE_CC_TT_RR\\\"\\n\\n if file_name_format.upper() == \\\"YYYYMMDD_[NAME]EEE_CC_TT_RR\\\":\\n # backward compatibility check\\n if raw_extension.startswith(\\\".\\\"):\\n warnings.warn(\\n \\\"Deprecation warning: raw_extension should not include the '.'\\\"\\n )\\n raw_extension = raw_extension[1:]\\n warnings.warn(f\\\"Removing it -> {raw_extension}\\\")\\n # TODO: give warning/error-message if run_name contains more than one file (due to duplicate in db)\\n glob_text_raw = f\\\"{run_name}*.{raw_extension}\\\"\\n else:\\n glob_text_raw = file_name_format\\n\\n logging.debug(f\\\"searching for raw files in: {raw_file_dir}\\\")\\n logging.debug(f\\\"searching for {run_name}\\\")\\n logging.debug(f\\\"using glob {glob_text_raw}\\\")\\n\\n cellpy_file = f\\\"{run_name}.{cellpy_file_extension}\\\"\\n cellpy_file = cellpy_file_dir / cellpy_file\\n # Not sure if for example pandas can handle OtherPath objects:\\n cellpy_file = cellpy_file.full_path\\n\\n logging.debug(f\\\"generated cellpy filename {cellpy_file}\\\")\\n\\n if file_list is not None:\\n if len(raw_file_dir) > 1:\\n logging.info(\\\"you provided several raw file directories\\\")\\n logging.debug(\\\"searching within provided list of files\\\")\\n\\n if with_prefix:\\n glob_text_raw_for_list = f\\\"*{glob_text_raw}\\\"\\n else:\\n glob_text_raw_for_list = glob_text_raw\\n\\n run_files = fnmatch.filter(file_list, glob_text_raw_for_list)\\n\\n if pre_path is not None:\\n pre_path = OtherPath(pre_path)\\n run_files = list(map(lambda x: pre_path / x, run_files))\\n\\n else:\\n run_files = []\\n for d in raw_file_dir:\\n if d.is_external:\\n logging.debug(\\\"external file\\\")\\n if not d.is_dir():\\n warnings.warn(\\\"your raw file directory cannot be accessed!\\\")\\n # raise cellpy.exceptions.IOError(\\\"your raw file directory cannot be accessed!\\\")\\n _run_files = []\\n else:\\n logging.debug(f\\\"checking in folder {d}\\\")\\n logging.debug(f\\\"{sub_folders=}\\\")\\n if sub_folders:\\n _run_files = d.rglob(glob_text_raw)\\n\\n else:\\n _run_files = d.glob(glob_text_raw)\\n\\n _run_files = [str(_f.resolve()) for _f in _run_files]\\n _run_files.sort()\\n run_files.extend(_run_files)\\n\\n return run_files, cellpy_file\",\n \"def check_for_translation_updates_with_repo_and_legalcodes(\\n self, repo: git.Repo, legalcodes: Iterable[\\\"licenses.models.LegalCode\\\"]\\n ):\\n self.say(3, \\\"check if repo is dirty\\\")\\n if repo.is_dirty():\\n raise Exception(\\n f\\\"Git repo at {settings.TRANSLATION_REPOSITORY_DIRECTORY} is\\\"\\n \\\" dirty. We cannot continue.\\\"\\n )\\n self.say(2, \\\"Fetch to update repo\\\")\\n repo.remotes.origin.fetch()\\n resource_slugs_on_transifex = self.stats.keys()\\n\\n # We only have the BY* 4.0 licenses in our database so far.\\n # We'd like to process one potential translation branch at a time.\\n # For the BY* 4.0 licenses, there's a single translation branch for\\n # each language. So identify all the languages and iterate over those.\\n # (Except English)\\n\\n # Gather the files we need to update in git.\\n # This is a dict with keys = branch names, and values dictionaries\\n # mapping relative paths of files to update, to their contents\\n # (bytes).\\n self.branches_to_update = defaultdict(_empty_branch_object)\\n self.legalcodes_to_update = []\\n self.branch_objects_to_update = []\\n\\n legalcodes_with_updated_translations = []\\n\\n for legalcode in legalcodes:\\n language_code = legalcode.language_code\\n resource_slug = legalcode.license.resource_slug\\n if resource_slug not in resource_slugs_on_transifex:\\n self.say(\\n 2, f\\\"Transifex has no resource {resource_slug}. Creating.\\\"\\n )\\n\\n # Create the resource\\n english_pofile = legalcode.get_english_pofile()\\n pofile_content = get_pofile_content(english_pofile)\\n self.create_resource(\\n resource_slug=resource_slug,\\n resource_name=legalcode.license.fat_code(),\\n pofilename=os.path.basename(\\n legalcode.translation_filename()\\n ),\\n pofile_content=pofile_content,\\n )\\n self.clear_transifex_stats()\\n\\n if language_code not in self.stats[resource_slug]:\\n self.say(\\n 2,\\n f\\\"Transifex has no {language_code} translation for\\\"\\n f\\\" {resource_slug}\\\",\\n ) # pragma: no cover\\n\\n # Upload the language\\n self.upload_messages_to_transifex(legalcode)\\n self.clear_transifex_stats()\\n\\n # We have a translation in this language for this license on\\n # Transifex.\\n # When was it last updated?\\n last_activity = self.stats[resource_slug][language_code][\\n \\\"translated\\\"\\n ][\\\"last_activity\\\"]\\n last_tx_update = (\\n iso8601.parse_date(last_activity) if last_activity else None\\n )\\n\\n if legalcode.translation_last_update is None:\\n # First time: initialize, don't create branch\\n legalcode.translation_last_update = last_tx_update\\n legalcode.save()\\n self.say(2, f\\\"Initialized last update time for {legalcode}\\\")\\n continue\\n\\n if last_tx_update <= legalcode.translation_last_update:\\n # No change\\n self.say(3, f\\\"No changes for {legalcode}\\\")\\n continue\\n\\n # Translation has changed!\\n self.say(2, f\\\"Translation has changed for {legalcode}\\\")\\n legalcodes_with_updated_translations.append(legalcode)\\n\\n return self.handle_legalcodes_with_updated_translations(\\n repo, legalcodes_with_updated_translations\\n )\",\n \"def get_changed_paths(*args, globs=None):\\n if globs:\\n args = list(args) + [\\\"--\\\", *globs]\\n diff_output = git(\\\"diff\\\", \\\"--name-only\\\", *args)\\n\\n return set([line.strip() for line in diff_output.splitlines()])\",\n \"def check_file_references(name):\\n references = []\\n for root, dirs, files in walk(Path(\\\".\\\")):\\n for file_name in files:\\n if is_notebook(file_name):\\n nb_markdown_cells = markdown_cells(os.path.join(root, file_name))\\n for cell in nb_markdown_cells:\\n for line in cell:\\n if name in line:\\n references.append(file_name)\\n else:\\n with open(os.path.join(root, file_name), encoding=\\\"utf8\\\", errors='ignore') as non_nb_file:\\n if name in non_nb_file.read():\\n references.append(file_name)\\n return references\",\n \"def initialise_languages():\\n is_language_folder = r\\\"^[^\\\\\\\\\\\\.]*\\\" # Cannot have backslash or dot.\\n language_folder_path = os.path.join(definitions.ROOT_DIR, \\\"languages\\\")\\n\\n for root, dirs, files in os.walk(language_folder_path):\\n for name in files:\\n if name.startswith(\\\"_NEW_\\\") or name.startswith(\\\"_CHANGED_\\\"):\\n # Files that are auto-generated will not be added.\\n continue\\n\\n full_dir = os.path.join(root, name)\\n relative_dir = full_dir.replace(language_folder_path + \\\"\\\\\\\\\\\", \\\"\\\")\\n\\n match = re.match(is_language_folder, relative_dir)\\n language_id = match.group(0)\\n\\n language = None\\n if language_id != \\\"cache\\\":\\n language = definitions.LANGUAGES[language_id]\\n else:\\n language = definitions.CACHE_LANGUAGE\\n\\n if relative_dir == \\\"{id}\\\\\\\\commands.json\\\".format(id=language_id):\\n # Take the commands.\\n definitions.COMMANDS.add_command_localisation(full_dir, language_id)\\n\\n elif relative_dir == \\\"{id}\\\\\\\\languages.json\\\".format(id=language_id):\\n # Take the languages.\\n language.add_languages(full_dir)\\n\\n elif relative_dir == \\\"{id}\\\\\\\\meta.json\\\".format(id=language_id):\\n # Take the metadata.\\n language.add_meta(full_dir)\\n\\n elif relative_dir == \\\"{id}\\\\\\\\permissions.json\\\".format(id=language_id):\\n # Take the permissions.\\n language.add_permission_names(full_dir)\\n\\n elif relative_dir == \\\"{id}\\\\\\\\units.json\\\".format(id=language_id):\\n language.add_units(full_dir)\\n\\n else:\\n # Take the keys\\n language.add_keys_from_path(full_dir)\\n\\n for name in dirs:\\n full_dir = os.path.join(root, name)\\n relative_dir = full_dir.replace(language_folder_path + \\\"\\\\\\\\\\\", \\\"\\\")\\n\\n match = re.fullmatch(is_language_folder, relative_dir)\\n if match is not None:\\n if name != \\\"cache\\\":\\n definitions.LANGUAGES[name] = Language(name)\\n else:\\n definitions.CACHE_LANGUAGE = Language(name)\",\n \"def _get_changes_to_be_committed(wit_path, current_id):\\n\\n if current_id != 'None':\\n files = {os.path.relpath(file, os.path.join(wit_path, '.wit', 'images', current_id)): file\\n for file in _get_all_files_names(\\n wit_path, dir_name=os.path.join(wit_path, '.wit', 'images', current_id))}\\n\\n for file in _get_staging_area_files(wit_path, plus_root=True):\\n if os.path.relpath(file, wit_path) in files:\\n yield {os.path.basename(file): _compare_file(os.path.join(wit_path, '.wit', 'staging_area', file),\\n files[os.path.relpath(file, wit_path)])}\\n else:\\n yield ''\",\n \"def _bbcomponentwalk(self):\\n for path, subdirs, files in os.walk(self.oedir()):\\n try:\\n del subdirs[subdirs.index('.svn')]\\n\\n except ValueError:\\n pass\\n \\n for file in files:\\n pf, ext = os.path.splitext(file)\\n if ext == '.bb':\\n yield pf.partition('_')[0]\",\n \"def flake8_scan_file(commit_sha, owner, repo, parent_sha=None):\\n if parent_sha is None:\\n parent_sha = get_commit_parent(commit_sha, owner, repo)\\n diff_url = GIT_COMPARE_URL.format(base=parent_sha,\\n head=commit_sha,\\n owner=owner,\\n repo=repo,\\n host=host_api)\\n diff_info = get(diff_url, auth=auth).json()\\n diff_content = get(diff_url,\\n auth=auth,\\n headers={\\\"Accept\\\": \\\"application/vnd.github.v3.diff\\\"}\\n ).content.decode('utf8')\\n patch_set = PatchSet(diff_content)\\n comments_per_file = {}\\n for file in diff_info['files']:\\n content = get(file['contents_url'], auth=auth).json()\\n file_content = get(content['download_url']).content\\n with open(\\\"flake8_tmp_file.py\\\", 'wb') as test_file:\\n test_file.write(file_content)\\n style_guide = flake8.get_style_guide(ignore=['E24', 'W503'])\\n style_guide.input_file('./flake8_tmp_file.py', )\\n results = style_guide._application.file_checker_manager.checkers[\\n 0].results\\n comments_per_line = {}\\n for code, line_n, offset, text, src in results:\\n if changed_in_diff(get_file_by_name(patch_set,\\n file['filename']), line_n):\\n comments = comments_per_line.get(line_n, [])\\n comments.append((file['filename'], line_n, offset, code, text))\\n comments_per_line[line_n] = comments\\n comments_per_file[file['filename']] = comments_per_line\\n return comments_per_file\",\n \"def get_and_update_versions ():\\n\\n try:\\n get_comp_versions (\\\"ACE\\\")\\n get_comp_versions (\\\"TAO\\\")\\n\\n if opts.update:\\n files = []\\n files += update_version_files (\\\"ACE\\\")\\n files += update_version_files (\\\"TAO\\\")\\n files += create_changelog (\\\"ACE\\\")\\n files += create_changelog (\\\"TAO\\\")\\n files += update_spec_file ()\\n files += update_debianbuild ()\\n\\n commit (files)\\n\\n except:\\n print (\\\"Fatal error in get_and_update_versions.\\\")\\n raise\",\n \"def find_files(\\n base,\\n languages=all_languages,\\n dir_ignore=default_dir_ignore,\\n file_ignore=default_file_ignore,\\n):\\n if base[-1] != \\\"/\\\":\\n base += \\\"/\\\"\\n\\n def update_dirs(dirs):\\n \\\"\\\"\\\"strip the ignored directories out of the provided list\\\"\\\"\\\"\\n index = len(dirs) - 1\\n for i, d in enumerate(reversed(dirs)):\\n if d in dir_ignore:\\n del dirs[index - i]\\n\\n # walk over base\\n for root, dirs, files in os.walk(base):\\n root = root.replace(base, \\\"\\\", 1)\\n\\n # strip ignored directories from the list\\n update_dirs(dirs)\\n\\n for filename in files:\\n if filename in file_ignore:\\n # skip ignored files\\n continue\\n\\n # try to figure out the language of the specified file\\n fullpath = os.path.join(base, root, filename)\\n language = lang_type(fullpath)\\n\\n # if the file is one of the langauges that we want return\\n # its name and the language\\n if language in languages:\\n yield fullpath, language\",\n \"def get_all_changes_for_package(\\n provider_package_id: str,\\n verbose: bool,\\n base_branch: str,\\n force: bool,\\n) -> tuple[bool, list[list[Change]] | Change | None, str]:\\n provider_details = get_provider_details(provider_package_id)\\n current_version = provider_details.versions[0]\\n current_tag_no_suffix = get_version_tag(current_version, provider_package_id)\\n if verbose:\\n console.print(f\\\"Checking if tag '{current_tag_no_suffix}' exist.\\\")\\n if not force and not subprocess.call(\\n get_git_tag_check_command(current_tag_no_suffix),\\n cwd=provider_details.source_provider_package_path,\\n stderr=subprocess.DEVNULL,\\n ):\\n if verbose:\\n console.print(f\\\"The tag {current_tag_no_suffix} exists.\\\")\\n # The tag already exists\\n changes = subprocess.check_output(\\n get_git_log_command(verbose, f\\\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\\\", current_tag_no_suffix),\\n cwd=provider_details.source_provider_package_path,\\n text=True,\\n )\\n if changes:\\n provider_details = get_provider_details(provider_package_id)\\n doc_only_change_file = os.path.join(\\n provider_details.source_provider_package_path, \\\".latest-doc-only-change.txt\\\"\\n )\\n if os.path.exists(doc_only_change_file):\\n with open(doc_only_change_file) as f:\\n last_doc_only_hash = f.read().strip()\\n try:\\n changes_since_last_doc_only_check = subprocess.check_output(\\n get_git_log_command(\\n verbose, f\\\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\\\", last_doc_only_hash\\n ),\\n cwd=provider_details.source_provider_package_path,\\n text=True,\\n )\\n if not changes_since_last_doc_only_check:\\n console.print()\\n console.print(\\n \\\"[yellow]The provider has doc-only changes since the last release. Skipping[/]\\\"\\n )\\n # Returns 66 in case of doc-only changes\\n sys.exit(66)\\n if len(changes) > len(changes_since_last_doc_only_check):\\n # if doc-only was released after previous release - use it as starting point\\n # but if before - stay with the releases from last tag.\\n changes = changes_since_last_doc_only_check\\n except subprocess.CalledProcessError:\\n # ignore when the commit mentioned as last doc-only change is obsolete\\n pass\\n\\n console.print(f\\\"[yellow]The provider {provider_package_id} has changes since last release[/]\\\")\\n console.print()\\n console.print(f\\\"[bright_blue]Provider: {provider_package_id}[/]\\\\n\\\")\\n changes_table, array_of_changes = convert_git_changes_to_table(\\n f\\\"NEXT VERSION AFTER + {provider_details.versions[0]}\\\",\\n changes,\\n base_url=\\\"https://github.com/apache/airflow/commit/\\\",\\n markdown=False,\\n )\\n print_changes_table(changes_table)\\n return False, array_of_changes[0], changes_table\\n else:\\n console.print(f\\\"No changes for {provider_package_id}\\\")\\n return False, None, \\\"\\\"\\n if verbose:\\n console.print(\\\"The tag does not exist. \\\")\\n if len(provider_details.versions) == 1:\\n console.print(\\n f\\\"The provider '{provider_package_id}' has never been released but it is ready to release!\\\\n\\\"\\n )\\n else:\\n console.print(f\\\"New version of the '{provider_package_id}' package is ready to be released!\\\\n\\\")\\n next_version_tag = f\\\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\\\"\\n changes_table = \\\"\\\"\\n current_version = provider_details.versions[0]\\n list_of_list_of_changes: list[list[Change]] = []\\n for version in provider_details.versions[1:]:\\n version_tag = get_version_tag(version, provider_package_id)\\n changes = subprocess.check_output(\\n get_git_log_command(verbose, next_version_tag, version_tag),\\n cwd=provider_details.source_provider_package_path,\\n text=True,\\n )\\n changes_table_for_version, array_of_changes_for_version = convert_git_changes_to_table(\\n current_version, changes, base_url=\\\"https://github.com/apache/airflow/commit/\\\", markdown=False\\n )\\n changes_table += changes_table_for_version\\n list_of_list_of_changes.append(array_of_changes_for_version)\\n next_version_tag = version_tag\\n current_version = version\\n changes = subprocess.check_output(\\n get_git_log_command(verbose, next_version_tag),\\n cwd=provider_details.source_provider_package_path,\\n text=True,\\n )\\n changes_table_for_version, array_of_changes_for_version = convert_git_changes_to_table(\\n current_version, changes, base_url=\\\"https://github.com/apache/airflow/commit/\\\", markdown=False\\n )\\n changes_table += changes_table_for_version\\n if verbose:\\n print_changes_table(changes_table)\\n return True, list_of_list_of_changes or None, changes_table\",\n \"def push(browser, files):\\n table = browser.get_template_table()\\n table.open()\\n\\n if len(files) == 0:\\n modified = []\\n for letter in table.rows:\\n if letter.local_modified():\\n modified.append(letter)\\n\\n if len(modified) == 0:\\n sys.stdout.write(Fore.GREEN + 'No files contained local modifications.' + Style.RESET_ALL + '\\\\n')\\n return\\n\\n sys.stdout.write(Fore.GREEN + 'The following {} file(s) contains local modifications.'.format(len(modified)) + Style.RESET_ALL + '\\\\n')\\n for letter in modified:\\n print(' - {}'.format(letter.filename))\\n files.append(letter.filename)\\n\\n msg = 'Push updates to Alma? '\\n if input(\\\"%s (y/N) \\\" % msg).lower() != 'y':\\n print('Aborting')\\n return\\n\\n for filename in files:\\n letter = table.get_letter(filename)\\n\\n old_chk = letter.checksum\\n if letter.push():\\n letter.console_msg('updated from {} to {}'.format(old_chk[0:7], letter.checksum[0:7]))\\n sys.stdout.write('\\\\n')\",\n \"def changelog(self, branch, since=None):\\n walker = Walker(self.repo, [self.latest_branch_revision(branch)])\\n for entry in walker:\\n if since is not None and entry.commit.id == since:\\n break\\n commit = entry.commit\\n files = Command(\\n 'git show --pretty=\\\"format:\\\" --name-only %s' % commit.id,\\n cwd=self.path,\\n ).out.split()\\n yield Commit(\\n commit.id,\\n commit.committer,\\n datetime.fromtimestamp(commit.commit_time),\\n commit.message,\\n files,\\n )\",\n \"def get_patches_for_repo(patch_dir, repo, branch):\\n # Sometimes patch_dir isn't given!\\n if patch_dir is None:\\n return []\\n patch_path_pattern = os.path.join(patch_dir, branch, repo, '*.patch')\\n return sorted(glob.glob(patch_path_pattern))\",\n \"def up_to_date(db, args, pattern):\\n\\n any_out_of_date = False\\n num_found_files = 0\\n for arg in args:\\n find_data = None\\n try:\\n find_data = eugene_sys.FindFiles(pattern(arg))\\n\\n num_found_files = num_found_files+len(find_data)\\n except ValueError:\\n #print(f\\\"couldn't open directory {arg} or no suitable files found within...\\\")\\n continue\\n\\n for file in find_data:\\n key = file['cFileName']\\n record = file['ftLastWriteTime']\\n\\n if db.compareWithRecord(key, record): continue # Up-to-date\\n\\n db_record = db.readRecord(key)\\n db.writeRecord(key, record)\\n\\n old = eugene_sys.GetDateTimeFormat(db_record) if db_record > 0 else \\\"(null)\\\"\\n new = eugene_sys.GetDateTimeFormat(record)\\n\\n msg = f\\\"`{key}' not up to date\\\"\\n msg += _PAD[len(msg):]\\n\\n print(f\\\"{msg} ({old} -> {new})...\\\")\\n any_out_of_date = True\\n\\n # For the query to return a 'True' result\\n # - No database record can be out-of-date\\n # - At LEAST one file in the directories\\n # given in 'args' must've matched the\\n # pattern\\n result = (num_found_files > 0) and not any_out_of_date\\n\\n if result: print(\\\" ...up to date!\\\")\\n return result\",\n \"def find_new_files(self):\\r\\n in_file_system = self._find_data_files(self.basepath,\\r\\n self.data_file_extension, [])\\r\\n status_msg('Search for data files. Found: {0}'.format(\\r\\n len(in_file_system)), True\\r\\n )\\r\\n status_msg('Check that there are no duplicate filenames', True)\\r\\n in_db = self._get_files_in_db()\\r\\n status_msg('Get list of files in data base. Found: {0}'.format(\\r\\n len(in_db)), True\\r\\n )\\r\\n for filename in in_file_system:\\r\\n # Filename has been added to the database both where a part of the\\r\\n # path was a part of the unique name and where it was not,\\r\\n # therefore we need to check for both AND we ignore filenames that\\r\\n # start with IGNORE\\r\\n if form_unique_name(filename) not in in_db and\\\\\\r\\n os.path.basename(filename) not in in_db and not\\\\\\r\\n os.path.basename(filename).startswith('IGNORE'):\\r\\n self.new_data_files.append(filename)\\r\\n\\r\\n print # Adds a new line to the statusses before parsing\\r\\n status_msg('Collecting new files. Found: {0}'.format(\\r\\n len(self.new_data_files)), True\\r\\n )\",\n \"def fileCheck(path):\\n print('[+] Checking For File patching ')\\n for url in check_files:\\n try:\\n #File Rereive\\n data = query(url)\\n file_name = url.split(\\\"/\\\")[-1]\\n _,tmp_file = tempfile.mkstemp(prefix=\\\"exitmap_%s_\\\" % file_name)\\n with open(tmp_file, \\\"wb\\\") as fd:\\n fd.write(data)\\n for i in check_files_patch_results:\\n if str(i.url) == str(url):\\n if str(i.filehash) != str(sha512_file(tmp_file)):\\n print('[+] ALERT File Patch FOUND !')\\n print(' | exitnode : %s' % str(i.exitnode) )\\n print(' |_________> url: %s' % str(i.url) )\\n print(' |_________> filePath: %s' % str(i.filepath) )\\n print(' |_________> fileHash: %s' % str(i.filehash) )\\n #check_files_patch_results.append( File_Check_Results(url, file_name, tmp_file, path, sha512_file(tmp_file)) )\\n else :\\n print('[+] File (%s) seems to be ok' % i.url)\\n break\\n\\n except Exception as err:\\n print('[-] Error ! %s' % err)\\n traceback.print_exc()\\n pass\\n return time.time()\",\n \"def test_case_3():\\n print(\\\"*********Test_case_3***********\\\")\\n result = find_files('.c', None)\\n print(result)\",\n \"def edited_file_locations(self):\",\n \"def apiFiles(language):\\n if language in [\\\"Python3\\\", \\\"Python2\\\"]:\\n apisDir = \\\\\\n os.path.join(os.path.dirname(__file__), \\\"ProjectDjango\\\", \\\"APIs\\\")\\n apis = glob.glob(os.path.join(apisDir, '*.api'))\\n else:\\n apis = []\\n return apis\",\n \"def find_todo():\\n remove_pyc_files()\\n local('grep -ir \\\"TODO\\\" *')\",\n \"def _get_files(repo, patterns, options):\\n ctx = repo[None]\\n match = match_func(repo, ctx, patterns, options)\\n try:\\n status = ctx.status(listclean=True, listignored=True, listunknown=True)\\n except TypeError:\\n # Compatibility with older Mercurial versions.\\n status = ctx.status(clean=True, ignored=True, unknown=True)\\n modified = status[0]\\n added = status[1]\\n unknown = status[4]\\n ignored = status[5]\\n clean = status[6]\\n files = []\\n for file_list in [clean, modified, added]:\\n for filename in file_list:\\n if match(filename):\\n files.append(filename)\\n for file_list in [ignored, unknown]:\\n for filename in file_list:\\n if match.exact(filename):\\n files.append(filename)\\n return files\",\n \"def _update_code_urls(self):\\n\\n to_ignore = [\\\".gitignore\\\", \\\".keep\\\", \\\"test_converter_internal_url.py\\\"]\\n\\n for root, _, files in walk(\\n PyFunceble.CONFIG_DIRECTORY\\n + directory_separator\\n + \\\"PyFunceble\\\"\\n + directory_separator\\n ):\\n # We loop through every directories and files in the `PyFunceble` directory.\\n\\n for file in files:\\n # We loop through the list of files of the currently read directory.\\n\\n if file not in to_ignore and \\\"__pycache__\\\" not in root:\\n # * The filename is not into the list of file to ignore.\\n # and\\n # * The directory we are reading is not `__pycache__`.\\n\\n if root.endswith(directory_separator):\\n # The root directory ends with the directory separator.\\n\\n # We fix the path in the currently read file.\\n self._update_docs(root + file)\\n else:\\n # The root directory does not ends with the directory separator.\\n\\n # We fix the path in the currently read file.\\n # (after appending the directory separator between the root and file)\\n self._update_docs(root + directory_separator + file)\\n\\n for root, _, files in walk(\\n PyFunceble.CONFIG_DIRECTORY\\n + directory_separator\\n + \\\"tests\\\"\\n + directory_separator\\n ):\\n # We loop through every directories and files in the `tests` directory.\\n for file in files:\\n # We loop through the list of files of the currently read directory.\\n\\n if file not in to_ignore and \\\"__pycache__\\\" not in root:\\n # * The filename is not into the list of file to ignore.\\n # and\\n # * The directory we are reading is not `__pycache__`.\\n\\n if root.endswith(directory_separator):\\n # The root directory ends with the directory separator.\\n\\n # We fix the path in the currently read file.\\n self._update_docs(root + file)\\n else:\\n # The root directory does not ends with the directory separator.\\n\\n # We fix the path in the currently read file.\\n # (after appending the directory separator between the root and file)\\n self._update_docs(root + directory_separator + file)\",\n \"def find_reference_files():\\n for root, _, files in os.walk(\\\"./tests/references/\\\"):\\n for basename in fnmatch.filter(files, \\\"*.tex\\\"):\\n yield os.path.join(root, basename)\",\n \"def find_deleted_files_in(self, msg, branch, create_issue):\\n self._asset_bind(msg)\\n trans = self._translation_util(msg)\\n deleted_file_list = trans.find_deleted_files(\\n REPOSITORY_NAME, branch, TARGET_LANG)\\n\\n moved_files = trans.find_moved_files_in_deleted_files(REPOSITORY_NAME, branch, TARGET_LANG, deleted_file_list)\\n\\n deleted_files = list(sorted(set(deleted_file_list) - set(moved_files.values())))\\n\\n if create_issue == 0:\\n yield (\\\"move: \\\\n\\\" + \\\"\\\\n\\\".join(limit_result(list(sorted(moved_files.values())))))\\n yield (\\\"delete: \\\\n\\\" + \\\"\\\\n\\\".join(limit_result(deleted_file_list)))\\n else:\\n yield (\\\"Processing....\\\")\\n client = self._github_operator(msg)\\n new_count, skip_count = build_move_issue(trans, client, branch, moved_files)\\n yield (\\\"move: {} Issues had been created. {} Issues had been skipped.\\\".format(\\n new_count, skip_count))\\n new_count, skip_count = build_delete_issue(trans, client, branch, deleted_file_list)\\n yield (\\\"delete: {} Issues had been created. {} Issues had been skipped.\\\".format(\\n new_count, skip_count))\\n yield (\\\"Please cache issues again.\\\")\",\n \"def verify_up_to_date(path, branch=\\\"master\\\"):\\n\\n sys.stdout.write(\\\" - Verifying your branch up to date:\\\")\\n run_in_component(path, ['git', 'remote', 'update'])\\n\\n result = run_in_component(path, ['git', 'rev-list', 'HEAD...origin/%s' % branch, '--count'])\\n count = int(result.strip())\\n\\n if count == 0:\\n print(\\\" OKAY\\\")\\n return\\n\\n print(\\\" FAILED\\\")\\n\\n raise GenericError(\\\"You branch is not up-to-date with remote branch: %d different commits\\\" % count)\",\n \"def query(self):\\r\\n reports = self.get_relevant_reports()\\r\\n new_files = self.construct_report_dict(reports)\\r\\n updated, new_reports = self.is_updated(new_files, self.old_files)\\r\\n if len(self.old_files) != 0 and updated:\\r\\n self.process_changes(new_reports)\\r\\n self.old_files = new_files\",\n \"def test_case_5():\\n print(\\\"*********Test_case_5***********\\\")\\n result = find_files('.c', \\\"\\\")\\n print(result)\",\n \"def coffeescript_files():\\r\\n dirs = \\\" \\\".join(THEME_COFFEE_PATHS + [Env.REPO_ROOT / coffee_dir for coffee_dir in COFFEE_DIRS])\\r\\n return cmd('find', dirs, '-type f', '-name \\\\\\\"*.coffee\\\\\\\"')\",\n \"def on_dir_changed(self, event):\\r\\n\\r\\n if not self.searchin_update:\\r\\n pth = event.directory\\r\\n if pth is not None and exists(pth):\\r\\n self.searchin_update = True\\r\\n self.m_searchin_text.safe_set_value(pth)\\r\\n self.searchin_update = False\\r\\n event.Skip()\",\n \"def svn_fs_contents_changed(*args):\\r\\n return _fs.svn_fs_contents_changed(*args)\",\n \"def files_in_folder(self):\\n non_til = set()\\n filesInFolder = []\\n for f in self.find_all_files():\\n newstr = f.replace(\\\"~\\\", \\\"\\\") \\n if newstr in self.find_all_files():\\n non_til.add(newstr)\\n for fs in non_til:\\n filesInFolder.append(fs)\\n return filesInFolder\",\n \"def find_files():\\n \\n p = re.compile(REGEX_PART_NUMBER)\\n job_files = []\\n \\n for root, dirs, files in os.walk(project_path): # r at start of string need to prevent unicode error\\n for filename in files:\\n re_part_number = p.match(filename)\\n if re_part_number:\\n file_ext = filename.split(\\\".\\\")[-1].lower() # extract file extension \\n file_size = os.path.getsize((os.path.join(root, filename))) # filesize in bytes \\n \\n part_number = re_part_number.group() # extract part number from regular expression match\\n part_code = part_number.split(\\\"-\\\")[0]\\n \\n destinations = [] # destinations is a list in case a filetype is both a source and output filetype\\n \\n if (file_ext in EXTS_SOURCE_FILES) and flag_find_source_files:\\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \\n \\n if (file_ext in EXTS_OUTPUT_FILES) and flag_find_output_files:\\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \\n \\n if destinations: \\n job_files.append(File(filename,root,file_size,destinations,part_number,part_code))\\n print(f\\\"Found: {filename}\\\")\\n \\n return job_files\",\n \"def compare_old_and_new_change_status_files():\\n rdict=dict()\\n mastcontrol=dirutil.get_mast_control_path()\\n mastscratch=dirutil.get_mast_scratch_path()\\n recipedirs=dirutil.immediate_subdirs(os.path.join(mastcontrol,\\\"changestatusfiles\\\"))\\n for recipedir in recipedirs:\\n mystatus=\\\"unknown\\\"\\n rdict[recipedir]=dict()\\n changelist=list()\\n if not os.path.exists(os.path.join(mastscratch,recipedir)):\\n mystatus=\\\"archived\\\"\\n else:\\n ingreddirs = dirutil.immediate_subdirs(os.path.join(mastcontrol,\\\"changestatusfiles\\\",recipedir))\\n for ingreddir in ingreddirs:\\n scratchstatusfile = MASTFile(os.path.join(mastscratch,recipedir,ingreddir,\\\"change_status.txt\\\"))\\n controlstatusfile = MASTFile(os.path.join(mastcontrol,\\\"changestatusfiles\\\",recipedir,ingreddir,\\\"change_status.txt\\\"))\\n if scratchstatusfile.data == controlstatusfile.data:\\n mystatus=\\\"unchanged\\\"\\n else:\\n mystatus=\\\"changed\\\"\\n rdict[recipedir][ingreddir]=\\\"send\\\"\\n rdict[recipedir][\\\"MAIN\\\"]=mystatus\\n return rdict\",\n \"def get_list_of_copied_sample_files(self, repo, changeset_revision, dir):\\n deleted_sample_files = []\\n sample_files = []\\n for changeset in hg_util.reversed_upper_bounded_changelog(repo, changeset_revision):\\n changeset_ctx = repo.changectx(changeset)\\n for ctx_file in changeset_ctx.files():\\n ctx_file_name = basic_util.strip_path(ctx_file)\\n # If we decide in the future that files deleted later in the changelog should\\n # not be used, we can use the following if statement. if ctx_file_name.endswith( '.sample' )\\n # and ctx_file_name not in sample_files and ctx_file_name not in deleted_sample_files:\\n if ctx_file_name.endswith('.sample') and ctx_file_name not in sample_files:\\n fctx = hg_util.get_file_context_from_ctx(changeset_ctx, ctx_file)\\n if fctx in ['DELETED']:\\n # Since the possibly future used if statement above is commented out, the\\n # same file that was initially added will be discovered in an earlier changeset\\n # in the change log and fall through to the else block below. In other words,\\n # if a file named blast2go.loc.sample was added in change set 0 and then deleted\\n # in changeset 3, the deleted file in changeset 3 will be handled here, but the\\n # later discovered file in changeset 0 will be handled in the else block below.\\n # In this way, the file contents will always be found for future tools even though\\n # the file was deleted.\\n if ctx_file_name not in deleted_sample_files:\\n deleted_sample_files.append(ctx_file_name)\\n else:\\n sample_files.append(ctx_file_name)\\n tmp_ctx_file_name = os.path.join(dir, ctx_file_name.replace('.sample', ''))\\n fh = open(tmp_ctx_file_name, 'wb')\\n fh.write(fctx.data())\\n fh.close()\\n return sample_files, deleted_sample_files\",\n \"def find_legacy_log_files(xcresult_path):\\n\\n result = []\\n\\n for root, dirs, files in os.walk(xcresult_path, topdown=True):\\n for file in files:\\n if file.endswith('.txt'):\\n file = os.path.join(root, file)\\n result.append(file)\\n\\n # Sort the files by creation time.\\n result.sort(key=lambda f: os.stat(f).st_ctime)\\n return result\",\n \"def find_legacy_log_files(xcresult_path):\\n\\n result = []\\n\\n for root, dirs, files in os.walk(xcresult_path, topdown=True):\\n for file in files:\\n if file.endswith('.txt'):\\n file = os.path.join(root, file)\\n result.append(file)\\n\\n # Sort the files by creation time.\\n result.sort(key=lambda f: os.stat(f).st_ctime)\\n return result\",\n \"def get_new_change_status_files():\\n rdict=dict()\\n mastcontrol=dirutil.get_mast_control_path()\\n mastscratch=dirutil.get_mast_scratch_path()\\n olddirs=list()\\n recipedirs=dirutil.immediate_subdirs(os.path.join(mastcontrol,\\\"changestatusfiles\\\"))\\n for recipedir in recipedirs:\\n ingreddirs = dirutil.immediate_subdirs(os.path.join(mastcontrol,\\\"changestatusfiles\\\",recipedir))\\n for ingreddir in ingreddirs:\\n olddirs.append(\\\"%s/%s\\\" % (recipedir, ingreddir))\\n srecdirs = dirutil.immediate_subdirs(mastscratch)\\n for srecdir in srecdirs:\\n singreddirs = dirutil.immediate_subdirs(os.path.join(mastscratch,srecdir))\\n for singreddir in singreddirs:\\n csfile = os.path.join(mastscratch, srecdir, singreddir, \\\"change_status.txt\\\")\\n if os.path.isfile(csfile):\\n if not \\\"%s/%s\\\" % (srecdir, singreddir) in olddirs:\\n if not srecdir in rdict.keys():\\n rdict[srecdir]=dict()\\n rdict[srecdir][\\\"MAIN\\\"]=\\\"changed\\\"\\n rdict[srecdir][singreddir]=\\\"send\\\"\\n return rdict\",\n \"def getFiles(folderToProcess,filter):\\n\\n print(f\\\"Parsing {folderToProcess} for {filter} files\\\")\\n\\n if debug:\\n for path in Path(folderToProcess).rglob(filter):\\n print(f\\\"Found {path}\\\")\\n\\n all_files = [str(x) for x in Path(folderToProcess).rglob(filter)] \\n\\n return all_files\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60385823","0.5747651","0.5746556","0.5706303","0.56910634","0.5666029","0.56308615","0.56270456","0.54923","0.54485804","0.5438877","0.54278654","0.54130405","0.5369421","0.536918","0.5358993","0.53467005","0.53339535","0.5308779","0.52719516","0.52535146","0.5238755","0.52339923","0.5213406","0.52127576","0.5183439","0.51808083","0.51779795","0.5171954","0.5169869","0.51553273","0.51217145","0.509601","0.5092119","0.5086873","0.5067408","0.50539625","0.5040928","0.50321674","0.50312006","0.5027628","0.5000331","0.49860734","0.49818954","0.49807602","0.4967104","0.49640575","0.49447605","0.4942514","0.4940961","0.49389753","0.49146363","0.4914615","0.49119645","0.49106896","0.49001613","0.48912904","0.48881733","0.48806182","0.48784897","0.48729354","0.4853238","0.48503333","0.484964","0.4849463","0.4848384","0.48442283","0.48271635","0.4826384","0.48243907","0.4823418","0.48215726","0.482031","0.48199937","0.48120344","0.48118177","0.48058465","0.48009363","0.48001555","0.47976843","0.47940746","0.47904038","0.47872642","0.47868702","0.4785217","0.4780246","0.47782716","0.47782362","0.47770244","0.47723126","0.47703117","0.4763759","0.47582564","0.47568023","0.4754643","0.47536922","0.4750736","0.4750736","0.47501844","0.4746286"],"string":"[\n \"0.60385823\",\n \"0.5747651\",\n \"0.5746556\",\n \"0.5706303\",\n \"0.56910634\",\n \"0.5666029\",\n \"0.56308615\",\n \"0.56270456\",\n \"0.54923\",\n \"0.54485804\",\n \"0.5438877\",\n \"0.54278654\",\n \"0.54130405\",\n \"0.5369421\",\n \"0.536918\",\n \"0.5358993\",\n \"0.53467005\",\n \"0.53339535\",\n \"0.5308779\",\n \"0.52719516\",\n \"0.52535146\",\n \"0.5238755\",\n \"0.52339923\",\n \"0.5213406\",\n \"0.52127576\",\n \"0.5183439\",\n \"0.51808083\",\n \"0.51779795\",\n \"0.5171954\",\n \"0.5169869\",\n \"0.51553273\",\n \"0.51217145\",\n \"0.509601\",\n \"0.5092119\",\n \"0.5086873\",\n \"0.5067408\",\n \"0.50539625\",\n \"0.5040928\",\n \"0.50321674\",\n \"0.50312006\",\n \"0.5027628\",\n \"0.5000331\",\n \"0.49860734\",\n \"0.49818954\",\n \"0.49807602\",\n \"0.4967104\",\n \"0.49640575\",\n \"0.49447605\",\n \"0.4942514\",\n \"0.4940961\",\n \"0.49389753\",\n \"0.49146363\",\n \"0.4914615\",\n \"0.49119645\",\n \"0.49106896\",\n \"0.49001613\",\n \"0.48912904\",\n \"0.48881733\",\n \"0.48806182\",\n \"0.48784897\",\n \"0.48729354\",\n \"0.4853238\",\n \"0.48503333\",\n \"0.484964\",\n \"0.4849463\",\n \"0.4848384\",\n \"0.48442283\",\n \"0.48271635\",\n \"0.4826384\",\n \"0.48243907\",\n \"0.4823418\",\n \"0.48215726\",\n \"0.482031\",\n \"0.48199937\",\n \"0.48120344\",\n \"0.48118177\",\n \"0.48058465\",\n \"0.48009363\",\n \"0.48001555\",\n \"0.47976843\",\n \"0.47940746\",\n \"0.47904038\",\n \"0.47872642\",\n \"0.47868702\",\n \"0.4785217\",\n \"0.4780246\",\n \"0.47782716\",\n \"0.47782362\",\n \"0.47770244\",\n \"0.47723126\",\n \"0.47703117\",\n \"0.4763759\",\n \"0.47582564\",\n \"0.47568023\",\n \"0.4754643\",\n \"0.47536922\",\n \"0.4750736\",\n \"0.4750736\",\n \"0.47501844\",\n \"0.4746286\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6335095"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74596,"cells":{"query":{"kind":"string","value":"Find deleted/moved files from a branch for a language."},"document":{"kind":"string","value":"def find_deleted_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n deleted_file_list = trans.find_deleted_files(\n REPOSITORY_NAME, branch, TARGET_LANG)\n\n moved_files = trans.find_moved_files_in_deleted_files(REPOSITORY_NAME, branch, TARGET_LANG, deleted_file_list)\n\n deleted_files = list(sorted(set(deleted_file_list) - set(moved_files.values())))\n\n if create_issue == 0:\n yield (\"move: \\n\" + \"\\n\".join(limit_result(list(sorted(moved_files.values())))))\n yield (\"delete: \\n\" + \"\\n\".join(limit_result(deleted_file_list)))\n else:\n yield (\"Processing....\")\n client = self._github_operator(msg)\n new_count, skip_count = build_move_issue(trans, client, branch, moved_files)\n yield (\"move: {} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n new_count, skip_count = build_delete_issue(trans, client, branch, deleted_file_list)\n yield (\"delete: {} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n yield (\"Please cache issues again.\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def read_files(folder):\n print_header(\"READING FILES FROM FOLDER (RECURSIVE)\", \"=\")\n files = []\n for dirpath, dirnames, filenames in os.walk(folder):\n if not dirpath.endswith(\"updates\"):\n for filename in filenames:\n root, ext = os.path.splitext(filename)\n if ext.lower() == \".sql\":\n full_path = os.path.join(dirpath, filename)\n with open(full_path, \"r\") as f:\n sql = f.read()\n sql = sql.decode(\"latin-1\")\n\n files.append((filename, sql))\n return files","def tracked_files(self, branch):\n d = {}\n ls_tree = self.git_cmd('ls-tree -r --name-only --full-tree %s' %\n branch)\n for rpath in ls_tree.splitlines():\n if rpath == '.gitignore':\n continue\n if branch.startswith('timestamps'):\n d[rpath] = pathlib.PosixPath(self.repodir, rpath)\n else:\n if not rpath.startswith(ROOT_SUBDIR):\n continue\n d[rpath] = EtcPath(self.repodir, rpath)\n return d","def get_deleted_files(path_to_repository, ignore_subrepositories):\n diff = _get_diff_to_last_commit(path_to_repository, ignore_subrepositories)\n return [item.b_path for item in diff if item.change_type == _CHANGE_TYPE_DELETED]","def git_removed_files(self):\n\n etc_tracked = self.repo.tracked_files('etc-tmp')\n for rpath in etc_tracked:\n etc_file = os.path.join(self.root_dir, rpath)\n if not os.path.lexists(etc_file):\n self.etc_commits.removed.rpaths.append(rpath)\n self.etc_commits.removed.commit()\n\n master_tracked = self.repo.tracked_files('master-tmp')\n for rpath in master_tracked:\n etc_file = os.path.join(self.root_dir, rpath)\n if not os.path.lexists(etc_file):\n self.master_commits.removed.rpaths.append(rpath)\n self.master_commits.removed.commit()","def get_files_changed():\n files_list = []\n test = os.popen('git show --name-only')\n repo_location = os.popen('git rev-parse --show-toplevel')\n repo_location = repo_location.readlines()\n repo_location = repo_location[0]\n repo_location = repo_location.replace('\\n', '')\n if \"Not a git repository\" in repo_location:\n files_list.append(\"Not a git repository\")\n return files_list\n files_list.append(repo_location.split('/')[-1])\n output = test.readlines()\n for a in range(6, len(output)):\n files_list.append(output[a].replace('\\n', ''))\n return files_list","def findRepositoryByBackTracking():\n \n cLookBack = '.'\n while(True):\n cDir = os.path.abspath(cLookBack)\n print(\"Searching in %s\" % cDir)\n if os.path.isdir( os.path.join(cDir, DB_SUBFOLDER) ):\n return cDir\n else:\n if os.path.abspath(cLookBack) == os.path.abspath(cLookBack + '/..'):\n return os.path.abspath('.')\n cLookBack = cLookBack + '/..'\n \n return cDir","def find(query):\n acc = []\n for root, dirs, files in os.walk(query, topdown=False):\n for name in files:\n acc += [os.path.join(root, name)]\n return acc","def find_todo():\n remove_pyc_files()\n local('grep -ir \"TODO\" *')","def _find_changes(self):\n added = set()\n modified = set()\n existing_files = set()\n for dirpath_str, _, filenames in walk(str(self.path)):\n dirpath = Path(dirpath_str)\n for filename in filenames:\n if filename == DB_FILENAME:\n continue\n abs_filename = (dirpath / filename).absolute()\n if abs_filename in self.entries:\n entry = self.entries[abs_filename]\n existing_files.add(entry)\n st = lstat(str(abs_filename))\n if entry != st:\n modified.add(entry)\n else:\n try:\n entry = HashEntry(abs_filename)\n entry.update_attrs()\n added.add(entry)\n except FileNotFoundError:\n # If file was removed between listing and processing,\n # just treat it as if it never existed\n # We have nothing to compare it to anyway\n pass\n removed = set(self.entries.values()) - existing_files\n return added, removed, modified","def files_in_folder(self):\n non_til = set()\n filesInFolder = []\n for f in self.find_all_files():\n newstr = f.replace(\"~\", \"\") \n if newstr in self.find_all_files():\n non_til.add(newstr)\n for fs in non_til:\n filesInFolder.append(fs)\n return filesInFolder","def check_file_references(name):\n references = []\n for root, dirs, files in walk(Path(\".\")):\n for file_name in files:\n if is_notebook(file_name):\n nb_markdown_cells = markdown_cells(os.path.join(root, file_name))\n for cell in nb_markdown_cells:\n for line in cell:\n if name in line:\n references.append(file_name)\n else:\n with open(os.path.join(root, file_name), encoding=\"utf8\", errors='ignore') as non_nb_file:\n if name in non_nb_file.read():\n references.append(file_name)\n return references","def get_list_of_comitted_files():\n files = []\n output = []\n try:\n output = subprocess.check_output(['git','diff-index', '--name-status', '--cached','HEAD']\n ).decode(\"utf-8\")\n except subprocess.CalledProcessError:\n print(\"Error diff files get: trace %s\" % subprocess.CalledProcessError)\n return files\n\n for result in output.split(\"\\n\"):\n logging.info(result)\n if result != '':\n match = modified.match(result)\n if match:\n files.append(match.group('name'))\n\n return files","def get_tracked(wcdir):\n wcdir = abspath(wcdir)\n if \".svn\" in os.listdir(wcdir):\n svn_ls = Popen([\"svn\", \"ls\", \"-R\", wcdir], stdout=PIPE).stdout.read()\n return [normpath(l) for l in svn_ls.splitlines()]\n else:\n allfiles = []\n for root, dirs, files in os.walk(wcdir):\n allfiles += [normpath(join(root, p)[len(wcdir)+1:]) for p in files+dirs]\n return allfiles","def find_files(directory, extension, magictext):\n global files_logged\n global found_text\n dir_path = os.path.abspath(directory)\n dir_files = os.listdir(dir_path)\n for file in dir_files:\n if file.endswith(extension) and file not in files_logged:\n logger.info('New file found: {}'.format(file))\n files_logged.append(file)\n if file.endswith(extension):\n file_path = os.path.join(dir_path, file)\n if find_string_in_files(file_path, magictext):\n break\n for file in files_logged:\n if file not in dir_files:\n logger.info('File deleted: {}'.format(file))\n files_logged.remove(file)\n found_text[file] = 0","def get_changed_files():\n upstream = \"origin/master\"\n local_commit = subprocess.check_output(\n \"git rev-list HEAD ^{} -- 2>/dev/null | tail -1\".format(upstream),\n shell=True).strip().decode()\n diff_base = subprocess.check_output(\n ['git', 'rev-parse', local_commit +\n '^']).strip().decode() if local_commit else \"HEAD\"\n files = subprocess.check_output(['git', 'diff', '--name-only',\n diff_base]).strip().decode().split('\\n')\n\n repo = subprocess.check_output(['git', 'rev-parse',\n '--show-toplevel']).strip().decode()\n # add prefixes so that all and targets can be specified relative to FUCHSIA_DIR\n if repo.endswith('topaz'):\n files = [os.path.join('topaz', p) for p in files]\n elif repo.endswith('third_party/go'):\n files = [os.path.join('third_party/go', p) for p in files]\n\n return files","def get_list_of_copied_sample_files(self, repo, changeset_revision, dir):\n deleted_sample_files = []\n sample_files = []\n for changeset in hg_util.reversed_upper_bounded_changelog(repo, changeset_revision):\n changeset_ctx = repo.changectx(changeset)\n for ctx_file in changeset_ctx.files():\n ctx_file_name = basic_util.strip_path(ctx_file)\n # If we decide in the future that files deleted later in the changelog should\n # not be used, we can use the following if statement. if ctx_file_name.endswith( '.sample' )\n # and ctx_file_name not in sample_files and ctx_file_name not in deleted_sample_files:\n if ctx_file_name.endswith('.sample') and ctx_file_name not in sample_files:\n fctx = hg_util.get_file_context_from_ctx(changeset_ctx, ctx_file)\n if fctx in ['DELETED']:\n # Since the possibly future used if statement above is commented out, the\n # same file that was initially added will be discovered in an earlier changeset\n # in the change log and fall through to the else block below. In other words,\n # if a file named blast2go.loc.sample was added in change set 0 and then deleted\n # in changeset 3, the deleted file in changeset 3 will be handled here, but the\n # later discovered file in changeset 0 will be handled in the else block below.\n # In this way, the file contents will always be found for future tools even though\n # the file was deleted.\n if ctx_file_name not in deleted_sample_files:\n deleted_sample_files.append(ctx_file_name)\n else:\n sample_files.append(ctx_file_name)\n tmp_ctx_file_name = os.path.join(dir, ctx_file_name.replace('.sample', ''))\n fh = open(tmp_ctx_file_name, 'wb')\n fh.write(fctx.data())\n fh.close()\n return sample_files, deleted_sample_files","def FindCheckerFiles(path):\n if not path:\n Logger.fail(\"No source path provided\")\n elif os.path.isfile(path):\n return [ path ]\n elif os.path.isdir(path):\n foundFiles = []\n for root, dirs, files in os.walk(path):\n for file in files:\n extension = os.path.splitext(file)[1]\n if extension in [\".java\", \".smali\"]:\n foundFiles.append(os.path.join(root, file))\n return foundFiles\n else:\n Logger.fail(\"Source path \\\"\" + path + \"\\\" not found\")","def scan_root(str_ri, lst_rx, lst_rxe, db):\n print(f\"scan_root(); Scanning: {str_ri, lst_rx, lst_rxe}\")\n # get db info on this dir\n dic_db = dict() # dic by ffn of files known to the db\n str_sql = f\"SELECT * FROM files where filename like '{str_ri}%'\"\n for row in db.execute(str_sql):\n dic_db[row[0]] = row\n # Remove files that no longer exist\n lst_del_this = list()\n for str_ffn_db in dic_db.keys():\n if not os.path.isfile(str_ffn_db):\n lst_del_this.append(str_ffn_db)\n str_ffn_db__sql = str_ffn_db.replace(\"'\", \"''\")\n str_sql = f\"DELETE FROM files WHERE filename='{str_ffn_db__sql}';\"\n db.execute(str_sql)\n db.commit()\n for itm in lst_del_this: # can't change iterable from inside loop\n del dic_db[itm]\n # Walk the root-dir\n num_cntfil = 0\n for root, dirs, files in os.walk(str_ri):\n for str_fn in files:\n # if str_fn.lower().endswith('.jpg'):\n # print(str_fn)\n num_cntfil += 1\n if not any([str_fn.endswith(e) for e in lst_rxe]):\n str_ffn = os.path.join(root, str_fn)\n if not any([str_ffn.startswith(x) for x in lst_rx]): # if the file is not excluded\n if str_ffn in dic_db.keys(): # db knows this file\n obj_bdg = dic_db[str_ffn]\n tim, siz = timeandsize(str_ffn)\n if tim == dic_db[str_ffn][1] and siz == dic_db[str_ffn][2]:\n pass # print(f\" - skipping known file: {str_ffn} == {dic_db[str_ffn]}\") #\n else:\n ## print(f\"WTF: tim? {tim == dic_db[str_ffn][1]} siz? {siz == dic_db[str_ffn][2]} @ ffn: {str_ffn}\")\n # time or date have changed - so re-scanning file, and update DB.\n str_sql = f\"DELETE FROM files WHERE filename='{str_ffn}';\"\n db.execute(str_sql)\n db.commit()\n add_file2db(str_ffn, db)\n else: # db don't know this file - add it.\n add_file2db(str_ffn, db)\n if num_cntfil % 1000000 == 0:\n print(f\"Count: {num_cntfil}: {str_ffn}\")","def full_find(self, file, version):\n matches = []\n for root, dirnames, filenames in os.walk(self.checkout_path(version)):\n for filename in fnmatch.filter(filenames, file):\n matches.append(os.path.join(root, filename))\n return matches","def filesearch(word=\"\"):\n logger.info('Starting filesearch')\n file = []\n for f in glob.glob(\"*\"):\n if word[0] == \".\":\n if f.endswith(word):\n file.append(f)\n\n elif word in f:\n file.append(f)\n #return file\n logger.debug(file)\n return file","def untracked_files():\n res = run(\n \"cd %s ; git status\" % (SOURCE_ABSOLUTE),\n stdout=PIPE, stderr=PIPE,\n universal_newlines=True,\n shell=True\n )\n result = [line.strip() for line in res.stdout.split(\"\\n\")]\n\n files = [file\n for file in result if (file.endswith(\".txt\")\n and not (file.startswith(\"new file\") or\n file.startswith(\"deleted\") or file.startswith(\"modified\")))]\n\n return files","def deleted_files(conn, scan0, scan1):\n return get_new_files(conn, scan1, scan0)","def download_history_for_files(self, folder, globstring, dropbox_location, recursive=True):\n if recursive:\n files = self._recursive_file_search(folder, globstring)\n else:\n files = glob(folder + globstring)\n\n #print(files)\n for f in files:\n #print(f)\n dropboxpath = os.path.relpath(f, dropbox_location).replace(\"\\\\\", \"/\")\n dropboxpath = '/' + (dropboxpath)\n self.download_revisions(dropboxpath)","def _deleted_sources(self):\r\n # We compute the list lazily.\r\n if self._lazy_deleted_sources is None:\r\n with self.context.new_workunit('find-deleted-sources'):\r\n if os.path.exists(self._analysis_file):\r\n products = self._analysis_parser.parse_products_from_path(self._analysis_file)\r\n buildroot = get_buildroot()\r\n old_sources = products.keys() # Absolute paths.\r\n self._lazy_deleted_sources = [os.path.relpath(src, buildroot) for src in old_sources\r\n if not os.path.exists(src)]\r\n else:\r\n self._lazy_deleted_sources = []\r\n return self._lazy_deleted_sources","def git_callback(files): # type: (t.List[t.Tuple[str, str]]) -> None\n for dirpath, _dirnames, filenames in os.walk(os.path.join(data_context().content.root, '.git')):\n paths = [os.path.join(dirpath, filename) for filename in filenames]\n files.extend((path, os.path.relpath(path, data_context().content.root)) for path in paths)","def get_relevant_files(self):\n relevant_files = []\n\n if self.tree_cache is None:\n return relevant_files\n\n tree = json.loads(self.tree_cache)\n if \"commit\" in tree:\n commit_data = tree[\"commit\"]\n master_commit_files = commit_data[\"files\"]\n\n for patched_files in master_commit_files:\n relevant_file_path = \"./\" + patched_files[\"path\"]\n relevant_files.append(relevant_file_path)\n\n return relevant_files","def find_files():\n \n p = re.compile(REGEX_PART_NUMBER)\n job_files = []\n \n for root, dirs, files in os.walk(project_path): # r at start of string need to prevent unicode error\n for filename in files:\n re_part_number = p.match(filename)\n if re_part_number:\n file_ext = filename.split(\".\")[-1].lower() # extract file extension \n file_size = os.path.getsize((os.path.join(root, filename))) # filesize in bytes \n \n part_number = re_part_number.group() # extract part number from regular expression match\n part_code = part_number.split(\"-\")[0]\n \n destinations = [] # destinations is a list in case a filetype is both a source and output filetype\n \n if (file_ext in EXTS_SOURCE_FILES) and flag_find_source_files:\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \n \n if (file_ext in EXTS_OUTPUT_FILES) and flag_find_output_files:\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \n \n if destinations: \n job_files.append(File(filename,root,file_size,destinations,part_number,part_code))\n print(f\"Found: {filename}\")\n \n return job_files","def _get_files(repo, patterns, options):\n ctx = repo[None]\n match = match_func(repo, ctx, patterns, options)\n try:\n status = ctx.status(listclean=True, listignored=True, listunknown=True)\n except TypeError:\n # Compatibility with older Mercurial versions.\n status = ctx.status(clean=True, ignored=True, unknown=True)\n modified = status[0]\n added = status[1]\n unknown = status[4]\n ignored = status[5]\n clean = status[6]\n files = []\n for file_list in [clean, modified, added]:\n for filename in file_list:\n if match(filename):\n files.append(filename)\n for file_list in [ignored, unknown]:\n for filename in file_list:\n if match.exact(filename):\n files.append(filename)\n return files","def find_old_files(directory):\n # go to the directory\n os.chdir(directory)\n \n # construct the find command to interogate the directory\n find_cmd = [\"find\"]\n find_cmd.append(\"*\")\n find_cmd.append(\"-atime\")\n find_cmd.append(\"+\"+str(DAYS_OLD))\n \n # run the find command\n try:\n find = subprocess.Popen(\" \".join(find_cmd),shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\n except:\n raise\n # get the standard output\n out, err = find.communicate() # get the standard output\n found = out.decode().split() # split the text into a list\n \n # go back to our starting directory \n os.chdir(iwd)\n \n return(found)","def find_updated_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n updated_files = trans.find_updated_files(REPOSITORY_NAME, branch, TARGET_LANG)\n if create_issue == 0:\n yield (\"\\n\".join(limit_result(list(updated_files.keys()))))\n else:\n yield (\"Processing....\")\n new_count, skip_count = build_update_issue(trans, branch, updated_files)\n yield (\"{} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n yield (\"Please cache issues again.\")","def find_files(\n base,\n languages=all_languages,\n dir_ignore=default_dir_ignore,\n file_ignore=default_file_ignore,\n):\n if base[-1] != \"/\":\n base += \"/\"\n\n def update_dirs(dirs):\n \"\"\"strip the ignored directories out of the provided list\"\"\"\n index = len(dirs) - 1\n for i, d in enumerate(reversed(dirs)):\n if d in dir_ignore:\n del dirs[index - i]\n\n # walk over base\n for root, dirs, files in os.walk(base):\n root = root.replace(base, \"\", 1)\n\n # strip ignored directories from the list\n update_dirs(dirs)\n\n for filename in files:\n if filename in file_ignore:\n # skip ignored files\n continue\n\n # try to figure out the language of the specified file\n fullpath = os.path.join(base, root, filename)\n language = lang_type(fullpath)\n\n # if the file is one of the langauges that we want return\n # its name and the language\n if language in languages:\n yield fullpath, language","def _get_changed_files():\n if not ci_diff_helper:\n return None\n\n try:\n config = ci_diff_helper.get_config()\n except OSError: # Not on CI.\n return None\n\n changed_files = ci_diff_helper.get_changed_files('HEAD', config.base)\n\n changed_files = set([\n './{}'.format(filename) for filename in changed_files])\n\n return changed_files","def _findfile(self,path,label):\n files=[];filenames=os.listdir(path)\n for name in filenames:\n if os.path.splitext(name)[0]==str(label):\n files.append(name)\n return files","def test_case_3():\n print(\"*********Test_case_3***********\")\n result = find_files('.c', None)\n print(result)","def GetUnknownFiles(self):\n args = []\n status = RunShell([\"hg\", \"status\", \"--rev\", self.base_rev, \"-u\", \".\"],\n silent_ok=True)\n unknown_files = []\n for line in status.splitlines():\n st, fn = line.split(\" \", 1)\n if st == \"?\":\n unknown_files.append(fn)\n return unknown_files","def _bbcomponentwalk(self):\n for path, subdirs, files in os.walk(self.oedir()):\n try:\n del subdirs[subdirs.index('.svn')]\n\n except ValueError:\n pass\n \n for file in files:\n pf, ext = os.path.splitext(file)\n if ext == '.bb':\n yield pf.partition('_')[0]","def _git_diff_files(ref=\"master\"):\n result = []\n command = [\"git\", \"diff\", \"--name-status\", \"%s\" % (ref)]\n exit_code, output = _execute(command)\n if exit_code != 0:\n print(\"Failed to diff files.\")\n sys.exit(1)\n\n for line in output.decode(\"utf-8\").splitlines():\n parts = line.split(\"\\t\")\n action = parts[0]\n name = parts[-1]\n action = action.lower()\n result.append((action, name))\n\n return result","def detect_changed_files(self) -> list[Path]:\n repos = [(self.open_repo(), self.git_directory)]\n # Check server and api dirs too\n # Normally these are ignored but we need to check these\n if (server_repo_path := Path(self.git_directory, \"Paper-Server\")).exists():\n repos.append((pygit2.Repository(str(server_repo_path)), server_repo_path))\n if (api_repo_path := Path(self.git_directory, \"Paper-API\")).exists():\n repos.append((pygit2.Repository(str(api_repo_path)), api_repo_path))\n changed = []\n for repo, repo_path in repos:\n changed.extend(p.relative_to(self.git_directory) for p in detect_changed_files(repo, repo_path))\n changed.sort()\n return changed","def find_new_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n new_file_list = trans.find_new_files(\n REPOSITORY_NAME, branch, TARGET_LANG)\n\n if create_issue == 0:\n yield (\"\\n\".join(limit_result(new_file_list)))\n else:\n yield (\"Processing....\")\n new_count, skip_count = build_new_issue(trans, branch, new_file_list)\n yield (\"{} Issues had been created. {} Issues had been skipped.\".format(\n new_count, skip_count))\n yield (\"Please cache issues again.\")","def find_reference_files():\n for root, _, files in os.walk(\"./tests/references/\"):\n for basename in fnmatch.filter(files, \"*.tex\"):\n yield os.path.join(root, basename)","def retrieve_modified_files(self):\n result = [(diff_obj.a_path, diff_obj.b_path)\n for diff_obj in self.repo.index.diff(None)]\n\n return result","def calc_lang_features(commits, author):\n\tlang_features = ['/\\*\\*', '\\\\\"\\\\\"\\\\\"', '///', # documentation\n\t\t\t'^\\s*@', 'def.+:.+->', 'using\\s+System\\.ComponentModel\\.DataAnnotations', # assertion\n\t\t\t'assert', 'TODO', 'lambda']\n\n\t# delete contents\n\topen('lang_features.csv', 'w').close()\n\t\n\tfor count, commit in enumerate(commits):\n\t\t# status update\n\t\tif (count + 1) % 5 == 0:\n\t\t\tprint commit, '.. ..', count + 1, ' / ', len(commits)\n\n\n\t\t\t# for each blob modified\n\t\tquery = (\"for x in $(echo \" + commit + \" | ssh da4 ~/lookup/cmputeDiff2.perl); do \" +\n\t\t\t\t# get the chold and parent blob\n\t\t\t\t\"diff_blobs=$(echo $x | awk -v RS=';' 1 | sed -n '3,4 p');\" +\n\t\t\t\t# if a parent blob does not exist, the author authored all of the content of the file\n\t\t\t\t\"if [ $(echo $diff_blobs|wc -w) -eq 1 ]; then \" +\n\t\t\t\t\t\"echo $diff_blobs | ~/lookup/showCnt blob 2> /dev/null; \" +\n\t\t\t\t# if a parent blob exists, find the diff, in order to search only the modified lines\n\t\t\t\t\"elif [ $(echo $diff_blobs|wc -w) -eq 2 ]; then \" +\n\t\t\t\t\t\"vars=( $diff_blobs );\" +\n\t\t\t\t\t# using bash instead of sh in order to use the process substitution,\n\t\t\t\t\t# to get the modified lines\n\t\t\t\t\t\"/bin/bash -c \\\"diff <(echo ${vars[0]} | ~/lookup/showCnt blob)\" +\n\t\t\t\t\t\t\t\t\" <(echo ${vars[1]} | ~/lookup/showCnt blob)\\\";\" +\n\t\t\t\t\"fi;\" +\n\t\t\t# grep the above practices and discard the lines that were deleted from the parent blob\n\t\t\t# (they start with \">\" in diff)\n\t\t\t\"done | egrep \\\"\" + \"|\".join(lang_features) + \"\\\" | grep -v '^>' | wc -l \")\n\t\tcount_uses = int(bash(query).strip())\n\t\tif count_uses > 0: # good practice feature is used\n\t\t\tout = bash('echo ' + commit + ' | ~/lookup/getValues c2P')\n\t\t\tmain_proj = out.strip().split(';')[1]\n\t\t\ttime = search(commit, 'commit')[2]\n\n\t\t\tf = open(\"lang_features.csv\", \"a\")\n\t\t\tprint 'lang_f'\n\t\t\tf.write(author + ', ' + 'LANG_F' + ', ' + str(time) + ', ' + main_proj + ', ' + str(count_uses) + '\\n')\n\t\t\tf.close()\n\t\t\tprint 'wrote: -->', commit","def diff_files(self, added_deleted, path=(), pars=()):\n diffs = []\n for key, selection in self.selections.items():\n if self._is_normal_value(key):\n diffs.extend(selection.diff_files(added_deleted, path + (key,), pars + (self.diff_name,)))\n else:\n delete_special = selectors.DiffTuple(\n * (path + ((self.filename,), (key,),\n added_deleted + \" \" + repr(self._value_name(key)))),\n parameter_names = pars + (self.diff_name, self.parkey, \"DIFFERENCE\",))\n diffs.append(delete_special)\n return diffs","def test_case_5():\n print(\"*********Test_case_5***********\")\n result = find_files('.c', \"\")\n print(result)","def file_changes(self):\n new = []\n changed = []\n deleted = []\n parent = self.parent_tag\n # Loop through the files and find the ones that have changed\n for relative_path, file_dict in self.checksum[\"files\"].items():\n if relative_path not in parent[\"files\"]:\n new.append(relative_path)\n elif file_dict[\"checksum\"] != parent[\"files\"][relative_path][\"checksum\"]:\n changed.append(relative_path)\n # Loop through the parent files and see which files have been deleted\n for relative_path in parent[\"files\"].keys():\n if relative_path not in self.checksum[\"files\"]:\n deleted.append(relative_path)\n return {\"new\": new, \"changed\": changed, \"deleted\": deleted}","def get_changed_files(path_to_repository, ignore_subrepositories):\n diff = _get_diff_to_last_commit(path_to_repository, ignore_subrepositories)\n return [item.b_path for item in diff if item.change_type in _CHANGE_TYPES_CONSIDERED_FOR_PRECOMMIT]","def get_deleted_objects():\n main_objects = set()\n deleted_objects = []\n for _, dirs, files in os.walk(os.getcwd()):\n for d in dirs:\n obj_path = get_path_outside_wit(d)\n if obj_path:\n main_objects.add(d)\n for f in files:\n obj_path = get_path_outside_wit(f)\n if obj_path:\n main_objects.add(f)\n for o in staging_obj_names:\n if o not in main_objects:\n deleted_objects.append(o)\n return deleted_objects","def GetUnknownFiles(self):\r\n args = []\r\n status = RunShell([\"hg\", \"status\", \"--rev\", self.base_rev, \"-u\", \".\"],\r\n silent_ok=True)\r\n unknown_files = []\r\n for line in status.splitlines():\r\n st, fn = line.split(\" \", 1)\r\n if st == \"?\":\r\n unknown_files.append(fn)\r\n return unknown_files","def directory_find(root, word):\n for path, dirs, files in os.walk(root):\n if word in dirs:\n return os.path.join(path, word)","def getFiles(folderToProcess,filter):\n\n print(f\"Parsing {folderToProcess} for {filter} files\")\n\n if debug:\n for path in Path(folderToProcess).rglob(filter):\n print(f\"Found {path}\")\n\n all_files = [str(x) for x in Path(folderToProcess).rglob(filter)] \n\n return all_files","def find_files(suffix, path):\n returnList = []\n find_file_recursive(suffix, path, returnList)\n return returnList","def diff_files(self, added_deleted, path=(), pars=()):\n body_diffs = self.selector.flat_diff(added_deleted + \" terminal\",\n path = path + ((self.filename,)), pars = pars + (self.diff_name,))\n for diff in body_diffs:\n diff.instrument = self.instrument\n diff.filekind = self.filekind\n return body_diffs","def test_case_6():\n print(\"*********Test_case_6***********\")\n path = os.path.join(os.path.dirname(__file__), 'testdir', 't1.c')\n result = find_files('.c', path)\n print(result)","def clean_files(folder=\".\", posreg='.*[.]((py)|(rst))$',\n negreg=\".*[.]git/.*\", op=\"CR\", fLOG=print):\n def clean_file_cr(name):\n with open(name, \"rb\") as f:\n content = f.read()\n new_content = content.replace(b\"\\r\\n\", b\"\\n\")\n if new_content != content:\n with open(name, \"wb\") as f:\n f.write(new_content)\n return True\n return False\n\n def clean_file_cr_back(name):\n with open(name, \"rb\") as f:\n lines = f.read().split(b'\\n')\n new_lines = []\n changes = False\n for li in lines:\n if not li.endswith(b'\\r'):\n new_lines.append(li + b'\\r')\n changes = True\n else:\n new_lines.append(li)\n if changes:\n with open(name, \"wb\") as f:\n f.write(b'\\n'.join(new_lines))\n return changes\n\n if op == 'CR':\n clean_file = clean_file_cr\n elif op == 'CRB':\n clean_file = clean_file_cr_back\n elif op == 'pep8':\n from .code_helper import remove_extra_spaces_and_pep8\n clean_file = remove_extra_spaces_and_pep8\n else:\n raise ValueError(f\"Unknown cleaning '{op}'.\")\n\n if posreg and isinstance(posreg, str):\n posreg = re.compile(posreg)\n if negreg and isinstance(negreg, str):\n negreg = re.compile(negreg)\n\n res = []\n for root, _, files in os.walk(folder):\n for f in files:\n full = os.path.join(root, f)\n rel = os.path.relpath(full, folder)\n fn = rel.replace(\"\\\\\", \"/\")\n if posreg is None or posreg.search(fn):\n if negreg is None or not negreg.search(fn):\n r = clean_file(full)\n if r and fLOG:\n fLOG(f\"[clean_files] processed '{fn}'\")\n res.append(rel)\n return res","def find_files(substring, path, recursive=False,\n check_ext=None, ignore_invisible=True,\n ignore_substring=None):\n def check_file(f, path):\n if not (ignore_substring and ignore_substring in f):\n if substring in f:\n compl_path = os.path.join(path, f)\n if os.path.isfile(compl_path):\n return compl_path\n return False\n\n results = []\n\n if recursive:\n for par, nxt, fnames in os.walk(path):\n for f in fnames:\n fn = check_file(f, par)\n if fn:\n results.append(fn)\n\n else:\n for f in os.listdir(path):\n if ignore_invisible and f.startswith('.'):\n continue\n fn = check_file(f, path)\n if fn:\n results.append(fn)\n\n if check_ext:\n results = [r for r in results if os.path.splitext(r)[-1] == check_ext]\n\n return results","def get_files_from_git() -> Sequence[Path]:\n\n def get_files(cmd: str) -> Sequence[str]:\n output = subprocess.check_output(cmd, shell=True)\n return [os.fsdecode(x) for x in output.splitlines()]\n\n root = os.fsdecode(subprocess.check_output(\"git rev-parse --show-toplevel\", shell=True).strip())\n result: Set[str] = set()\n result.update(get_files(\"git diff --name-only --diff-filter=ACM --staged\"))\n result.update(get_files(\"git diff --name-only --diff-filter=ACM\"))\n result.update(get_files(\"git ls-files -o --full-name --exclude-standard\"))\n return sorted(Path(root, x) for x in result)","def changed_files(conn, scan0, scan1):\n c = conn.cursor()\n c.execute(\"SELECT a.pathid as pathid, a.hashid, b.hashid FROM \" +\n \"( SELECT pathid, hashid, scanid FROM files WHERE scanid=?) AS 'a' \" +\n \"JOIN (SELECT pathid, hashid, scanid FROM FILES WHERE scanid=?) as 'b' \" +\n \"ON a.pathid=b.pathid WHERE a.hashid != b.hashid\",\n (scan0, scan1))\n return (DBFile(f) for f in c)","def find_dcds(src):\n\n dcd_paths = []\n\n for root, dirs, files in os.walk(src):\n for filename in files:\n if filename.endswith(\".dcd\"):\n dcd_paths.append(os.path.join(root, filename))\n\n return dcd_paths","def _find_train_ckptfiles(path, is_delete):\n # if not exists(path):\n # return None, -1\n steps = [int(f[len(ckpt_prefix):-5]) for f in os.listdir(path)\n if f[:len(ckpt_prefix)] == ckpt_prefix and f[-5:] == '.meta']\n if len(steps) == 0:\n if is_delete:\n raise FileNotFoundError('No Available ckpt.')\n else:\n return None, -1\n max_step = max(steps)\n if len(steps) > 5 and is_delete:\n del_model_files = _get_model_files(sorted(steps)[:-5], path)\n for del_model_file in del_model_files:\n os.remove(path + del_model_file)\n\n model_files = _get_model_files(max_step, path)\n return model_files, max_step","def search(pattern='*',sub='',vol=default,mirror=False):\n base = path(sub=sub,vol=vol,mirror=mirror)\n return sorted([f.relative_to(base) for f in base.glob(pattern)])","def _search(self, log, progressbar):\n self._urls = []\n for filename in os.listdir(self._path):\n url = 'file:////' + filename\n self._urls.append(url)\n self._urls.sort()","def GetContentfilesDatabase(\n language: scrape_repos_pb2.LanguageToClone,\n) -> contentfiles.ContentFiles:\n d = pathlib.Path(language.destination_directory)\n d = d.parent / (str(d.name) + \".db\")\n return contentfiles.ContentFiles(f\"sqlite:///{d}\")","def _findChangedFiles(self):\n changedFiles = []\n # calculate and update checksums always for ALL files\n for observedFile in self.observedFiles:\n if os.path.isfile(observedFile.filePath):\n currentChecksum = checksumFile(observedFile.filePath)\n else:\n currentChecksum = None\n # different values with None value checking\n if ((observedFile.lastChecksum is None\n and currentChecksum is not None)\n or observedFile.lastChecksum != currentChecksum):\n changedFiles.append(observedFile) # notify change\n observedFile.lastChecksum = currentChecksum # update checksum\n\n return changedFiles","def get_sql_files(root=\".\", filterl=None, exclude=None):\r\n paths = Path(root).rglob(\"*.sql\")\r\n\r\n if root:\r\n if filterl:\r\n return [\r\n str(path)\r\n for path in paths\r\n if any(\r\n re.match(path.parts[-2], name, re.IGNORECASE) for name in filterl\r\n )\r\n ]\r\n if exclude:\r\n return [\r\n str(path)\r\n for path in paths\r\n if not any(\r\n re.match(path.parts[0], name, re.IGNORECASE) for name in exclude\r\n )\r\n ]\r\n return [str(path) for path in Path(root).rglob(\"*.sql\")]\r\n return [str(path) for path in paths]","def findfiles(path, ext=\".pyc\"):\n results = []\n regex = re.compile(re.escape(ext)+\"$\", re.I)\n\n tree = os.walk(path)\n for d in tree:\n # Each element of a walker represents a directory and its contents.\n # Diagnostic, if you wish.\n #print(d)\n if d[2]:\n # Are there files in this directory?\n for f in d[2]:\n if regex.findall(f):\n relpath = os.path.join(d[0], f)\n results.append(os.path.realpath(relpath))\n\n return results","def filter_files(files, lang=EXTES):\n\n lang_specific = []\n\n for f in files:\n if f.endswith(lang):\n lang_specific.append(f)\n return lang_specific","def get_changed_files_from(old_commit_sha, new_commit_sha):\n return check_output(\n \"git diff-tree --no-commit-id --name-only -r {0}..{1}\".format(\n old_commit_sha,\n new_commit_sha\n ).split(\" \")\n ).decode('utf-8').strip()","def get_files_from_of_type(path: str, ext: str) -> List[str]:\n files = []\n for root, dirnames, filenames in os.walk(path):\n for filename in fnmatch.filter(filenames, \"*.\" + str(ext)):\n files.append(os.path.join(root, filename))\n if not files:\n logging.error(\"No language files found in folder: \" + str(os.sep.join([convert_vars.BASE_PATH, \"source\"])))\n logging.debug(f\" --- found {len(files)} files of type {ext}. Showing first few:\\n* \" + str(\"\\n* \".join(files[:3])))\n return files","def _find_git_files(dirname='', git_dir=None):\n file_list = []\n if git_dir is None:\n git_dir = pbr.git._run_git_functions()\n if git_dir:\n file_list = pbr.git._run_git_command(['ls-files', '-z'], git_dir)\n file_list += pbr.git._run_git_command(\n ['submodule', 'foreach', '--quiet', 'ls-files', '-z'],\n git_dir\n )\n # Users can fix utf8 issues locally with a single commit, so we are\n # strict here.\n file_list = file_list.split(b'\\x00'.decode('utf-8'))\n submodules = _get_submodules(git_dir)\n return [f for f in file_list if f and f not in submodules]","def getContentFiles():\n contentFiles = []\n for contentDir, subDirs, filenames in os.walk(sourceDir, followlinks=True):\n if shouldIgnore(contentDir):\n subDirs[:] = []\n continue\n for filename in filenames:\n if not shouldIgnore(filename):\n cf = ContentFile(os.path.join(contentDir, filename))\n log(`cf.path`)\n contentFiles.append(cf)\n return contentFiles","def find_langs(args):\n infile = args.source\n langs = {}\n for line in infile:\n name_and_files = line.split()\n name = name_and_files[0]\n if name not in langs:\n langs[name] = []\n langs[name] += read_files.filter_files(name_and_files[1:])\n langs[args.unknown] += read_files.filter_files(args.classify)\n return langs","def scan_lang_folder(src_dir: Path, build_dir: Optional[Path],\n lang: Language, mode: TestMode) -> List[Path]:\n lang_dir = f\"epi_judge_{lang.value}\"\n if mode == TestMode.SOLUTION:\n lang_dir += '_solutions'\n src_dir = src_dir / lang_dir\n\n if lang == Language.PYTHON:\n return sorted([f for f in src_dir.glob(\"*.py\")\n if find_str_in_file(f, 'generic_test_main')])\n elif lang == Language.CPP:\n if not build_dir:\n raise RuntimeError('Missing --build-dir')\n solution_files = (f.with_suffix('').name for f in src_dir.glob(\"*.cc\")\n if find_str_in_file(f, 'GenericTestMain'))\n EXCLUDED_FILES = ['queue_with_max_using_deque', 'reverse_list']\n solution_files = sorted([build_dir / f for f in solution_files\n if f not in EXCLUDED_FILES])\n if os.name == 'nt':\n return [f.with_suffix('.exe') for f in solution_files]\n else:\n return solution_files\n elif lang == Language.JAVA:\n EXCLUDED_FILES = ['QueueWithMaxUsingDeque.java', 'ReverseList.java']\n return sorted([\n f for f in (src_dir / 'epi').glob(\"*.java\") if\n (find_str_in_file(f, '@EpiTest') and f.name not in EXCLUDED_FILES)\n ])\n\n raise NotImplementedError()","def _get_relevant_files(self, local_repo_path):\n r = GitRepo(local_repo_path)\n all_commits = r.git.log('--name-only', '--pretty=format:').split()\n counted_commits = Counter(all_commits)\n # Sort the files according to the number of commits they appear in\n sorted_commits = sorted(counted_commits.items(),\n key=lambda x: x[1],\n reverse=True)\n # Return the file names sorted per commits number\n return list(zip(*sorted_commits))[0]","def checkGit(directory):","def get_filepaths(keyword, directory):\n \n matches = []\n filenames_total = []\n \n for root, dirnames, filenames in os.walk(directory):\n for filename in filenames:\n if keyword in filename:\n matches.append(root + '/' + filename)\n filenames_total.append(filename)\n return matches, filenames_total","def search_file(repos, path_parts, rev, search_re):\n\n # Read in each line of a checked-out file, and then use re.search to\n # search line.\n fp = repos.openfile(path_parts, rev, {})[0]\n matches = 0\n while 1:\n line = fp.readline()\n if not line:\n break\n # FIXME: Is there what can we do about file encoding?\n if search_re.search(line.decode(\"utf-8\", \"surrogateescape\")):\n matches = 1\n fp.close()\n break\n return matches","def get_path_to_github_files(github_dir):\n path_to_files = {}\n for r, d, f in os.walk(github_dir):\n if '.git' in r or '_tutorial' in r:\n continue\n for folder in d:\n if folder == '.git' or folder == '_tutorial':\n continue\n path = os.path.join(r, folder)\n path_to_files[path] = os.listdir(path)\n filtered_path_to_files = {}\n for path in path_to_files:\n if DESCRIPTION_FILE in path_to_files[path]:\n filtered_path_to_files[path] = path_to_files[path]\n return filtered_path_to_files","def find_legacy_log_files(xcresult_path):\n\n result = []\n\n for root, dirs, files in os.walk(xcresult_path, topdown=True):\n for file in files:\n if file.endswith('.txt'):\n file = os.path.join(root, file)\n result.append(file)\n\n # Sort the files by creation time.\n result.sort(key=lambda f: os.stat(f).st_ctime)\n return result","def find_legacy_log_files(xcresult_path):\n\n result = []\n\n for root, dirs, files in os.walk(xcresult_path, topdown=True):\n for file in files:\n if file.endswith('.txt'):\n file = os.path.join(root, file)\n result.append(file)\n\n # Sort the files by creation time.\n result.sort(key=lambda f: os.stat(f).st_ctime)\n return result","def find(self, file, version):\n matches = []\n for root, dirnames, filenames in os.walk(self.full_doc_path(version)):\n for filename in fnmatch.filter(filenames, file):\n matches.append(os.path.join(root, filename))\n return matches","def coffeescript_files():\r\n dirs = \" \".join(THEME_COFFEE_PATHS + [Env.REPO_ROOT / coffee_dir for coffee_dir in COFFEE_DIRS])\r\n return cmd('find', dirs, '-type f', '-name \\\"*.coffee\\\"')","def all_changed_files(self):\n return [path_to_file_type(os.path.join(self.path, p)) for p in self.changed_paths() if p]","def process_files(file_location, day):\n # construct file path\n file_dir = PREFIX+file_location\n file_pattern = file_dir+'lz_'+day+'*_raw.root'\n # print(file_pattern)\n file_list = glob.glob(file_pattern)\n print(\"There are %s MC files in the requested directory (%s).\" %(len(file_list), file_dir))\n file_names = []\n for f in file_list:\n file_name_only = f.split('/')\n file_names.append(file_name_only[-1])\n return file_names","async def get_files_for_menu(language: str):\n menu_query = menu_queries.QUERY_FILES_FOR_LANGUAGE\n current_bind_vars = {\"language\": language}\n if language == \"multi\":\n menu_query = menu_queries.QUERY_FILES_FOR_MULTILANG\n current_bind_vars = {}\n try:\n language_menu_query_result = get_db().AQLQuery(\n query=menu_query,\n batchSize=10000,\n bindVars=current_bind_vars\n )\n return {\"result\": language_menu_query_result.result}\n\n except DocumentNotFoundError as error:\n print(error)\n raise HTTPException(status_code=404, detail=\"Item not found\") from error\n except AQLQueryError as error:\n print(\"AQLQueryError: \", error)\n raise HTTPException(status_code=400, detail=error.errors) from error\n except KeyError as error:\n print(\"KeyError: \", error)\n raise HTTPException(status_code=400) from error","def createFileList(my_dir, term):\n \n for subdir, dirs, files in os.walk(my_dir):\n for file in files:\n if re.search(re.escape(term), file):\n result = os.path.join(subdir, file)\n fileList.append(result)\n return fileList","def retrieve_tracked_files(self):\n result = []\n\n for key in self.repo.index.entries.keys():\n\n result.append(os.path.join(self.repo.working_dir, key[0]))\n\n return result","def unix_find(pathin):\n return [os.path.join(path, file)\n for (path, dirs, files) in os.walk(pathin, followlinks=False)\n for file in files]","def _get_target_files(self) -> List[Path]:\n repo = get_git_repo()\n submodules = repo.submodules # type: ignore\n submodule_paths = [\n self._fname_to_path(repo, submodule.path) for submodule in submodules\n ]\n\n # resolve given paths relative to current working directory\n paths = [p.resolve() for p in self._paths]\n if self._base_commit is not None:\n paths = [\n a\n for a in (self._status.added + self._status.modified)\n # diff_path is a subpath of some element of input_paths\n if any((a == path or path in a.parents) for path in paths)\n ]\n changed_count = len(paths)\n click.echo(f\"| looking at {unit_len(paths, 'changed path')}\", err=True)\n paths = [\n path\n for path in paths\n if all(\n submodule_path not in path.parents\n for submodule_path in submodule_paths\n )\n ]\n if len(paths) != changed_count:\n click.echo(\n f\"| skipping files in {unit_len(submodule_paths, 'submodule')}: \"\n + \", \".join(str(path) for path in submodule_paths),\n err=True,\n )\n\n # Filter out ignore rules, expand directories\n self._ignore_rules_file.seek(0)\n patterns = Parser(self._base_path).parse(self._ignore_rules_file)\n\n file_ignore = FileIgnore(\n base_path=self._base_path, patterns=patterns, target_paths=paths\n )\n\n walked_entries = list(file_ignore.entries())\n click.echo(\n f\"| found {unit_len(walked_entries, 'file')} in the paths to be scanned\",\n err=True,\n )\n filtered: List[Path] = []\n for elem in walked_entries:\n if elem.survives:\n filtered.append(elem.path)\n\n skipped_count = len(walked_entries) - len(filtered)\n if skipped_count:\n click.echo(\n f\"| skipping {unit_len(range(skipped_count), 'file')} based on path ignore rules\",\n err=True,\n )\n\n relative_paths = [path.relative_to(self._base_path) for path in filtered]\n\n return relative_paths","def find_new_files(self):\r\n in_file_system = self._find_data_files(self.basepath,\r\n self.data_file_extension, [])\r\n status_msg('Search for data files. Found: {0}'.format(\r\n len(in_file_system)), True\r\n )\r\n status_msg('Check that there are no duplicate filenames', True)\r\n in_db = self._get_files_in_db()\r\n status_msg('Get list of files in data base. Found: {0}'.format(\r\n len(in_db)), True\r\n )\r\n for filename in in_file_system:\r\n # Filename has been added to the database both where a part of the\r\n # path was a part of the unique name and where it was not,\r\n # therefore we need to check for both AND we ignore filenames that\r\n # start with IGNORE\r\n if form_unique_name(filename) not in in_db and\\\r\n os.path.basename(filename) not in in_db and not\\\r\n os.path.basename(filename).startswith('IGNORE'):\r\n self.new_data_files.append(filename)\r\n\r\n print # Adds a new line to the statusses before parsing\r\n status_msg('Collecting new files. Found: {0}'.format(\r\n len(self.new_data_files)), True\r\n )","def get_git_project_files():\n cached_and_untracked_files = git_ls_files(\n '--cached', # All files cached in the index\n '--others', # Untracked files\n # Exclude untracked files that would be excluded by .gitignore, etc.\n '--exclude-standard')\n uncommitted_deleted_files = git_ls_files('--deleted')\n\n # Since sorting of files in a set is arbitrary, return a sorted list to\n # provide a well-defined order to tools like flake8, etc.\n return sorted(cached_and_untracked_files - uncommitted_deleted_files)","def find_files(root, directory, filename):\n\n path_list = []\n walker = os.walk(root, followlinks=True)\n for root, dirs, files in walker:\n remove_vcs_dirs(dirs)\n\n #if dirs containt 'directory', don't walk others\n if directory in dirs: dirs[:] = [directory]\n\n if root.endswith(os.path.sep + directory):\n if filename in files:\n path_list.append(os.path.join(root, filename))\n dirs[:] = []\n\n return path_list","def directory(mirror):\n # Download the directory of files from the webpage for a particular language.\n parser = ParseDirectory()\n directory = urllib2.urlopen(mirror)\n parser.feed(directory.read().decode('utf-8'))\n # Extract the filenames of each XML meta history file.\n meta = re.compile('^[a-zA-Z-]+wiki-latest-pages-meta-history.*\\.bz2$')\n return [(mirror, filename) for filename in parser.files if meta.match(filename)]","def find_build(branch, tag, build_path=None, old_build_path=None):\n for directory_format in [build_path, old_build_path]:\n if directory_format is None:\n continue\n loc = directory_format % (branch, tag)\n if isdir(loc):\n return loc\n raise BuildNotFound(branch, tag)","def filter_changed_files(changed_files, path_to_repository, file_encoding):\n filtered_files = []\n for changed_file in changed_files:\n file_is_valid = True\n if (os.path.isdir(os.path.join(path_to_repository, changed_file))):\n #ignore directories (e.g., git submodule folders)\n continue\n if os.path.getsize(os.path.join(path_to_repository, changed_file)) > 1 * 1024 * 1024:\n print('File too large for precommit analysis. Ignoring: %s' % changed_file)\n file_is_valid = False\n\n try:\n with open(os.path.join(path_to_repository, changed_file), encoding=file_encoding) as file:\n file.read()\n except UnicodeDecodeError:\n encoding_string = file_encoding\n if encoding_string is None:\n encoding_string = locale.getpreferredencoding() + ' (system encoding)'\n\n print(\n 'File at %s is not encoded in %s. Try using the --file-encoding option.' % (\n changed_file, encoding_string))\n file_is_valid = False\n\n if file_is_valid:\n filtered_files.append(changed_file)\n\n return filtered_files","def list_all_files(self, branch_name) -> dict:\n try:\n files_response = self.repo.get_contents(self.repo_folder,\n ref=branch_name)\n files_list = []\n for file in files_response:\n files_list.append(file.raw_data.get('path'))\n return make_success_response(200, files_list)\n except GithubException as github_exc:\n return make_error_response(github_exc.status, github_exc.data)","def _get_file_list(folder):\n tree = [x for x in os.walk(folder)]\n files = [os.path.join(t[0], y) for t in tree for y in t[2]]\n return [os.path.relpath(x, start=folder)\n for x in files if x != LOCAL_METADATA_FILE]","def compare_tree(self):\n result = []\n \n pathA = os.path.join(self.testpath,'A')\n pathB = os.path.join(self.testpath,'B')\n\n filesA = [os.path.relpath(f,pathA) for f in self.tree(pathA)]\n filesB = [os.path.relpath(f,pathB) for f in self.tree(pathB)]\n\n filesAB = set(filesA).union(filesB)\n for fileAB in sorted(list(filesAB)):\n\n fileA = os.path.join(self.testpath,'A',fileAB)\n fileB = os.path.join(self.testpath,'B',fileAB)\n try:\n fileAtxt = open(fileA).read()\n except IOError:\n result.append( ('missing_inA',fileAB) )\n continue\n \n try:\n fileBtxt = open(fileB).read()\n except IOError:\n result.append( ('missing_inB',fileAB) )\n continue\n\n if not fileAtxt == fileBtxt:\n result.append( ('disagree',fileAB))\n \n return result","def _FindLocations(input_api, search_regexes, files_to_check, files_to_skip):\n def FilterFile(affected_file):\n return input_api.FilterSourceFile(\n affected_file,\n files_to_check=files_to_check,\n files_to_skip=files_to_skip)\n\n no_presubmit = r\"// no-presubmit-check\"\n locations = []\n for f in input_api.AffectedSourceFiles(FilterFile):\n for line_num, line in f.ChangedContents():\n for search_regex in search_regexes:\n if (input_api.re.search(search_regex, line) and\n not input_api.re.search(no_presubmit, line)):\n locations.append(\" %s:%d\" % (f.LocalPath(), line_num))\n break\n return locations","def files_changed(revish: Text,\n ignore_rules: Optional[Sequence[Text]] = None,\n include_uncommitted: bool = False,\n include_new: bool = False\n ) -> Tuple[List[Text], List[Text]]:\n files = repo_files_changed(revish,\n include_uncommitted=include_uncommitted,\n include_new=include_new)\n if not files:\n return [], []\n\n return exclude_ignored(files, ignore_rules)","def _mark_deleted(self):\n\n self.__class__ = DeletedCurrentMirrorDirectory\n\n for (cvs_path, id) in self._entries.iteritems():\n if id in self.repo._new_nodes:\n node = self[cvs_path]\n if isinstance(node, _WritableMirrorDirectoryMixin):\n # Mark deleted and recurse:\n node._mark_deleted()"],"string":"[\n \"def read_files(folder):\\n print_header(\\\"READING FILES FROM FOLDER (RECURSIVE)\\\", \\\"=\\\")\\n files = []\\n for dirpath, dirnames, filenames in os.walk(folder):\\n if not dirpath.endswith(\\\"updates\\\"):\\n for filename in filenames:\\n root, ext = os.path.splitext(filename)\\n if ext.lower() == \\\".sql\\\":\\n full_path = os.path.join(dirpath, filename)\\n with open(full_path, \\\"r\\\") as f:\\n sql = f.read()\\n sql = sql.decode(\\\"latin-1\\\")\\n\\n files.append((filename, sql))\\n return files\",\n \"def tracked_files(self, branch):\\n d = {}\\n ls_tree = self.git_cmd('ls-tree -r --name-only --full-tree %s' %\\n branch)\\n for rpath in ls_tree.splitlines():\\n if rpath == '.gitignore':\\n continue\\n if branch.startswith('timestamps'):\\n d[rpath] = pathlib.PosixPath(self.repodir, rpath)\\n else:\\n if not rpath.startswith(ROOT_SUBDIR):\\n continue\\n d[rpath] = EtcPath(self.repodir, rpath)\\n return d\",\n \"def get_deleted_files(path_to_repository, ignore_subrepositories):\\n diff = _get_diff_to_last_commit(path_to_repository, ignore_subrepositories)\\n return [item.b_path for item in diff if item.change_type == _CHANGE_TYPE_DELETED]\",\n \"def git_removed_files(self):\\n\\n etc_tracked = self.repo.tracked_files('etc-tmp')\\n for rpath in etc_tracked:\\n etc_file = os.path.join(self.root_dir, rpath)\\n if not os.path.lexists(etc_file):\\n self.etc_commits.removed.rpaths.append(rpath)\\n self.etc_commits.removed.commit()\\n\\n master_tracked = self.repo.tracked_files('master-tmp')\\n for rpath in master_tracked:\\n etc_file = os.path.join(self.root_dir, rpath)\\n if not os.path.lexists(etc_file):\\n self.master_commits.removed.rpaths.append(rpath)\\n self.master_commits.removed.commit()\",\n \"def get_files_changed():\\n files_list = []\\n test = os.popen('git show --name-only')\\n repo_location = os.popen('git rev-parse --show-toplevel')\\n repo_location = repo_location.readlines()\\n repo_location = repo_location[0]\\n repo_location = repo_location.replace('\\\\n', '')\\n if \\\"Not a git repository\\\" in repo_location:\\n files_list.append(\\\"Not a git repository\\\")\\n return files_list\\n files_list.append(repo_location.split('/')[-1])\\n output = test.readlines()\\n for a in range(6, len(output)):\\n files_list.append(output[a].replace('\\\\n', ''))\\n return files_list\",\n \"def findRepositoryByBackTracking():\\n \\n cLookBack = '.'\\n while(True):\\n cDir = os.path.abspath(cLookBack)\\n print(\\\"Searching in %s\\\" % cDir)\\n if os.path.isdir( os.path.join(cDir, DB_SUBFOLDER) ):\\n return cDir\\n else:\\n if os.path.abspath(cLookBack) == os.path.abspath(cLookBack + '/..'):\\n return os.path.abspath('.')\\n cLookBack = cLookBack + '/..'\\n \\n return cDir\",\n \"def find(query):\\n acc = []\\n for root, dirs, files in os.walk(query, topdown=False):\\n for name in files:\\n acc += [os.path.join(root, name)]\\n return acc\",\n \"def find_todo():\\n remove_pyc_files()\\n local('grep -ir \\\"TODO\\\" *')\",\n \"def _find_changes(self):\\n added = set()\\n modified = set()\\n existing_files = set()\\n for dirpath_str, _, filenames in walk(str(self.path)):\\n dirpath = Path(dirpath_str)\\n for filename in filenames:\\n if filename == DB_FILENAME:\\n continue\\n abs_filename = (dirpath / filename).absolute()\\n if abs_filename in self.entries:\\n entry = self.entries[abs_filename]\\n existing_files.add(entry)\\n st = lstat(str(abs_filename))\\n if entry != st:\\n modified.add(entry)\\n else:\\n try:\\n entry = HashEntry(abs_filename)\\n entry.update_attrs()\\n added.add(entry)\\n except FileNotFoundError:\\n # If file was removed between listing and processing,\\n # just treat it as if it never existed\\n # We have nothing to compare it to anyway\\n pass\\n removed = set(self.entries.values()) - existing_files\\n return added, removed, modified\",\n \"def files_in_folder(self):\\n non_til = set()\\n filesInFolder = []\\n for f in self.find_all_files():\\n newstr = f.replace(\\\"~\\\", \\\"\\\") \\n if newstr in self.find_all_files():\\n non_til.add(newstr)\\n for fs in non_til:\\n filesInFolder.append(fs)\\n return filesInFolder\",\n \"def check_file_references(name):\\n references = []\\n for root, dirs, files in walk(Path(\\\".\\\")):\\n for file_name in files:\\n if is_notebook(file_name):\\n nb_markdown_cells = markdown_cells(os.path.join(root, file_name))\\n for cell in nb_markdown_cells:\\n for line in cell:\\n if name in line:\\n references.append(file_name)\\n else:\\n with open(os.path.join(root, file_name), encoding=\\\"utf8\\\", errors='ignore') as non_nb_file:\\n if name in non_nb_file.read():\\n references.append(file_name)\\n return references\",\n \"def get_list_of_comitted_files():\\n files = []\\n output = []\\n try:\\n output = subprocess.check_output(['git','diff-index', '--name-status', '--cached','HEAD']\\n ).decode(\\\"utf-8\\\")\\n except subprocess.CalledProcessError:\\n print(\\\"Error diff files get: trace %s\\\" % subprocess.CalledProcessError)\\n return files\\n\\n for result in output.split(\\\"\\\\n\\\"):\\n logging.info(result)\\n if result != '':\\n match = modified.match(result)\\n if match:\\n files.append(match.group('name'))\\n\\n return files\",\n \"def get_tracked(wcdir):\\n wcdir = abspath(wcdir)\\n if \\\".svn\\\" in os.listdir(wcdir):\\n svn_ls = Popen([\\\"svn\\\", \\\"ls\\\", \\\"-R\\\", wcdir], stdout=PIPE).stdout.read()\\n return [normpath(l) for l in svn_ls.splitlines()]\\n else:\\n allfiles = []\\n for root, dirs, files in os.walk(wcdir):\\n allfiles += [normpath(join(root, p)[len(wcdir)+1:]) for p in files+dirs]\\n return allfiles\",\n \"def find_files(directory, extension, magictext):\\n global files_logged\\n global found_text\\n dir_path = os.path.abspath(directory)\\n dir_files = os.listdir(dir_path)\\n for file in dir_files:\\n if file.endswith(extension) and file not in files_logged:\\n logger.info('New file found: {}'.format(file))\\n files_logged.append(file)\\n if file.endswith(extension):\\n file_path = os.path.join(dir_path, file)\\n if find_string_in_files(file_path, magictext):\\n break\\n for file in files_logged:\\n if file not in dir_files:\\n logger.info('File deleted: {}'.format(file))\\n files_logged.remove(file)\\n found_text[file] = 0\",\n \"def get_changed_files():\\n upstream = \\\"origin/master\\\"\\n local_commit = subprocess.check_output(\\n \\\"git rev-list HEAD ^{} -- 2>/dev/null | tail -1\\\".format(upstream),\\n shell=True).strip().decode()\\n diff_base = subprocess.check_output(\\n ['git', 'rev-parse', local_commit +\\n '^']).strip().decode() if local_commit else \\\"HEAD\\\"\\n files = subprocess.check_output(['git', 'diff', '--name-only',\\n diff_base]).strip().decode().split('\\\\n')\\n\\n repo = subprocess.check_output(['git', 'rev-parse',\\n '--show-toplevel']).strip().decode()\\n # add prefixes so that all and targets can be specified relative to FUCHSIA_DIR\\n if repo.endswith('topaz'):\\n files = [os.path.join('topaz', p) for p in files]\\n elif repo.endswith('third_party/go'):\\n files = [os.path.join('third_party/go', p) for p in files]\\n\\n return files\",\n \"def get_list_of_copied_sample_files(self, repo, changeset_revision, dir):\\n deleted_sample_files = []\\n sample_files = []\\n for changeset in hg_util.reversed_upper_bounded_changelog(repo, changeset_revision):\\n changeset_ctx = repo.changectx(changeset)\\n for ctx_file in changeset_ctx.files():\\n ctx_file_name = basic_util.strip_path(ctx_file)\\n # If we decide in the future that files deleted later in the changelog should\\n # not be used, we can use the following if statement. if ctx_file_name.endswith( '.sample' )\\n # and ctx_file_name not in sample_files and ctx_file_name not in deleted_sample_files:\\n if ctx_file_name.endswith('.sample') and ctx_file_name not in sample_files:\\n fctx = hg_util.get_file_context_from_ctx(changeset_ctx, ctx_file)\\n if fctx in ['DELETED']:\\n # Since the possibly future used if statement above is commented out, the\\n # same file that was initially added will be discovered in an earlier changeset\\n # in the change log and fall through to the else block below. In other words,\\n # if a file named blast2go.loc.sample was added in change set 0 and then deleted\\n # in changeset 3, the deleted file in changeset 3 will be handled here, but the\\n # later discovered file in changeset 0 will be handled in the else block below.\\n # In this way, the file contents will always be found for future tools even though\\n # the file was deleted.\\n if ctx_file_name not in deleted_sample_files:\\n deleted_sample_files.append(ctx_file_name)\\n else:\\n sample_files.append(ctx_file_name)\\n tmp_ctx_file_name = os.path.join(dir, ctx_file_name.replace('.sample', ''))\\n fh = open(tmp_ctx_file_name, 'wb')\\n fh.write(fctx.data())\\n fh.close()\\n return sample_files, deleted_sample_files\",\n \"def FindCheckerFiles(path):\\n if not path:\\n Logger.fail(\\\"No source path provided\\\")\\n elif os.path.isfile(path):\\n return [ path ]\\n elif os.path.isdir(path):\\n foundFiles = []\\n for root, dirs, files in os.walk(path):\\n for file in files:\\n extension = os.path.splitext(file)[1]\\n if extension in [\\\".java\\\", \\\".smali\\\"]:\\n foundFiles.append(os.path.join(root, file))\\n return foundFiles\\n else:\\n Logger.fail(\\\"Source path \\\\\\\"\\\" + path + \\\"\\\\\\\" not found\\\")\",\n \"def scan_root(str_ri, lst_rx, lst_rxe, db):\\n print(f\\\"scan_root(); Scanning: {str_ri, lst_rx, lst_rxe}\\\")\\n # get db info on this dir\\n dic_db = dict() # dic by ffn of files known to the db\\n str_sql = f\\\"SELECT * FROM files where filename like '{str_ri}%'\\\"\\n for row in db.execute(str_sql):\\n dic_db[row[0]] = row\\n # Remove files that no longer exist\\n lst_del_this = list()\\n for str_ffn_db in dic_db.keys():\\n if not os.path.isfile(str_ffn_db):\\n lst_del_this.append(str_ffn_db)\\n str_ffn_db__sql = str_ffn_db.replace(\\\"'\\\", \\\"''\\\")\\n str_sql = f\\\"DELETE FROM files WHERE filename='{str_ffn_db__sql}';\\\"\\n db.execute(str_sql)\\n db.commit()\\n for itm in lst_del_this: # can't change iterable from inside loop\\n del dic_db[itm]\\n # Walk the root-dir\\n num_cntfil = 0\\n for root, dirs, files in os.walk(str_ri):\\n for str_fn in files:\\n # if str_fn.lower().endswith('.jpg'):\\n # print(str_fn)\\n num_cntfil += 1\\n if not any([str_fn.endswith(e) for e in lst_rxe]):\\n str_ffn = os.path.join(root, str_fn)\\n if not any([str_ffn.startswith(x) for x in lst_rx]): # if the file is not excluded\\n if str_ffn in dic_db.keys(): # db knows this file\\n obj_bdg = dic_db[str_ffn]\\n tim, siz = timeandsize(str_ffn)\\n if tim == dic_db[str_ffn][1] and siz == dic_db[str_ffn][2]:\\n pass # print(f\\\" - skipping known file: {str_ffn} == {dic_db[str_ffn]}\\\") #\\n else:\\n ## print(f\\\"WTF: tim? {tim == dic_db[str_ffn][1]} siz? {siz == dic_db[str_ffn][2]} @ ffn: {str_ffn}\\\")\\n # time or date have changed - so re-scanning file, and update DB.\\n str_sql = f\\\"DELETE FROM files WHERE filename='{str_ffn}';\\\"\\n db.execute(str_sql)\\n db.commit()\\n add_file2db(str_ffn, db)\\n else: # db don't know this file - add it.\\n add_file2db(str_ffn, db)\\n if num_cntfil % 1000000 == 0:\\n print(f\\\"Count: {num_cntfil}: {str_ffn}\\\")\",\n \"def full_find(self, file, version):\\n matches = []\\n for root, dirnames, filenames in os.walk(self.checkout_path(version)):\\n for filename in fnmatch.filter(filenames, file):\\n matches.append(os.path.join(root, filename))\\n return matches\",\n \"def filesearch(word=\\\"\\\"):\\n logger.info('Starting filesearch')\\n file = []\\n for f in glob.glob(\\\"*\\\"):\\n if word[0] == \\\".\\\":\\n if f.endswith(word):\\n file.append(f)\\n\\n elif word in f:\\n file.append(f)\\n #return file\\n logger.debug(file)\\n return file\",\n \"def untracked_files():\\n res = run(\\n \\\"cd %s ; git status\\\" % (SOURCE_ABSOLUTE),\\n stdout=PIPE, stderr=PIPE,\\n universal_newlines=True,\\n shell=True\\n )\\n result = [line.strip() for line in res.stdout.split(\\\"\\\\n\\\")]\\n\\n files = [file\\n for file in result if (file.endswith(\\\".txt\\\")\\n and not (file.startswith(\\\"new file\\\") or\\n file.startswith(\\\"deleted\\\") or file.startswith(\\\"modified\\\")))]\\n\\n return files\",\n \"def deleted_files(conn, scan0, scan1):\\n return get_new_files(conn, scan1, scan0)\",\n \"def download_history_for_files(self, folder, globstring, dropbox_location, recursive=True):\\n if recursive:\\n files = self._recursive_file_search(folder, globstring)\\n else:\\n files = glob(folder + globstring)\\n\\n #print(files)\\n for f in files:\\n #print(f)\\n dropboxpath = os.path.relpath(f, dropbox_location).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n dropboxpath = '/' + (dropboxpath)\\n self.download_revisions(dropboxpath)\",\n \"def _deleted_sources(self):\\r\\n # We compute the list lazily.\\r\\n if self._lazy_deleted_sources is None:\\r\\n with self.context.new_workunit('find-deleted-sources'):\\r\\n if os.path.exists(self._analysis_file):\\r\\n products = self._analysis_parser.parse_products_from_path(self._analysis_file)\\r\\n buildroot = get_buildroot()\\r\\n old_sources = products.keys() # Absolute paths.\\r\\n self._lazy_deleted_sources = [os.path.relpath(src, buildroot) for src in old_sources\\r\\n if not os.path.exists(src)]\\r\\n else:\\r\\n self._lazy_deleted_sources = []\\r\\n return self._lazy_deleted_sources\",\n \"def git_callback(files): # type: (t.List[t.Tuple[str, str]]) -> None\\n for dirpath, _dirnames, filenames in os.walk(os.path.join(data_context().content.root, '.git')):\\n paths = [os.path.join(dirpath, filename) for filename in filenames]\\n files.extend((path, os.path.relpath(path, data_context().content.root)) for path in paths)\",\n \"def get_relevant_files(self):\\n relevant_files = []\\n\\n if self.tree_cache is None:\\n return relevant_files\\n\\n tree = json.loads(self.tree_cache)\\n if \\\"commit\\\" in tree:\\n commit_data = tree[\\\"commit\\\"]\\n master_commit_files = commit_data[\\\"files\\\"]\\n\\n for patched_files in master_commit_files:\\n relevant_file_path = \\\"./\\\" + patched_files[\\\"path\\\"]\\n relevant_files.append(relevant_file_path)\\n\\n return relevant_files\",\n \"def find_files():\\n \\n p = re.compile(REGEX_PART_NUMBER)\\n job_files = []\\n \\n for root, dirs, files in os.walk(project_path): # r at start of string need to prevent unicode error\\n for filename in files:\\n re_part_number = p.match(filename)\\n if re_part_number:\\n file_ext = filename.split(\\\".\\\")[-1].lower() # extract file extension \\n file_size = os.path.getsize((os.path.join(root, filename))) # filesize in bytes \\n \\n part_number = re_part_number.group() # extract part number from regular expression match\\n part_code = part_number.split(\\\"-\\\")[0]\\n \\n destinations = [] # destinations is a list in case a filetype is both a source and output filetype\\n \\n if (file_ext in EXTS_SOURCE_FILES) and flag_find_source_files:\\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \\n \\n if (file_ext in EXTS_OUTPUT_FILES) and flag_find_output_files:\\n destinations.append(os.path.join(target_source_path,part_code,part_number)) \\n \\n if destinations: \\n job_files.append(File(filename,root,file_size,destinations,part_number,part_code))\\n print(f\\\"Found: {filename}\\\")\\n \\n return job_files\",\n \"def _get_files(repo, patterns, options):\\n ctx = repo[None]\\n match = match_func(repo, ctx, patterns, options)\\n try:\\n status = ctx.status(listclean=True, listignored=True, listunknown=True)\\n except TypeError:\\n # Compatibility with older Mercurial versions.\\n status = ctx.status(clean=True, ignored=True, unknown=True)\\n modified = status[0]\\n added = status[1]\\n unknown = status[4]\\n ignored = status[5]\\n clean = status[6]\\n files = []\\n for file_list in [clean, modified, added]:\\n for filename in file_list:\\n if match(filename):\\n files.append(filename)\\n for file_list in [ignored, unknown]:\\n for filename in file_list:\\n if match.exact(filename):\\n files.append(filename)\\n return files\",\n \"def find_old_files(directory):\\n # go to the directory\\n os.chdir(directory)\\n \\n # construct the find command to interogate the directory\\n find_cmd = [\\\"find\\\"]\\n find_cmd.append(\\\"*\\\")\\n find_cmd.append(\\\"-atime\\\")\\n find_cmd.append(\\\"+\\\"+str(DAYS_OLD))\\n \\n # run the find command\\n try:\\n find = subprocess.Popen(\\\" \\\".join(find_cmd),shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\\n except:\\n raise\\n # get the standard output\\n out, err = find.communicate() # get the standard output\\n found = out.decode().split() # split the text into a list\\n \\n # go back to our starting directory \\n os.chdir(iwd)\\n \\n return(found)\",\n \"def find_updated_files_in(self, msg, branch, create_issue):\\n self._asset_bind(msg)\\n trans = self._translation_util(msg)\\n updated_files = trans.find_updated_files(REPOSITORY_NAME, branch, TARGET_LANG)\\n if create_issue == 0:\\n yield (\\\"\\\\n\\\".join(limit_result(list(updated_files.keys()))))\\n else:\\n yield (\\\"Processing....\\\")\\n new_count, skip_count = build_update_issue(trans, branch, updated_files)\\n yield (\\\"{} Issues had been created. {} Issues had been skipped.\\\".format(\\n new_count, skip_count))\\n yield (\\\"Please cache issues again.\\\")\",\n \"def find_files(\\n base,\\n languages=all_languages,\\n dir_ignore=default_dir_ignore,\\n file_ignore=default_file_ignore,\\n):\\n if base[-1] != \\\"/\\\":\\n base += \\\"/\\\"\\n\\n def update_dirs(dirs):\\n \\\"\\\"\\\"strip the ignored directories out of the provided list\\\"\\\"\\\"\\n index = len(dirs) - 1\\n for i, d in enumerate(reversed(dirs)):\\n if d in dir_ignore:\\n del dirs[index - i]\\n\\n # walk over base\\n for root, dirs, files in os.walk(base):\\n root = root.replace(base, \\\"\\\", 1)\\n\\n # strip ignored directories from the list\\n update_dirs(dirs)\\n\\n for filename in files:\\n if filename in file_ignore:\\n # skip ignored files\\n continue\\n\\n # try to figure out the language of the specified file\\n fullpath = os.path.join(base, root, filename)\\n language = lang_type(fullpath)\\n\\n # if the file is one of the langauges that we want return\\n # its name and the language\\n if language in languages:\\n yield fullpath, language\",\n \"def _get_changed_files():\\n if not ci_diff_helper:\\n return None\\n\\n try:\\n config = ci_diff_helper.get_config()\\n except OSError: # Not on CI.\\n return None\\n\\n changed_files = ci_diff_helper.get_changed_files('HEAD', config.base)\\n\\n changed_files = set([\\n './{}'.format(filename) for filename in changed_files])\\n\\n return changed_files\",\n \"def _findfile(self,path,label):\\n files=[];filenames=os.listdir(path)\\n for name in filenames:\\n if os.path.splitext(name)[0]==str(label):\\n files.append(name)\\n return files\",\n \"def test_case_3():\\n print(\\\"*********Test_case_3***********\\\")\\n result = find_files('.c', None)\\n print(result)\",\n \"def GetUnknownFiles(self):\\n args = []\\n status = RunShell([\\\"hg\\\", \\\"status\\\", \\\"--rev\\\", self.base_rev, \\\"-u\\\", \\\".\\\"],\\n silent_ok=True)\\n unknown_files = []\\n for line in status.splitlines():\\n st, fn = line.split(\\\" \\\", 1)\\n if st == \\\"?\\\":\\n unknown_files.append(fn)\\n return unknown_files\",\n \"def _bbcomponentwalk(self):\\n for path, subdirs, files in os.walk(self.oedir()):\\n try:\\n del subdirs[subdirs.index('.svn')]\\n\\n except ValueError:\\n pass\\n \\n for file in files:\\n pf, ext = os.path.splitext(file)\\n if ext == '.bb':\\n yield pf.partition('_')[0]\",\n \"def _git_diff_files(ref=\\\"master\\\"):\\n result = []\\n command = [\\\"git\\\", \\\"diff\\\", \\\"--name-status\\\", \\\"%s\\\" % (ref)]\\n exit_code, output = _execute(command)\\n if exit_code != 0:\\n print(\\\"Failed to diff files.\\\")\\n sys.exit(1)\\n\\n for line in output.decode(\\\"utf-8\\\").splitlines():\\n parts = line.split(\\\"\\\\t\\\")\\n action = parts[0]\\n name = parts[-1]\\n action = action.lower()\\n result.append((action, name))\\n\\n return result\",\n \"def detect_changed_files(self) -> list[Path]:\\n repos = [(self.open_repo(), self.git_directory)]\\n # Check server and api dirs too\\n # Normally these are ignored but we need to check these\\n if (server_repo_path := Path(self.git_directory, \\\"Paper-Server\\\")).exists():\\n repos.append((pygit2.Repository(str(server_repo_path)), server_repo_path))\\n if (api_repo_path := Path(self.git_directory, \\\"Paper-API\\\")).exists():\\n repos.append((pygit2.Repository(str(api_repo_path)), api_repo_path))\\n changed = []\\n for repo, repo_path in repos:\\n changed.extend(p.relative_to(self.git_directory) for p in detect_changed_files(repo, repo_path))\\n changed.sort()\\n return changed\",\n \"def find_new_files_in(self, msg, branch, create_issue):\\n self._asset_bind(msg)\\n trans = self._translation_util(msg)\\n new_file_list = trans.find_new_files(\\n REPOSITORY_NAME, branch, TARGET_LANG)\\n\\n if create_issue == 0:\\n yield (\\\"\\\\n\\\".join(limit_result(new_file_list)))\\n else:\\n yield (\\\"Processing....\\\")\\n new_count, skip_count = build_new_issue(trans, branch, new_file_list)\\n yield (\\\"{} Issues had been created. {} Issues had been skipped.\\\".format(\\n new_count, skip_count))\\n yield (\\\"Please cache issues again.\\\")\",\n \"def find_reference_files():\\n for root, _, files in os.walk(\\\"./tests/references/\\\"):\\n for basename in fnmatch.filter(files, \\\"*.tex\\\"):\\n yield os.path.join(root, basename)\",\n \"def retrieve_modified_files(self):\\n result = [(diff_obj.a_path, diff_obj.b_path)\\n for diff_obj in self.repo.index.diff(None)]\\n\\n return result\",\n \"def calc_lang_features(commits, author):\\n\\tlang_features = ['/\\\\*\\\\*', '\\\\\\\\\\\"\\\\\\\\\\\"\\\\\\\\\\\"', '///', # documentation\\n\\t\\t\\t'^\\\\s*@', 'def.+:.+->', 'using\\\\s+System\\\\.ComponentModel\\\\.DataAnnotations', # assertion\\n\\t\\t\\t'assert', 'TODO', 'lambda']\\n\\n\\t# delete contents\\n\\topen('lang_features.csv', 'w').close()\\n\\t\\n\\tfor count, commit in enumerate(commits):\\n\\t\\t# status update\\n\\t\\tif (count + 1) % 5 == 0:\\n\\t\\t\\tprint commit, '.. ..', count + 1, ' / ', len(commits)\\n\\n\\n\\t\\t\\t# for each blob modified\\n\\t\\tquery = (\\\"for x in $(echo \\\" + commit + \\\" | ssh da4 ~/lookup/cmputeDiff2.perl); do \\\" +\\n\\t\\t\\t\\t# get the chold and parent blob\\n\\t\\t\\t\\t\\\"diff_blobs=$(echo $x | awk -v RS=';' 1 | sed -n '3,4 p');\\\" +\\n\\t\\t\\t\\t# if a parent blob does not exist, the author authored all of the content of the file\\n\\t\\t\\t\\t\\\"if [ $(echo $diff_blobs|wc -w) -eq 1 ]; then \\\" +\\n\\t\\t\\t\\t\\t\\\"echo $diff_blobs | ~/lookup/showCnt blob 2> /dev/null; \\\" +\\n\\t\\t\\t\\t# if a parent blob exists, find the diff, in order to search only the modified lines\\n\\t\\t\\t\\t\\\"elif [ $(echo $diff_blobs|wc -w) -eq 2 ]; then \\\" +\\n\\t\\t\\t\\t\\t\\\"vars=( $diff_blobs );\\\" +\\n\\t\\t\\t\\t\\t# using bash instead of sh in order to use the process substitution,\\n\\t\\t\\t\\t\\t# to get the modified lines\\n\\t\\t\\t\\t\\t\\\"/bin/bash -c \\\\\\\"diff <(echo ${vars[0]} | ~/lookup/showCnt blob)\\\" +\\n\\t\\t\\t\\t\\t\\t\\t\\t\\\" <(echo ${vars[1]} | ~/lookup/showCnt blob)\\\\\\\";\\\" +\\n\\t\\t\\t\\t\\\"fi;\\\" +\\n\\t\\t\\t# grep the above practices and discard the lines that were deleted from the parent blob\\n\\t\\t\\t# (they start with \\\">\\\" in diff)\\n\\t\\t\\t\\\"done | egrep \\\\\\\"\\\" + \\\"|\\\".join(lang_features) + \\\"\\\\\\\" | grep -v '^>' | wc -l \\\")\\n\\t\\tcount_uses = int(bash(query).strip())\\n\\t\\tif count_uses > 0: # good practice feature is used\\n\\t\\t\\tout = bash('echo ' + commit + ' | ~/lookup/getValues c2P')\\n\\t\\t\\tmain_proj = out.strip().split(';')[1]\\n\\t\\t\\ttime = search(commit, 'commit')[2]\\n\\n\\t\\t\\tf = open(\\\"lang_features.csv\\\", \\\"a\\\")\\n\\t\\t\\tprint 'lang_f'\\n\\t\\t\\tf.write(author + ', ' + 'LANG_F' + ', ' + str(time) + ', ' + main_proj + ', ' + str(count_uses) + '\\\\n')\\n\\t\\t\\tf.close()\\n\\t\\t\\tprint 'wrote: -->', commit\",\n \"def diff_files(self, added_deleted, path=(), pars=()):\\n diffs = []\\n for key, selection in self.selections.items():\\n if self._is_normal_value(key):\\n diffs.extend(selection.diff_files(added_deleted, path + (key,), pars + (self.diff_name,)))\\n else:\\n delete_special = selectors.DiffTuple(\\n * (path + ((self.filename,), (key,),\\n added_deleted + \\\" \\\" + repr(self._value_name(key)))),\\n parameter_names = pars + (self.diff_name, self.parkey, \\\"DIFFERENCE\\\",))\\n diffs.append(delete_special)\\n return diffs\",\n \"def test_case_5():\\n print(\\\"*********Test_case_5***********\\\")\\n result = find_files('.c', \\\"\\\")\\n print(result)\",\n \"def file_changes(self):\\n new = []\\n changed = []\\n deleted = []\\n parent = self.parent_tag\\n # Loop through the files and find the ones that have changed\\n for relative_path, file_dict in self.checksum[\\\"files\\\"].items():\\n if relative_path not in parent[\\\"files\\\"]:\\n new.append(relative_path)\\n elif file_dict[\\\"checksum\\\"] != parent[\\\"files\\\"][relative_path][\\\"checksum\\\"]:\\n changed.append(relative_path)\\n # Loop through the parent files and see which files have been deleted\\n for relative_path in parent[\\\"files\\\"].keys():\\n if relative_path not in self.checksum[\\\"files\\\"]:\\n deleted.append(relative_path)\\n return {\\\"new\\\": new, \\\"changed\\\": changed, \\\"deleted\\\": deleted}\",\n \"def get_changed_files(path_to_repository, ignore_subrepositories):\\n diff = _get_diff_to_last_commit(path_to_repository, ignore_subrepositories)\\n return [item.b_path for item in diff if item.change_type in _CHANGE_TYPES_CONSIDERED_FOR_PRECOMMIT]\",\n \"def get_deleted_objects():\\n main_objects = set()\\n deleted_objects = []\\n for _, dirs, files in os.walk(os.getcwd()):\\n for d in dirs:\\n obj_path = get_path_outside_wit(d)\\n if obj_path:\\n main_objects.add(d)\\n for f in files:\\n obj_path = get_path_outside_wit(f)\\n if obj_path:\\n main_objects.add(f)\\n for o in staging_obj_names:\\n if o not in main_objects:\\n deleted_objects.append(o)\\n return deleted_objects\",\n \"def GetUnknownFiles(self):\\r\\n args = []\\r\\n status = RunShell([\\\"hg\\\", \\\"status\\\", \\\"--rev\\\", self.base_rev, \\\"-u\\\", \\\".\\\"],\\r\\n silent_ok=True)\\r\\n unknown_files = []\\r\\n for line in status.splitlines():\\r\\n st, fn = line.split(\\\" \\\", 1)\\r\\n if st == \\\"?\\\":\\r\\n unknown_files.append(fn)\\r\\n return unknown_files\",\n \"def directory_find(root, word):\\n for path, dirs, files in os.walk(root):\\n if word in dirs:\\n return os.path.join(path, word)\",\n \"def getFiles(folderToProcess,filter):\\n\\n print(f\\\"Parsing {folderToProcess} for {filter} files\\\")\\n\\n if debug:\\n for path in Path(folderToProcess).rglob(filter):\\n print(f\\\"Found {path}\\\")\\n\\n all_files = [str(x) for x in Path(folderToProcess).rglob(filter)] \\n\\n return all_files\",\n \"def find_files(suffix, path):\\n returnList = []\\n find_file_recursive(suffix, path, returnList)\\n return returnList\",\n \"def diff_files(self, added_deleted, path=(), pars=()):\\n body_diffs = self.selector.flat_diff(added_deleted + \\\" terminal\\\",\\n path = path + ((self.filename,)), pars = pars + (self.diff_name,))\\n for diff in body_diffs:\\n diff.instrument = self.instrument\\n diff.filekind = self.filekind\\n return body_diffs\",\n \"def test_case_6():\\n print(\\\"*********Test_case_6***********\\\")\\n path = os.path.join(os.path.dirname(__file__), 'testdir', 't1.c')\\n result = find_files('.c', path)\\n print(result)\",\n \"def clean_files(folder=\\\".\\\", posreg='.*[.]((py)|(rst))$',\\n negreg=\\\".*[.]git/.*\\\", op=\\\"CR\\\", fLOG=print):\\n def clean_file_cr(name):\\n with open(name, \\\"rb\\\") as f:\\n content = f.read()\\n new_content = content.replace(b\\\"\\\\r\\\\n\\\", b\\\"\\\\n\\\")\\n if new_content != content:\\n with open(name, \\\"wb\\\") as f:\\n f.write(new_content)\\n return True\\n return False\\n\\n def clean_file_cr_back(name):\\n with open(name, \\\"rb\\\") as f:\\n lines = f.read().split(b'\\\\n')\\n new_lines = []\\n changes = False\\n for li in lines:\\n if not li.endswith(b'\\\\r'):\\n new_lines.append(li + b'\\\\r')\\n changes = True\\n else:\\n new_lines.append(li)\\n if changes:\\n with open(name, \\\"wb\\\") as f:\\n f.write(b'\\\\n'.join(new_lines))\\n return changes\\n\\n if op == 'CR':\\n clean_file = clean_file_cr\\n elif op == 'CRB':\\n clean_file = clean_file_cr_back\\n elif op == 'pep8':\\n from .code_helper import remove_extra_spaces_and_pep8\\n clean_file = remove_extra_spaces_and_pep8\\n else:\\n raise ValueError(f\\\"Unknown cleaning '{op}'.\\\")\\n\\n if posreg and isinstance(posreg, str):\\n posreg = re.compile(posreg)\\n if negreg and isinstance(negreg, str):\\n negreg = re.compile(negreg)\\n\\n res = []\\n for root, _, files in os.walk(folder):\\n for f in files:\\n full = os.path.join(root, f)\\n rel = os.path.relpath(full, folder)\\n fn = rel.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n if posreg is None or posreg.search(fn):\\n if negreg is None or not negreg.search(fn):\\n r = clean_file(full)\\n if r and fLOG:\\n fLOG(f\\\"[clean_files] processed '{fn}'\\\")\\n res.append(rel)\\n return res\",\n \"def find_files(substring, path, recursive=False,\\n check_ext=None, ignore_invisible=True,\\n ignore_substring=None):\\n def check_file(f, path):\\n if not (ignore_substring and ignore_substring in f):\\n if substring in f:\\n compl_path = os.path.join(path, f)\\n if os.path.isfile(compl_path):\\n return compl_path\\n return False\\n\\n results = []\\n\\n if recursive:\\n for par, nxt, fnames in os.walk(path):\\n for f in fnames:\\n fn = check_file(f, par)\\n if fn:\\n results.append(fn)\\n\\n else:\\n for f in os.listdir(path):\\n if ignore_invisible and f.startswith('.'):\\n continue\\n fn = check_file(f, path)\\n if fn:\\n results.append(fn)\\n\\n if check_ext:\\n results = [r for r in results if os.path.splitext(r)[-1] == check_ext]\\n\\n return results\",\n \"def get_files_from_git() -> Sequence[Path]:\\n\\n def get_files(cmd: str) -> Sequence[str]:\\n output = subprocess.check_output(cmd, shell=True)\\n return [os.fsdecode(x) for x in output.splitlines()]\\n\\n root = os.fsdecode(subprocess.check_output(\\\"git rev-parse --show-toplevel\\\", shell=True).strip())\\n result: Set[str] = set()\\n result.update(get_files(\\\"git diff --name-only --diff-filter=ACM --staged\\\"))\\n result.update(get_files(\\\"git diff --name-only --diff-filter=ACM\\\"))\\n result.update(get_files(\\\"git ls-files -o --full-name --exclude-standard\\\"))\\n return sorted(Path(root, x) for x in result)\",\n \"def changed_files(conn, scan0, scan1):\\n c = conn.cursor()\\n c.execute(\\\"SELECT a.pathid as pathid, a.hashid, b.hashid FROM \\\" +\\n \\\"( SELECT pathid, hashid, scanid FROM files WHERE scanid=?) AS 'a' \\\" +\\n \\\"JOIN (SELECT pathid, hashid, scanid FROM FILES WHERE scanid=?) as 'b' \\\" +\\n \\\"ON a.pathid=b.pathid WHERE a.hashid != b.hashid\\\",\\n (scan0, scan1))\\n return (DBFile(f) for f in c)\",\n \"def find_dcds(src):\\n\\n dcd_paths = []\\n\\n for root, dirs, files in os.walk(src):\\n for filename in files:\\n if filename.endswith(\\\".dcd\\\"):\\n dcd_paths.append(os.path.join(root, filename))\\n\\n return dcd_paths\",\n \"def _find_train_ckptfiles(path, is_delete):\\n # if not exists(path):\\n # return None, -1\\n steps = [int(f[len(ckpt_prefix):-5]) for f in os.listdir(path)\\n if f[:len(ckpt_prefix)] == ckpt_prefix and f[-5:] == '.meta']\\n if len(steps) == 0:\\n if is_delete:\\n raise FileNotFoundError('No Available ckpt.')\\n else:\\n return None, -1\\n max_step = max(steps)\\n if len(steps) > 5 and is_delete:\\n del_model_files = _get_model_files(sorted(steps)[:-5], path)\\n for del_model_file in del_model_files:\\n os.remove(path + del_model_file)\\n\\n model_files = _get_model_files(max_step, path)\\n return model_files, max_step\",\n \"def search(pattern='*',sub='',vol=default,mirror=False):\\n base = path(sub=sub,vol=vol,mirror=mirror)\\n return sorted([f.relative_to(base) for f in base.glob(pattern)])\",\n \"def _search(self, log, progressbar):\\n self._urls = []\\n for filename in os.listdir(self._path):\\n url = 'file:////' + filename\\n self._urls.append(url)\\n self._urls.sort()\",\n \"def GetContentfilesDatabase(\\n language: scrape_repos_pb2.LanguageToClone,\\n) -> contentfiles.ContentFiles:\\n d = pathlib.Path(language.destination_directory)\\n d = d.parent / (str(d.name) + \\\".db\\\")\\n return contentfiles.ContentFiles(f\\\"sqlite:///{d}\\\")\",\n \"def _findChangedFiles(self):\\n changedFiles = []\\n # calculate and update checksums always for ALL files\\n for observedFile in self.observedFiles:\\n if os.path.isfile(observedFile.filePath):\\n currentChecksum = checksumFile(observedFile.filePath)\\n else:\\n currentChecksum = None\\n # different values with None value checking\\n if ((observedFile.lastChecksum is None\\n and currentChecksum is not None)\\n or observedFile.lastChecksum != currentChecksum):\\n changedFiles.append(observedFile) # notify change\\n observedFile.lastChecksum = currentChecksum # update checksum\\n\\n return changedFiles\",\n \"def get_sql_files(root=\\\".\\\", filterl=None, exclude=None):\\r\\n paths = Path(root).rglob(\\\"*.sql\\\")\\r\\n\\r\\n if root:\\r\\n if filterl:\\r\\n return [\\r\\n str(path)\\r\\n for path in paths\\r\\n if any(\\r\\n re.match(path.parts[-2], name, re.IGNORECASE) for name in filterl\\r\\n )\\r\\n ]\\r\\n if exclude:\\r\\n return [\\r\\n str(path)\\r\\n for path in paths\\r\\n if not any(\\r\\n re.match(path.parts[0], name, re.IGNORECASE) for name in exclude\\r\\n )\\r\\n ]\\r\\n return [str(path) for path in Path(root).rglob(\\\"*.sql\\\")]\\r\\n return [str(path) for path in paths]\",\n \"def findfiles(path, ext=\\\".pyc\\\"):\\n results = []\\n regex = re.compile(re.escape(ext)+\\\"$\\\", re.I)\\n\\n tree = os.walk(path)\\n for d in tree:\\n # Each element of a walker represents a directory and its contents.\\n # Diagnostic, if you wish.\\n #print(d)\\n if d[2]:\\n # Are there files in this directory?\\n for f in d[2]:\\n if regex.findall(f):\\n relpath = os.path.join(d[0], f)\\n results.append(os.path.realpath(relpath))\\n\\n return results\",\n \"def filter_files(files, lang=EXTES):\\n\\n lang_specific = []\\n\\n for f in files:\\n if f.endswith(lang):\\n lang_specific.append(f)\\n return lang_specific\",\n \"def get_changed_files_from(old_commit_sha, new_commit_sha):\\n return check_output(\\n \\\"git diff-tree --no-commit-id --name-only -r {0}..{1}\\\".format(\\n old_commit_sha,\\n new_commit_sha\\n ).split(\\\" \\\")\\n ).decode('utf-8').strip()\",\n \"def get_files_from_of_type(path: str, ext: str) -> List[str]:\\n files = []\\n for root, dirnames, filenames in os.walk(path):\\n for filename in fnmatch.filter(filenames, \\\"*.\\\" + str(ext)):\\n files.append(os.path.join(root, filename))\\n if not files:\\n logging.error(\\\"No language files found in folder: \\\" + str(os.sep.join([convert_vars.BASE_PATH, \\\"source\\\"])))\\n logging.debug(f\\\" --- found {len(files)} files of type {ext}. Showing first few:\\\\n* \\\" + str(\\\"\\\\n* \\\".join(files[:3])))\\n return files\",\n \"def _find_git_files(dirname='', git_dir=None):\\n file_list = []\\n if git_dir is None:\\n git_dir = pbr.git._run_git_functions()\\n if git_dir:\\n file_list = pbr.git._run_git_command(['ls-files', '-z'], git_dir)\\n file_list += pbr.git._run_git_command(\\n ['submodule', 'foreach', '--quiet', 'ls-files', '-z'],\\n git_dir\\n )\\n # Users can fix utf8 issues locally with a single commit, so we are\\n # strict here.\\n file_list = file_list.split(b'\\\\x00'.decode('utf-8'))\\n submodules = _get_submodules(git_dir)\\n return [f for f in file_list if f and f not in submodules]\",\n \"def getContentFiles():\\n contentFiles = []\\n for contentDir, subDirs, filenames in os.walk(sourceDir, followlinks=True):\\n if shouldIgnore(contentDir):\\n subDirs[:] = []\\n continue\\n for filename in filenames:\\n if not shouldIgnore(filename):\\n cf = ContentFile(os.path.join(contentDir, filename))\\n log(`cf.path`)\\n contentFiles.append(cf)\\n return contentFiles\",\n \"def find_langs(args):\\n infile = args.source\\n langs = {}\\n for line in infile:\\n name_and_files = line.split()\\n name = name_and_files[0]\\n if name not in langs:\\n langs[name] = []\\n langs[name] += read_files.filter_files(name_and_files[1:])\\n langs[args.unknown] += read_files.filter_files(args.classify)\\n return langs\",\n \"def scan_lang_folder(src_dir: Path, build_dir: Optional[Path],\\n lang: Language, mode: TestMode) -> List[Path]:\\n lang_dir = f\\\"epi_judge_{lang.value}\\\"\\n if mode == TestMode.SOLUTION:\\n lang_dir += '_solutions'\\n src_dir = src_dir / lang_dir\\n\\n if lang == Language.PYTHON:\\n return sorted([f for f in src_dir.glob(\\\"*.py\\\")\\n if find_str_in_file(f, 'generic_test_main')])\\n elif lang == Language.CPP:\\n if not build_dir:\\n raise RuntimeError('Missing --build-dir')\\n solution_files = (f.with_suffix('').name for f in src_dir.glob(\\\"*.cc\\\")\\n if find_str_in_file(f, 'GenericTestMain'))\\n EXCLUDED_FILES = ['queue_with_max_using_deque', 'reverse_list']\\n solution_files = sorted([build_dir / f for f in solution_files\\n if f not in EXCLUDED_FILES])\\n if os.name == 'nt':\\n return [f.with_suffix('.exe') for f in solution_files]\\n else:\\n return solution_files\\n elif lang == Language.JAVA:\\n EXCLUDED_FILES = ['QueueWithMaxUsingDeque.java', 'ReverseList.java']\\n return sorted([\\n f for f in (src_dir / 'epi').glob(\\\"*.java\\\") if\\n (find_str_in_file(f, '@EpiTest') and f.name not in EXCLUDED_FILES)\\n ])\\n\\n raise NotImplementedError()\",\n \"def _get_relevant_files(self, local_repo_path):\\n r = GitRepo(local_repo_path)\\n all_commits = r.git.log('--name-only', '--pretty=format:').split()\\n counted_commits = Counter(all_commits)\\n # Sort the files according to the number of commits they appear in\\n sorted_commits = sorted(counted_commits.items(),\\n key=lambda x: x[1],\\n reverse=True)\\n # Return the file names sorted per commits number\\n return list(zip(*sorted_commits))[0]\",\n \"def checkGit(directory):\",\n \"def get_filepaths(keyword, directory):\\n \\n matches = []\\n filenames_total = []\\n \\n for root, dirnames, filenames in os.walk(directory):\\n for filename in filenames:\\n if keyword in filename:\\n matches.append(root + '/' + filename)\\n filenames_total.append(filename)\\n return matches, filenames_total\",\n \"def search_file(repos, path_parts, rev, search_re):\\n\\n # Read in each line of a checked-out file, and then use re.search to\\n # search line.\\n fp = repos.openfile(path_parts, rev, {})[0]\\n matches = 0\\n while 1:\\n line = fp.readline()\\n if not line:\\n break\\n # FIXME: Is there what can we do about file encoding?\\n if search_re.search(line.decode(\\\"utf-8\\\", \\\"surrogateescape\\\")):\\n matches = 1\\n fp.close()\\n break\\n return matches\",\n \"def get_path_to_github_files(github_dir):\\n path_to_files = {}\\n for r, d, f in os.walk(github_dir):\\n if '.git' in r or '_tutorial' in r:\\n continue\\n for folder in d:\\n if folder == '.git' or folder == '_tutorial':\\n continue\\n path = os.path.join(r, folder)\\n path_to_files[path] = os.listdir(path)\\n filtered_path_to_files = {}\\n for path in path_to_files:\\n if DESCRIPTION_FILE in path_to_files[path]:\\n filtered_path_to_files[path] = path_to_files[path]\\n return filtered_path_to_files\",\n \"def find_legacy_log_files(xcresult_path):\\n\\n result = []\\n\\n for root, dirs, files in os.walk(xcresult_path, topdown=True):\\n for file in files:\\n if file.endswith('.txt'):\\n file = os.path.join(root, file)\\n result.append(file)\\n\\n # Sort the files by creation time.\\n result.sort(key=lambda f: os.stat(f).st_ctime)\\n return result\",\n \"def find_legacy_log_files(xcresult_path):\\n\\n result = []\\n\\n for root, dirs, files in os.walk(xcresult_path, topdown=True):\\n for file in files:\\n if file.endswith('.txt'):\\n file = os.path.join(root, file)\\n result.append(file)\\n\\n # Sort the files by creation time.\\n result.sort(key=lambda f: os.stat(f).st_ctime)\\n return result\",\n \"def find(self, file, version):\\n matches = []\\n for root, dirnames, filenames in os.walk(self.full_doc_path(version)):\\n for filename in fnmatch.filter(filenames, file):\\n matches.append(os.path.join(root, filename))\\n return matches\",\n \"def coffeescript_files():\\r\\n dirs = \\\" \\\".join(THEME_COFFEE_PATHS + [Env.REPO_ROOT / coffee_dir for coffee_dir in COFFEE_DIRS])\\r\\n return cmd('find', dirs, '-type f', '-name \\\\\\\"*.coffee\\\\\\\"')\",\n \"def all_changed_files(self):\\n return [path_to_file_type(os.path.join(self.path, p)) for p in self.changed_paths() if p]\",\n \"def process_files(file_location, day):\\n # construct file path\\n file_dir = PREFIX+file_location\\n file_pattern = file_dir+'lz_'+day+'*_raw.root'\\n # print(file_pattern)\\n file_list = glob.glob(file_pattern)\\n print(\\\"There are %s MC files in the requested directory (%s).\\\" %(len(file_list), file_dir))\\n file_names = []\\n for f in file_list:\\n file_name_only = f.split('/')\\n file_names.append(file_name_only[-1])\\n return file_names\",\n \"async def get_files_for_menu(language: str):\\n menu_query = menu_queries.QUERY_FILES_FOR_LANGUAGE\\n current_bind_vars = {\\\"language\\\": language}\\n if language == \\\"multi\\\":\\n menu_query = menu_queries.QUERY_FILES_FOR_MULTILANG\\n current_bind_vars = {}\\n try:\\n language_menu_query_result = get_db().AQLQuery(\\n query=menu_query,\\n batchSize=10000,\\n bindVars=current_bind_vars\\n )\\n return {\\\"result\\\": language_menu_query_result.result}\\n\\n except DocumentNotFoundError as error:\\n print(error)\\n raise HTTPException(status_code=404, detail=\\\"Item not found\\\") from error\\n except AQLQueryError as error:\\n print(\\\"AQLQueryError: \\\", error)\\n raise HTTPException(status_code=400, detail=error.errors) from error\\n except KeyError as error:\\n print(\\\"KeyError: \\\", error)\\n raise HTTPException(status_code=400) from error\",\n \"def createFileList(my_dir, term):\\n \\n for subdir, dirs, files in os.walk(my_dir):\\n for file in files:\\n if re.search(re.escape(term), file):\\n result = os.path.join(subdir, file)\\n fileList.append(result)\\n return fileList\",\n \"def retrieve_tracked_files(self):\\n result = []\\n\\n for key in self.repo.index.entries.keys():\\n\\n result.append(os.path.join(self.repo.working_dir, key[0]))\\n\\n return result\",\n \"def unix_find(pathin):\\n return [os.path.join(path, file)\\n for (path, dirs, files) in os.walk(pathin, followlinks=False)\\n for file in files]\",\n \"def _get_target_files(self) -> List[Path]:\\n repo = get_git_repo()\\n submodules = repo.submodules # type: ignore\\n submodule_paths = [\\n self._fname_to_path(repo, submodule.path) for submodule in submodules\\n ]\\n\\n # resolve given paths relative to current working directory\\n paths = [p.resolve() for p in self._paths]\\n if self._base_commit is not None:\\n paths = [\\n a\\n for a in (self._status.added + self._status.modified)\\n # diff_path is a subpath of some element of input_paths\\n if any((a == path or path in a.parents) for path in paths)\\n ]\\n changed_count = len(paths)\\n click.echo(f\\\"| looking at {unit_len(paths, 'changed path')}\\\", err=True)\\n paths = [\\n path\\n for path in paths\\n if all(\\n submodule_path not in path.parents\\n for submodule_path in submodule_paths\\n )\\n ]\\n if len(paths) != changed_count:\\n click.echo(\\n f\\\"| skipping files in {unit_len(submodule_paths, 'submodule')}: \\\"\\n + \\\", \\\".join(str(path) for path in submodule_paths),\\n err=True,\\n )\\n\\n # Filter out ignore rules, expand directories\\n self._ignore_rules_file.seek(0)\\n patterns = Parser(self._base_path).parse(self._ignore_rules_file)\\n\\n file_ignore = FileIgnore(\\n base_path=self._base_path, patterns=patterns, target_paths=paths\\n )\\n\\n walked_entries = list(file_ignore.entries())\\n click.echo(\\n f\\\"| found {unit_len(walked_entries, 'file')} in the paths to be scanned\\\",\\n err=True,\\n )\\n filtered: List[Path] = []\\n for elem in walked_entries:\\n if elem.survives:\\n filtered.append(elem.path)\\n\\n skipped_count = len(walked_entries) - len(filtered)\\n if skipped_count:\\n click.echo(\\n f\\\"| skipping {unit_len(range(skipped_count), 'file')} based on path ignore rules\\\",\\n err=True,\\n )\\n\\n relative_paths = [path.relative_to(self._base_path) for path in filtered]\\n\\n return relative_paths\",\n \"def find_new_files(self):\\r\\n in_file_system = self._find_data_files(self.basepath,\\r\\n self.data_file_extension, [])\\r\\n status_msg('Search for data files. Found: {0}'.format(\\r\\n len(in_file_system)), True\\r\\n )\\r\\n status_msg('Check that there are no duplicate filenames', True)\\r\\n in_db = self._get_files_in_db()\\r\\n status_msg('Get list of files in data base. Found: {0}'.format(\\r\\n len(in_db)), True\\r\\n )\\r\\n for filename in in_file_system:\\r\\n # Filename has been added to the database both where a part of the\\r\\n # path was a part of the unique name and where it was not,\\r\\n # therefore we need to check for both AND we ignore filenames that\\r\\n # start with IGNORE\\r\\n if form_unique_name(filename) not in in_db and\\\\\\r\\n os.path.basename(filename) not in in_db and not\\\\\\r\\n os.path.basename(filename).startswith('IGNORE'):\\r\\n self.new_data_files.append(filename)\\r\\n\\r\\n print # Adds a new line to the statusses before parsing\\r\\n status_msg('Collecting new files. Found: {0}'.format(\\r\\n len(self.new_data_files)), True\\r\\n )\",\n \"def get_git_project_files():\\n cached_and_untracked_files = git_ls_files(\\n '--cached', # All files cached in the index\\n '--others', # Untracked files\\n # Exclude untracked files that would be excluded by .gitignore, etc.\\n '--exclude-standard')\\n uncommitted_deleted_files = git_ls_files('--deleted')\\n\\n # Since sorting of files in a set is arbitrary, return a sorted list to\\n # provide a well-defined order to tools like flake8, etc.\\n return sorted(cached_and_untracked_files - uncommitted_deleted_files)\",\n \"def find_files(root, directory, filename):\\n\\n path_list = []\\n walker = os.walk(root, followlinks=True)\\n for root, dirs, files in walker:\\n remove_vcs_dirs(dirs)\\n\\n #if dirs containt 'directory', don't walk others\\n if directory in dirs: dirs[:] = [directory]\\n\\n if root.endswith(os.path.sep + directory):\\n if filename in files:\\n path_list.append(os.path.join(root, filename))\\n dirs[:] = []\\n\\n return path_list\",\n \"def directory(mirror):\\n # Download the directory of files from the webpage for a particular language.\\n parser = ParseDirectory()\\n directory = urllib2.urlopen(mirror)\\n parser.feed(directory.read().decode('utf-8'))\\n # Extract the filenames of each XML meta history file.\\n meta = re.compile('^[a-zA-Z-]+wiki-latest-pages-meta-history.*\\\\.bz2$')\\n return [(mirror, filename) for filename in parser.files if meta.match(filename)]\",\n \"def find_build(branch, tag, build_path=None, old_build_path=None):\\n for directory_format in [build_path, old_build_path]:\\n if directory_format is None:\\n continue\\n loc = directory_format % (branch, tag)\\n if isdir(loc):\\n return loc\\n raise BuildNotFound(branch, tag)\",\n \"def filter_changed_files(changed_files, path_to_repository, file_encoding):\\n filtered_files = []\\n for changed_file in changed_files:\\n file_is_valid = True\\n if (os.path.isdir(os.path.join(path_to_repository, changed_file))):\\n #ignore directories (e.g., git submodule folders)\\n continue\\n if os.path.getsize(os.path.join(path_to_repository, changed_file)) > 1 * 1024 * 1024:\\n print('File too large for precommit analysis. Ignoring: %s' % changed_file)\\n file_is_valid = False\\n\\n try:\\n with open(os.path.join(path_to_repository, changed_file), encoding=file_encoding) as file:\\n file.read()\\n except UnicodeDecodeError:\\n encoding_string = file_encoding\\n if encoding_string is None:\\n encoding_string = locale.getpreferredencoding() + ' (system encoding)'\\n\\n print(\\n 'File at %s is not encoded in %s. Try using the --file-encoding option.' % (\\n changed_file, encoding_string))\\n file_is_valid = False\\n\\n if file_is_valid:\\n filtered_files.append(changed_file)\\n\\n return filtered_files\",\n \"def list_all_files(self, branch_name) -> dict:\\n try:\\n files_response = self.repo.get_contents(self.repo_folder,\\n ref=branch_name)\\n files_list = []\\n for file in files_response:\\n files_list.append(file.raw_data.get('path'))\\n return make_success_response(200, files_list)\\n except GithubException as github_exc:\\n return make_error_response(github_exc.status, github_exc.data)\",\n \"def _get_file_list(folder):\\n tree = [x for x in os.walk(folder)]\\n files = [os.path.join(t[0], y) for t in tree for y in t[2]]\\n return [os.path.relpath(x, start=folder)\\n for x in files if x != LOCAL_METADATA_FILE]\",\n \"def compare_tree(self):\\n result = []\\n \\n pathA = os.path.join(self.testpath,'A')\\n pathB = os.path.join(self.testpath,'B')\\n\\n filesA = [os.path.relpath(f,pathA) for f in self.tree(pathA)]\\n filesB = [os.path.relpath(f,pathB) for f in self.tree(pathB)]\\n\\n filesAB = set(filesA).union(filesB)\\n for fileAB in sorted(list(filesAB)):\\n\\n fileA = os.path.join(self.testpath,'A',fileAB)\\n fileB = os.path.join(self.testpath,'B',fileAB)\\n try:\\n fileAtxt = open(fileA).read()\\n except IOError:\\n result.append( ('missing_inA',fileAB) )\\n continue\\n \\n try:\\n fileBtxt = open(fileB).read()\\n except IOError:\\n result.append( ('missing_inB',fileAB) )\\n continue\\n\\n if not fileAtxt == fileBtxt:\\n result.append( ('disagree',fileAB))\\n \\n return result\",\n \"def _FindLocations(input_api, search_regexes, files_to_check, files_to_skip):\\n def FilterFile(affected_file):\\n return input_api.FilterSourceFile(\\n affected_file,\\n files_to_check=files_to_check,\\n files_to_skip=files_to_skip)\\n\\n no_presubmit = r\\\"// no-presubmit-check\\\"\\n locations = []\\n for f in input_api.AffectedSourceFiles(FilterFile):\\n for line_num, line in f.ChangedContents():\\n for search_regex in search_regexes:\\n if (input_api.re.search(search_regex, line) and\\n not input_api.re.search(no_presubmit, line)):\\n locations.append(\\\" %s:%d\\\" % (f.LocalPath(), line_num))\\n break\\n return locations\",\n \"def files_changed(revish: Text,\\n ignore_rules: Optional[Sequence[Text]] = None,\\n include_uncommitted: bool = False,\\n include_new: bool = False\\n ) -> Tuple[List[Text], List[Text]]:\\n files = repo_files_changed(revish,\\n include_uncommitted=include_uncommitted,\\n include_new=include_new)\\n if not files:\\n return [], []\\n\\n return exclude_ignored(files, ignore_rules)\",\n \"def _mark_deleted(self):\\n\\n self.__class__ = DeletedCurrentMirrorDirectory\\n\\n for (cvs_path, id) in self._entries.iteritems():\\n if id in self.repo._new_nodes:\\n node = self[cvs_path]\\n if isinstance(node, _WritableMirrorDirectoryMixin):\\n # Mark deleted and recurse:\\n node._mark_deleted()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56908476","0.55866516","0.54323393","0.532697","0.5286356","0.5263294","0.52617866","0.5237295","0.5224555","0.5216255","0.52129155","0.52084595","0.5203285","0.5158452","0.5138303","0.5135574","0.5109658","0.51079535","0.51076376","0.51058733","0.50981826","0.5093252","0.5086958","0.508442","0.50733966","0.50648177","0.50508595","0.5034645","0.5032498","0.5028675","0.5016267","0.5015219","0.49837863","0.49761605","0.49759027","0.49752483","0.4966772","0.49640325","0.49628028","0.4958156","0.4947499","0.49473563","0.49383628","0.49352175","0.49349275","0.4928431","0.49280083","0.49262908","0.4919052","0.49029657","0.49021944","0.490211","0.4889664","0.48829922","0.48787344","0.48746675","0.48720947","0.48554763","0.48509052","0.4841559","0.4839998","0.4836982","0.48368555","0.4833637","0.48329714","0.48321477","0.48316243","0.4830295","0.48081148","0.4804229","0.47958684","0.47904742","0.47897506","0.47725248","0.47693887","0.47681615","0.47614512","0.47547397","0.47547397","0.47546625","0.47425565","0.47348225","0.47156465","0.47153732","0.4714426","0.47119394","0.47094965","0.47057024","0.47029245","0.46928906","0.46920514","0.4679371","0.46735308","0.46713206","0.4670756","0.4663064","0.46628523","0.46577385","0.4654994","0.46496716"],"string":"[\n \"0.56908476\",\n \"0.55866516\",\n \"0.54323393\",\n \"0.532697\",\n \"0.5286356\",\n \"0.5263294\",\n \"0.52617866\",\n \"0.5237295\",\n \"0.5224555\",\n \"0.5216255\",\n \"0.52129155\",\n \"0.52084595\",\n \"0.5203285\",\n \"0.5158452\",\n \"0.5138303\",\n \"0.5135574\",\n \"0.5109658\",\n \"0.51079535\",\n \"0.51076376\",\n \"0.51058733\",\n \"0.50981826\",\n \"0.5093252\",\n \"0.5086958\",\n \"0.508442\",\n \"0.50733966\",\n \"0.50648177\",\n \"0.50508595\",\n \"0.5034645\",\n \"0.5032498\",\n \"0.5028675\",\n \"0.5016267\",\n \"0.5015219\",\n \"0.49837863\",\n \"0.49761605\",\n \"0.49759027\",\n \"0.49752483\",\n \"0.4966772\",\n \"0.49640325\",\n \"0.49628028\",\n \"0.4958156\",\n \"0.4947499\",\n \"0.49473563\",\n \"0.49383628\",\n \"0.49352175\",\n \"0.49349275\",\n \"0.4928431\",\n \"0.49280083\",\n \"0.49262908\",\n \"0.4919052\",\n \"0.49029657\",\n \"0.49021944\",\n \"0.490211\",\n \"0.4889664\",\n \"0.48829922\",\n \"0.48787344\",\n \"0.48746675\",\n \"0.48720947\",\n \"0.48554763\",\n \"0.48509052\",\n \"0.4841559\",\n \"0.4839998\",\n \"0.4836982\",\n \"0.48368555\",\n \"0.4833637\",\n \"0.48329714\",\n \"0.48321477\",\n \"0.48316243\",\n \"0.4830295\",\n \"0.48081148\",\n \"0.4804229\",\n \"0.47958684\",\n \"0.47904742\",\n \"0.47897506\",\n \"0.47725248\",\n \"0.47693887\",\n \"0.47681615\",\n \"0.47614512\",\n \"0.47547397\",\n \"0.47547397\",\n \"0.47546625\",\n \"0.47425565\",\n \"0.47348225\",\n \"0.47156465\",\n \"0.47153732\",\n \"0.4714426\",\n \"0.47119394\",\n \"0.47094965\",\n \"0.47057024\",\n \"0.47029245\",\n \"0.46928906\",\n \"0.46920514\",\n \"0.4679371\",\n \"0.46735308\",\n \"0.46713206\",\n \"0.4670756\",\n \"0.4663064\",\n \"0.46628523\",\n \"0.46577385\",\n \"0.4654994\",\n \"0.46496716\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6073978"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":74597,"cells":{"query":{"kind":"string","value":"Train a torch model"},"document":{"kind":"string","value":"def train(get_generator, loss_fn, optimizer, num_epochs=1, monitor=None,\n on_epoch_start=None, on_finish=None, on_error=None):\n logger = logging.getLogger(__name__)\n\n t_begin_train = timer()\n for epoch in range(num_epochs):\n avg_loss = 0\n if on_epoch_start:\n on_epoch_start(epoch)\n\n data_loader = get_generator(epoch)\n num_steps = len(data_loader)\n\n t_start = timer()\n for batch_idx, data in enumerate(data_loader):\n if batch_idx % 500 == 0:\n avg_loss /= num_steps\n if monitor:\n monitor({'epoch': epoch,\n 'train_loss': float(avg_loss),\n 'batch': batch_idx})\n avg_loss = 0.0\n\n optimizer.zero_grad() # this is not done automatically in torch\n # pass all data args to loss_function\n loss = loss_fn(data)\n\n if torch.isnan(loss):\n logging.error(f\"Loss is nan on batch {batch_idx} and epoch \"\n f\"{epoch}. Raising floating point error\")\n if on_error:\n on_error(data)\n raise FloatingPointError\n\n loss.backward()\n optimizer.step()\n\n avg_loss += loss.data.cpu().numpy()\n\n if batch_idx % 200 == 99:\n t_end = timer()\n logger.debug(f\"{batch_idx}/{len(data_loader)} batches done. \"\n f\"Rate: {200/(t_end-t_start):.2f} batch/sec\")\n t_start = timer()\n\n logging.info(\"Done Training. Time elapsed {}\".format(timer()-t_begin_train))\n if on_finish:\n on_finish()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def train(train_loader : torch.utils.data.DataLoader, model : nn.Module, criterion : nn.Module, optimizer : torch.optim.Optimizer) -> logger.Result:","def trainNet():","def train(args, model, train_data_loader, dev_data_loader, device):\n\n\tmodel.train()\n\toptimizer = torch.optim.Adam(model.parameters())\n\tprint_loss_total = 0\n\tepoch_loss_total = 0\n\tstart = time.time()\n\n\t#### modify the following code to complete the training funtion\n\n\tbest_train_acc, best_dev_acc = 0.0, 0.0\n\n\tfor idx, batch in enumerate(train_data_loader):\n\t\tquestion_feature_vec = batch['feature_vec'].to(device)\n\t\tquestion_len = batch['len'].to(device)\n\t\tlabels = batch['labels'].to(device)\n\n\t\t#### Your code here ----\n\n\t\t# zero out\n\t\toptimizer.zero_grad()\n\n\t\t# get output from model\n\t\tlogits = model(question_feature_vec, question_len)\n\n\t\t# use loss_fn defined above to calculate loss\n\t\tloss = loss_fn(logits, labels)\n\n\t\t# use accuracy_fn defined above to calculate 'error' and number of examples ('num_examples') used to\n\t\t# calculate accuracy below.\n\t\terror, num_examples = accuracy_fn(logits, labels)\n\n\t\t# backprop\n\t\tloss.backward()\n\t\toptimizer.step()\n\n\t\t###Your code ends ---\n\t\taccuracy = 1 - error / num_examples\n\t\tclip_grad_norm_(model.parameters(), 5)\n\t\tprint_loss_total += loss.data.numpy()\n\t\tepoch_loss_total += loss.data.numpy()\n\n\t\tif (idx + 1) % args.checkpoint == 0 and idx > 0:\n\t\t\tprint_loss_avg = print_loss_total / args.checkpoint\n\n\t\t\tdev_acc = evaluate(dev_data_loader, model, device)\n\n\t\t\tprint('number of steps: %d, train loss: %.5f, train acc: %.3f, dev acc: %.3f, time: %.5f'\n\t\t\t % (idx + 1, print_loss_avg, accuracy, dev_acc, time.time() - start))\n\t\t\tprint_loss_total = 0\n\t\t\tif accuracy > best_train_acc:\n\t\t\t\tbest_train_acc = accuracy\n\t\t\tif dev_acc > best_dev_acc:\n\t\t\t\tbest_dev_acc = dev_acc\n\n\treturn best_train_acc, best_dev_acc","def run(self) -> None:\n self.model = self.trainer.train_model(self.model, self.data)","def train(model, optimizer: torch.optim, data: torch_geometric.data.Data):\n model.train()\n optimizer.zero_grad()\n F.nll_loss(model()[data.train_mask], data.y[data.train_mask]).backward()\n optimizer.step()\n\n model.eval()","def train(args, model, data_loader, optimizer):\n # Keep track of average accuracy and loss\n avg_loss = utils.AverageMeter()\n\n # Set training mode\n model.train()\n\n # for inputs, target in tqdm(data_loader):\n for inputs, targets in tqdm(data_loader):\n # Prep\n inputs = utils.wrap_variables(inputs, cuda=args.cuda)\n targets = utils.wrap_variables(targets, cuda=args.cuda)\n\n # Run forward\n predictions, log_likelihood = model(**inputs)\n\n # Loss = -NLL\n loss = -log_likelihood\n avg_loss.update(loss.data[0], len(inputs['x_tags']))\n\n # Run backward\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n return {'loss': avg_loss}","def train_model(self, *args, **kwargs):\n self.model.train(self.training, *args, **kwargs)","def train(self):\n\t\tself.model.fit(self.training_data, self.training_labels)","def train(args: Dict):\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n print('use device: %s' % device)\n\n train_data_src = read_corpus(args['--train-src'], source='src')\n train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')\n\n dev_data_src = read_corpus(args['--dev-src'], source='src')\n dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')\n\n train_data = list(zip(train_data_src, train_data_tgt))\n dev_data = list(zip(dev_data_src, dev_data_tgt))\n\n train_batch_size = int(args['--batch-size'])\n N = int(args['--N'])\n d_model = int(args['--d_model'])\n d_ff = int(args['--d_ff'])\n h = int(args['--h'])\n dropout = float(args['--dropout'])\n\n valid_niter = int(args['--valid-niter'])\n log_every = int(args['--log-every'])\n model_save_path = args['--save-to']\n lr=float(args['--lr'])\n\n vocab = Vocab.load(args['--vocab'])\n vocab_mask = torch.ones(len(vocab.tgt))\n vocab_mask[vocab.tgt['<pad>']] = 0\n\n model = make_model(len(vocab.src), len(vocab.tgt), N, d_model, d_ff, h, dropout)\n model = model.to(device)\n\n optimizer = NoamOpt(model.src_embed[0].d_model, 1, 400,\n torch.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.98), eps=1e-9))\n\n num_trial = 0\n train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0\n cum_exmaples = report_examples = epoch = valid_num = 0\n hist_valid_scores = []\n train_time = begin_time = time.time()\n print('begin Maximum Likelihood Training')\n\n while True:\n epoch += 1\n for src_sents, tgt_sents in batch_iter(train_data, batch_size=train_batch_size, shuffle=True):\n train_iter += 1\n optimizer.zero_grad()\n batch_size = len(src_sents)\n\n example_losses = - model(src_sents, tgt_sents) #(batch_size,)\n batch_loss = example_losses.sum()","def train():\n model.train()\n for batch_index, (xb, yb) in enumerate(train_dl):\n loss = loss_func(model(xb), yb)\n\n loss.backward()\n opt.step()\n opt.zero_grad()","def train(self, *args, **kwargs):\n # Handle overload of train() method\n if len(args) < 1 or (len(args) == 1 and type(args[0]) == bool):\n return nn.Sequential.train(self, *args, **kwargs)\n\n #\n # Parse training arguments\n #\n\n training_data = args[0]\n arguments = {\n \"validation_data\": None,\n \"batch_size\": 256,\n \"sigma_noise\": None,\n \"adversarial_training\": False,\n \"delta_at\": 0.01,\n \"initial_learning_rate\": 1e-2,\n \"momentum\": 0.0,\n \"convergence_epochs\": 5,\n \"learning_rate_decay\": 2.0,\n \"learning_rate_minimum\": 1e-6,\n \"maximum_epochs\": 1,\n \"training_split\": 0.9,\n \"gpu\": False,\n \"optimizer\": None,\n \"learning_rate_scheduler\": None\n }\n argument_names = arguments.keys()\n for a, n in zip(args[1:], argument_names):\n arguments[n] = a\n for k in kwargs:\n if k in arguments:\n arguments[k] = kwargs[k]\n else:\n raise ValueError(\"Unknown argument to {}.\".print(k))\n\n validation_data = arguments[\"validation_data\"]\n batch_size = arguments[\"batch_size\"]\n sigma_noise = arguments[\"sigma_noise\"]\n adversarial_training = arguments[\"adversarial_training\"]\n delta_at = arguments[\"delta_at\"]\n initial_learning_rate = arguments[\"initial_learning_rate\"]\n convergence_epochs = arguments[\"convergence_epochs\"]\n learning_rate_decay = arguments[\"learning_rate_decay\"]\n learning_rate_minimum = arguments[\"learning_rate_minimum\"]\n maximum_epochs = arguments[\"maximum_epochs\"]\n training_split = arguments[\"training_split\"]\n gpu = arguments[\"gpu\"]\n momentum = arguments[\"momentum\"]\n optimizer = arguments[\"optimizer\"]\n learning_rate_scheduler = arguments[\"learning_rate_scheduler\"]\n\n #\n # Determine device to use\n #\n if torch.cuda.is_available() and gpu:\n device = torch.device(\"cuda\")\n else:\n device = torch.device(\"cpu\")\n self.to(device)\n\n #\n # Handle input data\n #\n try:\n x, y = handle_input(training_data, device)\n training_data = BatchedDataset((x, y), batch_size)\n except:\n pass\n\n self.train()\n if not optimizer:\n self.optimizer = optim.SGD(\n self.parameters(), lr=initial_learning_rate, momentum=momentum\n )\n else:\n self.optimizer = optimizer\n self.criterion.to(device)\n\n if not optimizer and not learning_rate_scheduler:\n scheduler = ReduceLROnPlateau(\n self.optimizer,\n factor=1.0 / learning_rate_decay,\n patience=convergence_epochs,\n min_lr=learning_rate_minimum,\n )\n else:\n scheduler = learning_rate_scheduler\n\n training_errors = []\n validation_errors = []\n\n #\n # Training loop\n #\n\n for i in range(maximum_epochs):\n err = 0.0\n n = 0\n for j, (x, y) in enumerate(training_data):\n\n x = x.to(device)\n y = y.to(device)\n\n shape = x.size()\n shape = (shape[0], 1) + shape[2:]\n y = y.reshape(shape)\n\n self.optimizer.zero_grad()\n y_pred = self(x)\n c = self.criterion(y_pred, y)\n c.backward()\n self.optimizer.step()\n\n err += c.item() * x.size()[0]\n n += x.size()[0]\n\n if adversarial_training:\n self.optimizer.zero_grad()\n x_adv = self._make_adversarial_samples(x, y, delta_at)\n y_pred = self(x)\n c = self.criterion(y_pred, y)\n c.backward()\n self.optimizer.step()\n\n if j % 100:\n print(\n \"Epoch {} / {}: Batch {} / {}, Training error: {:.3f}\".format(\n i, maximum_epochs, j, len(training_data), err / n\n ),\n end=\"\\r\",\n )\n\n # Save training error\n training_errors.append(err / n)\n\n lr = [group[\"lr\"] for group in self.optimizer.param_groups][0]\n\n val_err = 0.0\n if not validation_data is None:\n n = 0\n for x, y in validation_data:\n x = x.to(device).detach()\n y = y.to(device).detach()\n\n shape = x.size()\n shape = (shape[0], 1) + shape[2:]\n y = y.reshape(shape)\n\n y_pred = self(x)\n c = self.criterion(y_pred, y)\n\n val_err += c.item() * x.size()[0]\n n += x.size()[0]\n validation_errors.append(val_err / n)\n\n print(\n \"Epoch {} / {}: Training error: {:.3f}, Validation error: {:.3f}, Learning rate: {:.5f}\".format(\n i,\n maximum_epochs,\n training_errors[-1],\n validation_errors[-1],\n lr,\n )\n )\n if scheduler:\n scheduler.step()\n else:\n scheduler.step()\n print(\n \"Epoch {} / {}: Training error: {:.3f}, Learning rate: {:.5f}\".format(\n i, maximum_epochs, training_errors[-1], lr\n )\n )\n\n self.training_errors += training_errors\n self.validation_errors += validation_errors\n self.eval()\n return {\n \"training_errors\": self.training_errors,\n \"validation_errors\": self.validation_errors,\n }","def train(self):\n\n # Ensure everything is sent to GPU if being trained on the cloud\n if self.local == False:\n torch.set_default_tensor_type(torch.cuda.FloatTensor)\n print(\"\\n \\n EVERYTHING TO CUDA \\n \\n\")\n\n # Load weights if applicable\n if self.load_weights == True:\n start_epoch, loss = self.load_checkpoint(\n self.model, self.optimizer, self.model_name\n )\n start_epoch += 1\n print(\"\\n \\n [WEIGHTS LOADED]\")\n else:\n start_epoch = 0\n\n # Start Training Loop\n for epoch in range(start_epoch, self.epochs + 1):\n\n # TRAIN\n if epoch > 0:\n\n # Set model to training mode\n self.model.train()\n\n # Initialize loss and counter that will allow model weights to be saved and overwritten every 10 minibatches\n train_loss = 0\n counter = 0\n\n # Iterate through train set\n for (\n image1,\n image2,\n annotation1,\n annotation2,\n landmark1,\n landmark2,\n ) in tqdm(self.train_loader, desc=\"Train Epoch \" + str(epoch)):\n\n # image tensors and bounding box and label tensors to device\n image1 = image1.to(self.device)\n image2 = image2.to(self.device)\n\n # Forward pass of model\n x1_hat, x2_hat, z1, z2 = self.model(image1, image2)\n\n # Calculate loss from one pass and append to training loss\n loss = self.loss_fcn(\n image1, image2, x1_hat, x2_hat, z1.detach(), z2.detach()\n )\n train_loss += loss.item()\n\n # Clear optimizer gradient\n self.optimizer.zero_grad()\n\n # Backprop\n loss.backward()\n\n # Take a step with optimizer\n self.optimizer.step()\n\n # Save/overwrite model weights every 10 minibatches\n if counter % 10 == 0:\n self.save_checkpoint(\n self.model,\n self.optimizer,\n self.model_name,\n epoch,\n train_loss,\n )\n\n print(\n f\"====> Epoch: {epoch} Average train loss: {train_loss / len(self.train_loader.dataset):.4f}\\n\"\n )\n\n # Save entire model as .pt after every epoch of training\n if self.local == True:\n torch.save(\n self.model,\n os.path.join(self.save_path, self.model_name + \".pt\"),\n )\n else:\n torch.save(\n self.model, self.model_name + \"_epoch\" + str(epoch) + \".pt\"\n )\n print(\"SAVED MODEL EPOCH \" + str(epoch))\n\n # Evaluate on Validation Set after each epoch\n with torch.no_grad():\n\n # Set model to evaluation mode\n self.model.eval()\n\n # Iterate through validation set\n for image1, image2, annotation1, annotation2 in tqdm(\n self.val_loader, desc=\"Validation Epoch \" + str(epoch)\n ):\n\n # Initialize validation loss\n val_loss = 0\n\n # Send images to device\n image1 = image1.to(self.device)\n image2 = image2.to(self.device)\n\n # Forward pass of model\n x1_hat, x2_hat, z1, z2 = self.model(image1, image2)\n\n # Calculate loss and append to validation loss\n loss = self.loss_fcn(\n image1, image2, x1_hat, x2_hat, z1.detach(), z2.detach()\n )\n val_loss += loss\n\n print(\n f\"====> Epoch: {epoch} Average test loss: {val_loss / len(self.val_loader.dataset):.4f}\\n\"\n )\n\n print(\"[DONE EPOCH{}]\".format(epoch))\n\n print(\"[DONE TRAINING]\")\n\n # Save model after all epochs are finished\n if self.local == True:\n torch.save(\n self.model, os.path.join(self.save_path, self.model_name + \".pt\")\n )\n else:\n torch.save(self.model, self.model_name + \".pt\")","def train_model(self, *args, **kwargs):\n raise NotImplementedError","def train(data_loader, model, optimizer, device):\n\t# put the model in train mode\n\tmodel.train()\n\t# go over every batch of data in data loader\n\tfor data in data_loader:\n\t\t# remember we have image and targets in our dataset class\n\t\tinputs = data['image']\n\t\ttargets = data['targets']\n\t\t# remove inputs/targets to cuda/cpu device\n\t\tinputs = inputs.to(device,dtype = torch.float)\n\t\ttargets = targets.to(device,dtype = torch.float)\n\t\t# zero grad the optimizer\n\toptimizer.zero_grad()\n\t# do the forward step of model\n\toutputs = model(inputs)\n\t# calculate loss\n\tloss = nn.BCEWithLogitsLoss()(outputs, targets.view(-1, 1)) \n\t# backward step the loss\n\tloss.backward()\n\t# step optimizer\n\toptimizer.step()\n\t# if you have a scheduler, you either need to\n\t# step it here or you have to step it after\n\t# the epoch. here, we are not using any learning # rate scheduler","def train(self):\n self.emission_model(self.train_data)\n self.transition_model(self.train_data)","def train(self):\n print('Model training...')\n self.model.train()\n self.likelihood.train()\n optimizer = torch.optim.Adam(\n [{'params': self.model.parameters()}], lr=self.lr)\n mll = gpytorch.mlls.ExactMarginalLogLikelihood(\n self.likelihood, self.model)\n for i in range(self.steps):\n optimizer.zero_grad()\n output = self.model(self.X)\n loss = -mll(output, self.y)\n loss.backward()\n self.lscales.append(\n self.model.covar_module.base_kernel.base_kernel.lengthscale.tolist()[0]\n )\n self.noise_all.append(\n self.model.likelihood.noise_covar.noise.item())\n if self.verbose:\n print('iter: {} ...'.format(i),\n 'loss: {} ...'.format(np.around(loss.item(), 4)),\n 'length: {} ...'.format(np.around(self.lscales[-1], 4)),\n 'noise: {} ...'.format(np.around(self.noise_all[-1], 7)))\n optimizer.step()\n return","def train(self, train_loader):\n\n self.model.train()\n with torch.enable_grad():\n return self._iteration(train_loader)","def train():\n pass","def train(args, data_loader, model, global_stats):\n # Initialize meters + timers\n train_loss = AverageMeter()\n epoch_time = Timer()\n \n for batch_idx, (input_idxs, target_idxs, input_tokens, target_tokens) in enumerate(data_loader):\n # input_idxs and target_idxs have dim (batch_size x max_len)\n # they are NOT sorted by length\n\n lengths = (input_idxs != 0).long().sum(dim=1)\n sorted_lengths, order = torch.sort(lengths, descending=True)\n\n input_variable = Variable(input_idxs[order, :][:, :max(lengths)])\n target_variable = Variable(target_idxs[order, :])\n \n model.optimizer.zero_grad()\n output_log_probs, output_ses = model(input_variable,\n list(sorted_lengths),\n targets=target_variable)\n \n batch_size = input_variable.shape[0]\n flattened_outputs = output_log_probs.view(batch_size * model.max_length, -1)\n \n batch_loss = model.citerion(flattened_outputs, target_variable.contiguous().view(-1))\n batch_loss.backward()\n model.optimizer.step()\n \n model.updates += 1\n \n train_loss.update(batch_loss[0], batch_size)\n \n if batch_idx % args.display_iter == 0:\n logger.info('train: Epoch = %d | iter = %d/%d | ' %\n (global_stats['epoch'], batch_idx, len(data_loader)) +\n 'loss = %.2f | elapsed time = %.2f (s)' %\n (train_loss.avg, global_stats['timer'].time()))\n train_loss.reset()\n \n logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' %\n (global_stats['epoch'], epoch_time.time()))\n \n # Checkpoint\n if args.checkpoint:\n model.checkpoint(args.model_file + '.checkpoint',\n global_stats['epoch'] + 1)","def train(self):\n train_dataloader = self.get_train_dataloader()\n\n if self.args.max_steps > 0:\n t_total = self.args.max_steps\n num_train_epochs = (\n self.args.max_steps // (len(train_dataloader) // self.args.gradient_accumulation_steps) + 1\n )\n else:\n t_total = int(len(train_dataloader) // self.args.gradient_accumulation_steps * self.args.num_train_epochs)\n num_train_epochs = self.args.num_train_epochs\n\n lr_scheduler = orttrainer.optim.LinearWarmupLRScheduler(t_total, self.args.warmup_steps / float(t_total))\n\n loss_scaler = amp.DynamicLossScaler() if self.args.fp16 else None\n device = self.args.device.type\n\n device = f\"{device}:{self.args.device.index}\" if self.args.device.index else f\"{device}:0\"\n options = orttrainer.ORTTrainerOptions(\n {\n \"batch\": {\"gradient_accumulation_steps\": self.args.gradient_accumulation_steps},\n \"device\": {\"id\": device},\n \"mixed_precision\": {\"enabled\": self.args.fp16, \"loss_scaler\": loss_scaler},\n \"debug\": {\n \"deterministic_compute\": True,\n },\n \"utils\": {\"grad_norm_clip\": False},\n \"distributed\": {\n # we are running single node multi gpu test. thus world_rank = local_rank\n # and world_size = self.args.n_gpu\n \"world_rank\": max(0, self.args.local_rank),\n \"world_size\": int(self.world_size),\n \"local_rank\": max(0, self.args.local_rank),\n \"allreduce_post_accumulation\": True,\n },\n \"lr_scheduler\": lr_scheduler,\n }\n )\n\n param_optimizer = list(self.model.named_parameters())\n params = [\n {\n \"params\": [n for n, p in param_optimizer if \"bias\" in n or \"LayerNorm.weight\" in n],\n \"weight_decay_mode\": 1,\n },\n {\n \"params\": [n for n, p in param_optimizer if not (\"bias\" in n or \"LayerNorm.weight\" in n)],\n \"weight_decay_mode\": 1,\n },\n ]\n\n optim_config = optim.AdamConfig(params=params, lr=2e-5, do_bias_correction=True)\n self.model = orttrainer.ORTTrainer(self.model, self.model_desc, optim_config, options=options)\n\n # Train!\n logger.info(\"***** Running training *****\")\n logger.info(\" Num examples = %d\", len(train_dataloader.dataset))\n logger.info(\" Num Epochs = %d\", num_train_epochs)\n logger.info(\" Instantaneous batch size per GPU = %d\", self.args.per_gpu_train_batch_size)\n logger.info(\n \" Total train batch size (w. parallel, distributed & accumulation) = %d\",\n self.args.train_batch_size\n * self.args.gradient_accumulation_steps\n * (torch.distributed.get_world_size() if self.args.local_rank != -1 else 1),\n )\n logger.info(\" Gradient Accumulation steps = %d\", self.args.gradient_accumulation_steps)\n logger.info(\" Total optimization steps = %d\", t_total)\n\n global_step = 0\n epochs_trained = 0\n steps_trained_in_current_epoch = 0\n\n tr_loss = 0.0\n logging_loss = 0.0\n train_iterator = trange(\n epochs_trained,\n int(num_train_epochs),\n desc=\"Epoch\",\n disable=self.args.local_rank not in [-1, 0],\n )\n\n for _epoch in train_iterator:\n epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\", disable=self.args.local_rank not in [-1, 0])\n for step, inputs in enumerate(epoch_iterator):\n # Skip past any already trained steps if resuming training\n if steps_trained_in_current_epoch > 0:\n steps_trained_in_current_epoch -= 1\n continue\n\n tr_loss += self._training_step(self.model, inputs)\n\n if (step + 1) % self.args.gradient_accumulation_steps == 0 or (\n len(epoch_iterator) <= self.args.gradient_accumulation_steps and (step + 1) == len(epoch_iterator)\n ):\n global_step += 1\n\n if self.args.local_rank in [-1, 0]:\n if (self.args.logging_steps > 0 and global_step % self.args.logging_steps == 0) or (\n global_step == 1 and self.args.logging_first_step\n ):\n logs = {}\n if self.args.evaluate_during_training:\n results = self.evaluate()\n for key, value in results.items():\n eval_key = f\"eval_{key}\"\n logs[eval_key] = value\n\n loss_scalar = (tr_loss - logging_loss) / self.args.logging_steps\n\n logs[\"loss\"] = loss_scalar\n logging_loss = tr_loss\n\n epoch_iterator.write(json.dumps({**logs, **{\"step\": global_step}}))\n\n if self.args.max_steps > 0 and global_step > self.args.max_steps:\n epoch_iterator.close()\n break\n if self.args.max_steps > 0 and global_step > self.args.max_steps:\n train_iterator.close()\n break\n\n logger.info(\"\\n\\nTraining completed. \\n\\n\")\n return TrainOutput(global_step, tr_loss / global_step)","def trainModel( self, featureTrain, classTrain):","def train(model, data_loader, optimizer, epoch, train_mloss, train_rloss, train_acc, learning_rate, lr_wr, output_tensor):\r\n print('===> Training mode')\r\n\r\n num_batches = len(data_loader) # iteration per epoch. e.g: 469\r\n total_step = args.epochs * num_batches\r\n epoch_tot_acc = 0\r\n\r\n # Switch to train mode\r\n model.train()\r\n\r\n if args.cuda:\r\n # When we wrap a Module in DataParallel for multi-GPUs\r\n model = model.module\r\n\r\n start_time = timer()\r\n\r\n for batch_idx, (data, target) in enumerate(tqdm(data_loader, unit='batch')):\r\n batch_size = data.size(0)\r\n global_step = batch_idx + (epoch * num_batches) - num_batches\r\n\r\n labels = target\r\n target_one_hot = utils.one_hot_encode(target, length=args.num_classes)\r\n assert target_one_hot.size() == torch.Size([batch_size, 10])\r\n\r\n data, target = Variable(data), Variable(target_one_hot)\r\n\r\n if args.cuda:\r\n data = data.to(args.device)\r\n target = target.to(args.device)\r\n labels = labels.to(args.device)\r\n\r\n # Train step - forward, backward and optimize\r\n optimizer.zero_grad()\r\n #utils.exponential_decay_LRR(optimizer, args.lr, global_step, args.decay_steps, args.decay_rate, args.staircase)\r\n # learning rate policies\r\n if args.find_lr:\r\n utils.find_lr(optimizer, global_step)\r\n\r\n elif args.exp_decay_lr:\r\n utils.exponential_decay_LRR(\r\n optimizer, args.lr, global_step, args.decay_steps, args.decay_rate, args.staircase)\r\n\r\n elif args.one_cycle_policy:\r\n utils.one_cycle_policy(optimizer, args.lr, global_step, total_step)\r\n\r\n elif args.warm_restarts:\r\n # lr_wr.update_lr(optimizer, num_batches)\r\n lr_wr.update_lr(optimizer)\r\n\r\n output, reconstruction = model(data, labels, True)\r\n # utils.write_tensor(output, output_tensor)\r\n loss, margin_loss, recon_loss = loss_func(\r\n output, target, args.regularization_scale, reconstruction, data, args.device, batch_size)\r\n loss.backward()\r\n optimizer.step()\r\n\r\n for param_group in optimizer.param_groups:\r\n lr_temp = param_group['lr']\r\n learning_rate.write('%.10f \\n' % lr_temp)\r\n\r\n # Calculate accuracy for each step and average accuracy for each epoch\r\n acc = utils.accuracy(output, labels, args.cuda)\r\n epoch_tot_acc += acc\r\n epoch_avg_acc = epoch_tot_acc / (batch_idx + 1)\r\n\r\n train_mloss.write('%.6f \\n' % margin_loss)\r\n train_rloss.write('%.6f \\n' % recon_loss)\r\n train_acc.write('%.6f \\n' % acc)\r\n\r\n # Print losses\r\n if batch_idx % args.log_interval == 0:\r\n template = 'Epoch {}/{}, ' \\\r\n 'Step {}/{}: ' \\\r\n '[Total loss: {:.6f},' \\\r\n '\\tMargin loss: {:.6f},' \\\r\n '\\tReconstruction loss: {:.6f},' \\\r\n '\\tBatch accuracy: {:.6f},' \\\r\n '\\tAccuracy: {:.6f}]'\r\n tqdm.write(template.format(\r\n epoch,\r\n args.epochs,\r\n global_step,\r\n total_step,\r\n loss.data.item(),\r\n margin_loss.data.item(),\r\n recon_loss.data.item() if args.use_reconstruction_loss else 0,\r\n acc,\r\n epoch_avg_acc))\r\n\r\n # Print time elapsed for an epoch\r\n end_time = timer()\r\n\r\n global avg_training_time_per_epoch\r\n\r\n avg_training_time_per_epoch = (avg_training_time_per_epoch * (epoch - 1) + end_time - start_time) / epoch\r\n\r\n print('Time elapsed for epoch {}: {:.0f}s.'.format(epoch, end_time - start_time))","def train(args: dict, lr_index: int, dataloaders: Tuple[DataLoader]):\n train_dataloader, val_dataloader = dataloaders\n device = torch.device('cuda') if args.gpu else torch.device('cpu')\n model = get_model(args, device)\n if args.label_weights is None:\n label_weights = None\n else:\n label_weights = args.label_weights.to(device)\n loss_criterion = nn.CrossEntropyLoss(weight=label_weights)\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=args.learning_rates[lr_index]\n )\n train_losses = []\n val_losses = []\n stats = {\"true\": [], \"pred\": []}\n for epoch in range(args.epochs):\n start = time.time()\n train_loss = train_epoch(\n model, loss_criterion, optimizer, train_dataloader, device\n )\n val_loss, epoch_stats = val_epoch(\n model, loss_criterion, val_dataloader, device\n )\n train_losses.append(train_loss)\n val_losses.append(val_loss)\n stats[\"true\"] += epoch_stats[\"true\"]\n stats[\"pred\"] += epoch_stats[\"pred\"]\n duration = (time.time() - start) / 60 # epoch duration in minutes\n print_training_update(\n epoch, duration, lr_index, (train_loss, val_loss), epoch_stats\n )\n recent_10_percent = int(0.9*args.epochs)\n return (\n model,\n np.mean(train_losses[recent_10_percent:]),\n np.mean(val_losses[recent_10_percent:]),\n training_metric(stats)\n )","def train(self):\n self.mode = \"train\"\n self.online_net.train()","def train(self):\n self.mode = \"train\"\n self.online_net.train()","def train(self, dataset, model_dir):\n raise NotImplementedError","def train(epochs, batch_size, lr, verbose):\n # autograd globally off\n torch.set_grad_enabled(False)\n # generate training and testing datasets\n train_data, train_label = generate_data()\n test_data, test_label = generate_data()\n # normalize data be centered at 0\n train_data, test_data = normalize(train_data, test_data)\n\n if verbose:\n print(\"--- Dataset ---\")\n print(\"Train X: \", train_data.size(), \" | Train y: \", train_label.size())\n print(\" Test X: \", test_data.size(), \" | Test y: \", test_label.size())\n\n layers =[]\n # input layer (2 input units)\n linear1 = Linear(2, 25, bias= True, weight_init=xavier_uniform)\n\n # 3 hidden layers (each 25 units)\n linear2 = Linear(25, 25, bias= True, weight_init=xavier_uniform)\n linear3 = Linear(25, 25, bias= True, weight_init=xavier_uniform)\n linear4 = Linear(25, 25, bias= True, weight_init=xavier_uniform)\n\n # output layer (2 output units)\n linear5 = Linear(25, 2, bias= True, weight_init=xavier_uniform)\n\n\n layers.append(linear1)\n layers.append(Relu())\n layers.append(linear2)\n layers.append(Relu())\n layers.append(linear3)\n layers.append(Relu())\n layers.append(linear4)\n layers.append(Tanh())\n layers.append(linear5)\n\n model = Sequential(layers)\n if verbose: print(\"Number of model parameters: {}\".format(sum([len(p) for p in model.param()])))\n\n criterion = MSE()\n optimizer = SGD(model, lr=lr)\n\n train_losses, test_losses = [], []\n train_accuracies, test_accuracies = [], []\n train_errors, test_errors = [], []\n\n if verbose: print(\"--- Training ---\")\n for epoch in range(1, epochs+1):\n if verbose:print(\"Epoch: {}\".format(epoch))\n\n # TRAINING\n for batch_idx in range(0, train_data.size(0), batch_size):\n # axis 0, start from batch_idx until batch_idx+batch_size\n output = model.forward(train_data.narrow(0, batch_idx, batch_size))\n\n # Calculate loss\n loss = criterion.forward(output, train_label.narrow(0, batch_idx, batch_size))\n train_losses.append(loss)\n if verbose: print(\"Train Loss: {:.2f}\".format(loss.item()))\n\n # put to zero weights and bias\n optimizer.zero_grad()\n\n ## Backpropagation\n # Calculate grad of loss\n loss_grad = criterion.backward()\n\n # Grad of the model\n model.backward(loss_grad)\n\n # Update parameters\n optimizer.step()\n\n train_prediction = model.forward(train_data)\n acc = accuracy(train_prediction, train_label)\n train_accuracies.append(acc)\n train_errors.append(1-acc)\n if verbose: print(\"Train Accuracy: {:.2f}\".format(acc.item()))\n\n # EVALUATION\n for batch_idx in range(0, test_data.size(0), batch_size):\n # axis 0, start from batch_idx until batch_idx+batch_size\n output = model.forward(test_data.narrow(0, batch_idx, batch_size))\n\n # Calculate loss\n loss = criterion.forward(output, test_label.narrow(0, batch_idx, batch_size))\n test_losses.append(loss)\n if verbose: print(\"Test Loss: {:.2f}\".format(loss.item()))\n\n test_prediction = model.forward(test_data)\n acc = accuracy(test_prediction, test_label)\n test_accuracies.append(acc) \n test_errors.append(1-acc)\n if verbose: print(\"Test Accuracy: {:.2f}\".format(acc.item()))\n\n return train_losses, test_losses, train_accuracies, test_accuracies, train_errors, test_errors","def train(settings=None):\n if not settings:\n settings = Settings()\n train_transform = torchvision.transforms.Compose([transforms.RandomlySelectPatchAndRescale(),\n transforms.RandomHorizontalFlip(),\n transforms.NegativeOneToOneNormalizeImage(),\n transforms.NumpyArraysToTorchTensors()])\n validation_transform = torchvision.transforms.Compose([transforms.RandomlySelectPatchAndRescale(),\n transforms.NegativeOneToOneNormalizeImage(),\n transforms.NumpyArraysToTorchTensors()])\n\n train_dataset = CrowdDatasetWithUnlabeled(settings.train_dataset_path, 'train', transform=train_transform)\n train_dataset_loader = torch.utils.data.DataLoader(train_dataset, batch_size=settings.batch_size, shuffle=True,\n num_workers=settings.number_of_data_loader_workers)\n validation_dataset = CrowdDataset(settings.validation_dataset_path, 'validation', transform=validation_transform)\n validation_dataset_loader = torch.utils.data.DataLoader(validation_dataset, batch_size=settings.batch_size,\n shuffle=False,\n num_workers=settings.number_of_data_loader_workers)\n\n gan = GAN()\n gpu(gan)\n D = gan.D\n G = gan.G\n discriminator_optimizer = Adam(D.parameters())\n generator_optimizer = Adam(G.parameters())\n\n step = 0\n epoch = 0\n\n if settings.load_model_path:\n d_model_state_dict, d_optimizer_state_dict, epoch, step = load_trainer(prefix='discriminator',\n settings=settings)\n D.load_state_dict(d_model_state_dict)\n discriminator_optimizer.load_state_dict(d_optimizer_state_dict)\n discriminator_optimizer.param_groups[0].update({'lr': settings.learning_rate, 'weight_decay': settings.weight_decay})\n if settings.load_model_path:\n g_model_state_dict, g_optimizer_state_dict, _, _ = load_trainer(prefix='generator',\n settings=settings)\n G.load_state_dict(g_model_state_dict)\n generator_optimizer.load_state_dict(g_optimizer_state_dict)\n generator_optimizer.param_groups[0].update({'lr': settings.learning_rate})\n\n running_scalars = defaultdict(float)\n validation_running_scalars = defaultdict(float)\n running_example_count = 0\n datetime_string = datetime.datetime.now().strftime(\"y%Ym%md%dh%Hm%Ms%S\")\n trial_directory = os.path.join(settings.log_directory, settings.trial_name + ' ' + datetime_string)\n os.makedirs(trial_directory, exist_ok=True)\n summary_writer = SummaryWriter(os.path.join(trial_directory, 'train'))\n validation_summary_writer = SummaryWriter(os.path.join(trial_directory, 'validation'))\n print('Starting training...')\n step_time_start = datetime.datetime.now()\n while epoch < settings.number_of_epochs:\n for examples, unlabeled_examples in train_dataset_loader:\n unlabeled_images = unlabeled_examples[0]\n # Real image discriminator processing.\n discriminator_optimizer.zero_grad()\n images, labels, _ = examples\n images, labels = Variable(gpu(images)), Variable(gpu(labels))\n current_batch_size = images.data.shape[0]\n predicted_labels, predicted_counts = D(images)\n real_feature_layer = D.feature_layer\n density_loss = torch.abs(predicted_labels - labels).pow(settings.loss_order).sum(1).sum(1).mean()\n count_loss = torch.abs(predicted_counts - labels.sum(1).sum(1)).pow(settings.loss_order).mean()\n loss = count_loss + (density_loss * 10)\n loss.backward()\n running_scalars['Labeled/Loss'] += loss.data[0]\n running_scalars['Labeled/Count Loss'] += count_loss.data[0]\n running_scalars['Labeled/Density Loss'] += density_loss.data[0]\n running_scalars['Labeled/Count ME'] += (predicted_counts - labels.sum(1).sum(1)).mean().data[0]\n # Unlabeled.\n _ = D(gpu(images))\n labeled_feature_layer = D.feature_layer\n _ = D(gpu(Variable(unlabeled_images)))\n unlabeled_feature_layer = D.feature_layer\n unlabeled_loss = feature_distance_loss(unlabeled_feature_layer, labeled_feature_layer,\n scale=False) * settings.unlabeled_loss_multiplier\n unlabeled_loss.backward()\n # Fake.\n _ = D(gpu(Variable(unlabeled_images)))\n unlabeled_feature_layer = D.feature_layer\n z = torch.from_numpy(MixtureModel([norm(-settings.mean_offset, 1), norm(settings.mean_offset, 1)]).rvs(\n size=[current_batch_size, 100]).astype(np.float32))\n # z = torch.randn(settings.batch_size, noise_size)\n fake_examples = G(gpu(Variable(z)))\n _ = D(fake_examples.detach())\n fake_feature_layer = D.feature_layer\n fake_loss = feature_distance_loss(unlabeled_feature_layer, fake_feature_layer,\n order=1).neg() * settings.fake_loss_multiplier\n fake_loss.backward()\n # Feature norm loss.\n _ = D(gpu(Variable(unlabeled_images)))\n unlabeled_feature_layer = D.feature_layer\n feature_norm_loss = (unlabeled_feature_layer.norm(dim=1).mean() - 1).pow(2)\n feature_norm_loss.backward()\n # Gradient penalty.\n if settings.gradient_penalty_on:\n alpha = gpu(Variable(torch.rand(2)))\n alpha = alpha / alpha.sum(0)\n interpolates = (alpha[0] * gpu(Variable(unlabeled_images, requires_grad=True)) +\n alpha[1] * gpu(Variable(fake_examples.detach().data, requires_grad=True)))\n _ = D(interpolates)\n interpolates_predictions = D.feature_layer\n gradients = torch.autograd.grad(outputs=interpolates_predictions, inputs=interpolates,\n grad_outputs=gpu(torch.ones(interpolates_predictions.size())),\n create_graph=True, only_inputs=True)[0]\n gradient_penalty = ((gradients.norm(dim=1) - 1) ** 2).mean() * settings.gradient_penalty_multiplier\n gradient_penalty.backward()\n # Discriminator update.\n discriminator_optimizer.step()\n # Generator.\n if step % 1 == 0:\n generator_optimizer.zero_grad()\n _ = D(gpu(Variable(unlabeled_images)))\n unlabeled_feature_layer = D.feature_layer.detach()\n z = torch.randn(current_batch_size, 100)\n fake_examples = G(gpu(Variable(z)))\n _ = D(fake_examples)\n fake_feature_layer = D.feature_layer\n generator_loss = feature_distance_loss(unlabeled_feature_layer, fake_feature_layer)\n generator_loss.backward()\n generator_optimizer.step()\n\n running_example_count += images.size()[0]\n if step % settings.summary_step_period == 0 and step != 0:\n comparison_image = viewer.create_crowd_images_comparison_grid(cpu(images), cpu(labels),\n cpu(predicted_labels))\n summary_writer.add_image('Comparison', comparison_image, global_step=step)\n fake_images_image = torchvision.utils.make_grid(fake_examples.data[:9], nrow=3)\n summary_writer.add_image('Fake', fake_images_image, global_step=step)\n print('\\rStep {}, {}...'.format(step, datetime.datetime.now() - step_time_start), end='')\n step_time_start = datetime.datetime.now()\n for name, running_scalar in running_scalars.items():\n mean_scalar = running_scalar / running_example_count\n summary_writer.add_scalar(name, mean_scalar, global_step=step)\n running_scalars[name] = 0\n running_example_count = 0\n for validation_examples in validation_dataset_loader:\n images, labels, _ = validation_examples\n images, labels = Variable(gpu(images)), Variable(gpu(labels))\n predicted_labels, predicted_counts = D(images)\n density_loss = torch.abs(predicted_labels - labels).pow(settings.loss_order).sum(1).sum(1).mean()\n count_loss = torch.abs(predicted_counts - labels.sum(1).sum(1)).pow(settings.loss_order).mean()\n count_mae = torch.abs(predicted_counts - labels.sum(1).sum(1)).mean()\n count_me = (predicted_counts - labels.sum(1).sum(1)).mean()\n validation_running_scalars['Labeled/Density Loss'] += density_loss.data[0]\n validation_running_scalars['Labeled/Count Loss'] += count_loss.data[0]\n validation_running_scalars['Test/Count MAE'] += count_mae.data[0]\n validation_running_scalars['Labeled/Count ME'] += count_me.data[0]\n comparison_image = viewer.create_crowd_images_comparison_grid(cpu(images), cpu(labels),\n cpu(predicted_labels))\n validation_summary_writer.add_image('Comparison', comparison_image, global_step=step)\n for name, running_scalar in validation_running_scalars.items():\n mean_scalar = running_scalar / len(validation_dataset)\n validation_summary_writer.add_scalar(name, mean_scalar, global_step=step)\n validation_running_scalars[name] = 0\n step += 1\n epoch += 1\n if epoch != 0 and epoch % settings.save_epoch_period == 0:\n save_trainer(trial_directory, D, discriminator_optimizer, epoch, step, prefix='discriminator')\n save_trainer(trial_directory, G, generator_optimizer, epoch, step, prefix='generator')\n save_trainer(trial_directory, D, discriminator_optimizer, epoch, step, prefix='discriminator')\n save_trainer(trial_directory, G, generator_optimizer, epoch, step, prefix='generator')\n print('Finished Training')\n return trial_directory","def train(self):\n # self.recognizer.train()\n self.detector.train()\n self.shared_conv.train()","def train(train_dataset: torch.utils.data.Dataset, test_dataset: torch.utils.data.Dataset,\n training_config: dict = train_config, global_config: dict = global_config):\n\n for path in global_config.values():\n create_dirs(path)\n\n # wrap datasets with Dataloader classes\n train_loader = torch.utils.data.DataLoader(train_dataset,\n **training_config[\"DATA_LOADER_CONFIG\"])\n test_loader = torch.utils.data.DataLoader(test_dataset,\n **training_config[\"DATA_LOADER_CONFIG\"])\n\n # model name & paths\n name = \"_\".join([train_config[\"DATE\"], train_config[\"SESSION_NAME\"]])\n modelpath = os.path.join(global_config[\"WEIGHT_DIR\"], name)\n\n # instantiate model\n model = training_config[\"MODEL\"](**training_config[\"MODEL_CONFIG\"])\n\n optimizer = training_config[\"OPTIMIZER\"](model.parameters(),\n **training_config[\"OPTIMIZER_CONFIG\"])\n\n # set up ignite engine\n training_config[\"METRICS\"].update({\"loss\" : Loss(training_config[\"LOSS\"])})\n trainer = create_supervised_trainer(model=model, optimizer=optimizer,\n loss_fn=training_config[\"LOSS\"],\n device=training_config[\"DEVICE\"])\n evaluator = create_supervised_evaluator(model,\n metrics=training_config[\"METRICS\"],\n device=training_config[\"DEVICE\"])\n\n\n # tensorboardX setup\n log_dir = os.path.join(global_config[\"LOG_DIR\"], \"tensorboardx\", name)\n create_dirs(log_dir)\n writer = SummaryWriter(logdir=log_dir)\n\n # log using the logging tool\n logger = log.Log(training_config, run_name=train_config['SESSION_NAME'])\n\n @trainer.on(Events.ITERATION_COMPLETED)\n def log_training(engine):\n iteration = (engine.state.iteration - 1) % len(train_loader) + 1\n writer.add_scalar(\"training/loss\", engine.state.output, engine.state.iteration)\n if iteration % 4 == 0:\n print(\"\\repoch[{}] iteration[{}/{}] loss: {:.2f} \".format(engine.state.epoch,\n iteration, len(train_loader),\n engine.state.output), end=\"\")\n\n # generic evaluation function\n def evaluate(engine, loader):\n evaluator.run(loader)\n metrics = evaluator.state.metrics\n return metrics\n\n # training data metrics\n @trainer.on(Events.EPOCH_COMPLETED)\n def log_training_results(engine):\n print(\"\\ntraining results - epoch {}\".format(engine.state.epoch))\n metrics = evaluate(engine, train_loader)\n print(metrics)\n for key, value in metrics.items():\n logger.log_metric(key, value)\n writer.add_scalar(\"training/avg_{}\".format(key), value, engine.state.epoch)\n\n # test data metrics\n @trainer.on(Events.EPOCH_COMPLETED)\n def log_validation_results(engine):\n print(\"test results - epoch {}\".format(engine.state.epoch))\n metrics = evaluate(engine, test_loader)\n print(metrics)\n for key, value in metrics.items():\n writer.add_scalar(\"validation/avg_{}\".format(key), value, engine.state.epoch)\n\n # model checkpointing\n @trainer.on(Events.EPOCH_COMPLETED)\n def model_checkpoint(engine):\n torch.save(model.state_dict(), modelpath + \".pth\")\n print(\"Checkpoint saved to {}\".format(modelpath + \".pth\"))\n\n # training iteration\n try:\n trainer.run(train_loader, max_epochs=training_config[\"EPOCHS\"])\n except KeyboardInterrupt:\n torch.save(model.state_dict(), modelpath + \".pth\")\n print(\"Model saved to {}\".format(modelpath + \".pth\"))\n raise KeyboardInterrupt\n\n # write weights\n torch.save(model.state_dict(), modelpath + \".pth\")\n\n # write csv log file\n log_content = training_config.copy()\n evaluator.run(test_loader)\n log_content[\"VAL_METRICS\"] = evaluator.state.metrics\n log_path = os.path.join(global_config[\"LOG_DIR\"], training_config[\"LOGFILE\"])\n write_log(log_path, log_content)\n\n logger.end_run()\n \n return evaluator.state.metrics[\"training/avg_loss\"]","def train(self):\n start_time = time()\n self.model.train()\n\n for step, sample in enumerate(self.train_loader):\n self.optimizer.zero_grad()\n\n x, _, _ = sample\n x = x.to(self.device)\n\n y_pred = self.model.forward(x)\n loss = nn.MSELoss()(y_pred, x)\n loss.backward()\n self.train_losses.append(loss.item())\n\n self.optimizer.step(None)\n\n # print an incredible progress bar\n print(f'\\r{self.progress_bar} │ Loss: {np.mean(self.train_losses):.6f}', end='')\n self.progress_bar.inc()\n\n # log average loss of this epoch\n mean_epoch_loss = np.mean(self.train_losses)\n self.sw.add_scalar(tag='train_loss', scalar_value=mean_epoch_loss, global_step=self.epoch)\n self.train_losses = []\n\n # log epoch duration\n print(f' │ T: {time() - start_time:.2f} s')","def training(self):\n \n best_valid_loss = np.inf\n c = 0\n \n self.train_loader, self.test_loader = self.get_train_test_loaders()\n \n print('Training the {} model with the following architecture:'.format(self.model_name))\n print(summary(self.model, (3, self.image_width, self.image_height)))\n print('*'*100)\n print('Starting the training...')\n print('*'*100)\n \n # Create the model save dir if it already doesn't exist\n if not os.path.exists(self.model_save_dir):\n os.makedirs(self.model_save_dir)\n \n for epoch in range(self.n_epochs):\n\n print(f'Epoch: {epoch+1:02}')\n\n start_time = time.time()\n\n train_loss = self.train(self.train_loader)\n valid_loss = self.evaluate(self.test_loader)\n\n epoch_mins, epoch_secs = self.epoch_time(start_time, time.time())\n\n c+=1\n if valid_loss < best_valid_loss:\n best_valid_loss = valid_loss\n torch.save(self.model.state_dict(), os.path.join(self.model_save_dir, '{}_trained.pt'.format(self.model_name)))\n c=0\n\n if c>4:\n #decrease lr if loss does not decrease after 5 steps\n self.scheduler.step()\n c=0\n\n print(f'Time: {epoch_mins}m {epoch_secs}s') \n print(f'Train Loss: {train_loss:.3f}')\n print(f'Val Loss: {valid_loss:.3f}')\n print('-'*60)\n print('The best validation loss is', best_valid_loss)\n print('*'*100)","def set_train(self):\n self.model.train()","def train(self):\r\n print(\"Starting training now\")\r\n cuda = True if torch.cuda.is_available() else False\r\n if cuda:\r\n self.model.cuda()\r\n\r\n # Construct optimizer after the model moved to GPU\r\n self.optm = self.make_optimizer()\r\n self.lr_scheduler = self.make_lr_scheduler(self.optm)\r\n\r\n dim_x = self.flags.dim_x\r\n dim_y = self.flags.dim_y\r\n dim_z = self.flags.dim_z\r\n dim_tot = self.flags.dim_tot\r\n\r\n # Time keeping\r\n tk = time_keeper(time_keeping_file=os.path.join(self.ckpt_dir, 'training time.txt'))\r\n\r\n for epoch in range(self.flags.train_step):\r\n # Set to Training Mode\r\n train_loss = 0\r\n self.model.train()\r\n # If MMD on x-space is present from the start, the model can get stuck.\r\n # Instead, ramp it up exponetially.\r\n loss_factor = min(1., 2. * 0.002 ** (1. - (float(epoch) / self.flags.train_step)))\r\n\r\n for j, (x, y) in enumerate(self.train_loader):\r\n batch_size = len(x)\r\n\r\n ######################\r\n # Preparing the data #\r\n ######################\r\n # Pad the x, y with zero_noise\r\n y_clean = y.clone() # keep a copy of y for backward\r\n x_pad = self.flags.zeros_noise_scale * torch.randn(batch_size,\r\n dim_tot - dim_x)\r\n y_pad = self.flags.zeros_noise_scale * torch.randn(batch_size,\r\n dim_tot - dim_y - dim_z)\r\n z = torch.randn(batch_size, dim_z)\r\n if cuda:\r\n x = x.cuda() # Put data onto GPU\r\n y = y.cuda() # Put data onto GPU\r\n x_pad = x_pad.cuda()\r\n y_pad = y_pad.cuda()\r\n y_clean = y_clean.cuda()\r\n z = z.cuda()\r\n\r\n # Concate the x and y with pads and add y with small purtubation\r\n y += self.flags.y_noise_scale * torch.randn(batch_size, dim_y, device=device)\r\n\r\n x, y = torch.cat((x, x_pad), dim=1), torch.cat((z, y_pad, y), dim=1)\r\n\r\n ################\r\n # Forward step #\r\n ################\r\n self.optm.zero_grad() # Zero the gradient first\r\n ypred = self.model(x) # Get the Ypred\r\n\r\n\r\n # Do the MSE loss for reconstruction, Doesn't compare z part (only pad and y itself)\r\n MSE_loss_y = self.make_loss(logit=ypred[:, dim_z:], labels=y[:, dim_z:])\r\n\r\n # Use the maximum likelihood method\r\n log_det = self.model.log_jacobian(x=x)\r\n #print(\"The log determinant is\", log_det)\r\n Forward_loss = 0.5 * (MSE_loss_y / self.flags.lambda_mse + torch.mean(torch.pow(z,2))) - torch.mean(log_det)\r\n Forward_loss.backward()\r\n\r\n ######################\r\n # Gradient Clipping #\r\n ######################\r\n for parameter in self.model.parameters():\r\n parameter.grad.data.clamp_(-self.flags.grad_clamp, self.flags.grad_clamp)\r\n\r\n #########################\r\n # Descent your gradient #\r\n #########################\r\n self.optm.step() # Move one step the optimizer\r\n\r\n # MLE training\r\n train_loss += Forward_loss \r\n\r\n # Calculate the avg loss of training\r\n train_avg_loss = train_loss.cpu().data.numpy() / (j + 1)\r\n\r\n if epoch % self.flags.eval_step == 0: # For eval steps, do the evaluations and tensor board\r\n # Record the training loss to the tensorboard\r\n self.log.add_scalar('Loss/total_train', train_avg_loss, epoch)\r\n self.log.add_scalar('Loss/MSE_y_train', MSE_loss_y, epoch)\r\n\r\n # Set to Evaluation Mode\r\n self.model.eval()\r\n print(\"Doing Evaluation on the model now\")\r\n\r\n test_loss = 0\r\n for j, (x, y) in enumerate(self.test_loader): # Loop through the eval set\r\n batch_size = len(x)\r\n\r\n ######################\r\n # Preparing the data #\r\n ######################\r\n # Pad the x, y with zero_noise\r\n y_clean = y.clone() # keep a copy of y for backward\r\n x_pad = self.flags.zeros_noise_scale * torch.randn(batch_size,\r\n dim_tot - dim_x)\r\n y_pad = self.flags.zeros_noise_scale * torch.randn(batch_size,\r\n dim_tot - dim_y - dim_z)\r\n z = torch.randn(batch_size, dim_z)\r\n if cuda:\r\n x = x.cuda() # Put data onto GPU\r\n y = y.cuda() # Put data onto GPU\r\n x_pad = x_pad.cuda()\r\n y_pad = y_pad.cuda()\r\n y_clean = y_clean.cuda()\r\n z = z.cuda()\r\n\r\n # Concate the x and y with pads and add y with small purtubation\r\n y += self.flags.y_noise_scale * torch.randn(batch_size, dim_y, device=device)\r\n\r\n x, y = torch.cat((x, x_pad), dim=1), torch.cat((z, y_pad, y), dim=1)\r\n\r\n ################\r\n # Forward step #\r\n ################\r\n self.optm.zero_grad() # Zero the gradient first\r\n ypred = self.model(x) # Get the Ypred\r\n # Do the MSE loss for reconstruction, Doesn't compare z part (only pad and y itself)\r\n MSE_loss_y = self.make_loss(logit=ypred[:, dim_z:], labels=y[:, dim_z:])\r\n\r\n log_det = self.model.log_jacobian(x=x)\r\n #print(\"The log determinant is\", log_det)\r\n Forward_loss = 0.5 * (MSE_loss_y / self.flags.lambda_mse + torch.mean(torch.pow(z,2))) - torch.mean(log_det)\r\n test_loss += Forward_loss\r\n # Aggregate the other loss (in np form)\r\n\r\n # Record the testing loss to the tensorboard\r\n test_avg_loss = test_loss.cpu().data.numpy() / (j+1)\r\n\r\n self.log.add_scalar('Loss/total_test', test_avg_loss, epoch)\r\n self.log.add_scalar('Loss/MSE_y_test', MSE_loss_y, epoch)\r\n\r\n print(\"This is Epoch %d, training loss %.5f, validation loss %.5f\" \\\r\n % (epoch, train_avg_loss, test_avg_loss ))\r\n\r\n # Model improving, save the model down\r\n if test_avg_loss < self.best_validation_loss:\r\n self.best_validation_loss = train_avg_loss\r\n self.save()\r\n print(\"Saving the model down...\")\r\n\r\n if self.best_validation_loss < self.flags.stop_threshold:\r\n print(\"Training finished EARLIER at epoch %d, reaching loss of %.5f\" %\\\r\n (epoch, self.best_validation_loss))\r\n break\r\n\r\n # Learning rate decay upon plateau\r\n self.lr_scheduler.step(train_avg_loss)\r\n tk.record(1) # Record the total time of the training peroid\r","def train(args):\n\n log_header('Training network')\n\n train_retriever(args)","def train(model, train_sampler, train_loader, test_sampler, test_loader,\n use_cuda, epochs, loss_func, optimizer_name, lr,\n batch_log_interval, hvd, smlb_out):\n console = smlb_out.log.console\n device = smlb_out.log.device\n\n # training history (validation only)\n with console.subproc('Creating training history'):\n loss_val_hist = torch.zeros(epochs, dtype=torch.float32)\n acc_val_hist = torch.zeros(epochs, dtype=torch.float32)\n\n # send to device\n if use_cuda:\n with console.subproc('Sending model and history to device'):\n model.cuda()\n loss_val_hist = loss_val_hist.cuda()\n acc_val_hist = acc_val_hist.cuda()\n\n # loss\n with console.subproc('Creating loss function'):\n loss_func = eval(f'nn.{loss_func}()')\n console.message(f'Loss function: {loss_func}')\n\n # optimizer\n with console.subproc('Creating optimizer'):\n console.message(f'Learning rate specified: {lr}')\n console.message(f'Reduction operation: {\"hvd.Average\"}')\n console.message(f'Learning rate will be scaled by a factor of '\n f'{hvd.size()} (hvd.size())')\n optimizer = eval(f'torch.optim.{optimizer_name}(model.parameters(), '\n f'lr={lr * hvd.size()})')\n console.message(f'Optimizer: {optimizer}')\n # Horovod: wrap optimizer with DistributedOptimizer\n optimizer = hvd.DistributedOptimizer(optimizer, op=hvd.Average)\n\n # Horovod: broadcast model and optimizer\n with console.subproc('Broadcasting model and optimizer'):\n hvd.broadcast_parameters(model.state_dict(), root_rank=0)\n hvd.broadcast_optimizer_state(optimizer, root_rank=0)\n\n # ----------------------\n # Epoch loop starts here\n # ----------------------\n console.begin('**** EPOCH LOOP ****')\n device.begin('**** EPOCH LOOP ****')\n for epoch in range(epochs):\n # only log on device within epoch loop\n device.begin(f'Epoch: {epoch}')\n\n # -------------------\n # Training batch loop\n # -------------------\n device.begin('Training batch loop')\n # stamp train epoch in system monitor\n smlb_out.system.stamp_event(f'epoch {epoch}: train')\n # enter train mode\n model.train()\n # Horovod: set epoch to sampler for shuffling\n train_sampler.set_epoch(epoch)\n # batch loop\n for batch_idx, (batch_x, batch_y) in enumerate(train_loader):\n if use_cuda:\n batch_x, batch_y = batch_x.cuda(), batch_y.cuda()\n # forward, loss, acc\n pred_y = model(batch_x)\n loss = loss_func(pred_y, batch_y)\n # backward\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n if batch_idx % batch_log_interval == 0:\n # accuracy on batch\n with torch.no_grad():\n acc = compute_acc(pred_y, batch_y)\n # Horovod: use train_sampler to determine the number of\n # samples in this worker's partition\n device.message(\n '[{:5d}/{:5d} ({:3.0f}%)] loss={:f}, acc={:f}, '\n 'elapsed={:f} sec'.format(\n batch_idx * len(batch_x), len(train_sampler),\n 100 * batch_idx / len(train_loader), loss, acc,\n device.elapsed_shallowest))\n device.ended('Training batch loop')\n\n # ----------------------\n # Validation on test set\n # ----------------------\n device.begin('Validating on test set')\n # stamp validate epoch in system monitor\n smlb_out.system.stamp_event(f'epoch {epoch}: validate')\n # enter eval mode\n model.eval()\n # accumulate loss and acc\n loss_val = torch.zeros((1,), dtype=torch.float32)\n acc_val = torch.zeros((1,), dtype=torch.float32)\n if use_cuda:\n loss_val, acc_val = loss_val.cuda(), acc_val.cuda()\n for batch_x, batch_y in test_loader:\n if use_cuda:\n batch_x, batch_y = batch_x.cuda(), batch_y.cuda()\n # forward, loss, acc\n with torch.no_grad():\n pred_y = model(batch_x)\n loss_val += loss_func(pred_y, batch_y)\n acc_val += compute_acc(pred_y, batch_y) * len(pred_y)\n if use_cuda:\n loss_val, acc_val = loss_val.cpu(), acc_val.cpu()\n loss_val /= len(test_sampler)\n acc_val /= len(test_sampler)\n # average metrics across ranks and save to history\n with device.subproc('Averaging metrics across ranks (allreduce)'):\n loss_val_hist[epoch] = metric_average(loss_val, 'avg_loss', hvd)\n acc_val_hist[epoch] = metric_average(acc_val, 'avg_accuracy', hvd)\n # log device-wise and average metrics\n device.message('Metrics on rank: loss_val={:f}, acc_val={:f}'\n .format(loss_val.item(), acc_val.item()))\n device.message('Average metrics: loss_val={:f}, acc_val={:f}'\n .format(loss_val_hist[epoch], acc_val_hist[epoch]))\n device.ended('Validating on test set')\n\n # only show average on console\n console.message(f'Epoch {epoch:2d}: '\n f'loss_val={loss_val_hist[epoch]:f}, '\n f'acc_val={acc_val_hist[epoch]:f}, '\n f'elapsed={device.elapsed_shallowest:f} sec')\n device.ended(f'Epoch: {epoch}')\n device.ended('**** EPOCH LOOP ****')\n console.ended('**** EPOCH LOOP ****')\n\n # send model and data back to CPU\n if use_cuda:\n with console.subproc('Sending model and history back to cpu'):\n model.cpu()\n loss_val_hist = loss_val_hist.cpu()\n acc_val_hist = acc_val_hist.cpu()\n\n # return history\n return {'loss_val': loss_val_hist.numpy().tolist(),\n 'acc_val_hist': acc_val_hist.numpy().tolist()}","def train(self, batch):\n pass","def __init__(self, options, path):\n print('Prepare the network and data.')\n self._options = options\n self._path = path\n self._epoch = 0\n\n self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n # Network.\n network = SCNN()\n #weight_init(network)\n network = network.to(self.device)\n # self._net = network.cuda()\n self._net = network\n #self._net = torch.nn.DataParallel(network)\n\n logspaced_LR = np.logspace(-1, -4, self._options['epochs'])\n # Load the model from disk.\n checkpoints_list = os.listdir(self._path['model'])\n if len(checkpoints_list) != 0:\n self._net.load_state_dict(torch.load(\n os.path.join(self._path['model'], '%s%s%s' % ('net_params', str(len(checkpoints_list) - 1), '.pkl'))))\n self._epoch = len(checkpoints_list)\n self._options['base_lr'] = logspaced_LR[len(checkpoints_list)]\n # self._net.load_state_dict(torch.load(self._path['model']))\n print(self._net)\n # Criterion.\n self._criterion = torch.nn.CrossEntropyLoss().cuda()\n # Solver.\n self._solver = torch.optim.SGD(\n self._net.parameters(), lr=self._options['base_lr'],\n momentum=0.9, weight_decay=self._options['weight_decay'])\n # self._solver = torch.optim.Adam(\n # self._net.parameters(), lr=self._options['base_lr'],\n # weight_decay=self._options['weight_decay'])\n lambda1 = lambda epoch: logspaced_LR[epoch]\n self._scheduler = torch.optim.lr_scheduler.LambdaLR(self._solver, lr_lambda=lambda1)\n\n self.train_transforms = torchvision.transforms.Compose([\n torchvision.transforms.Resize(size=256), # Let smaller edge match\n torchvision.transforms.RandomHorizontalFlip(),\n torchvision.transforms.RandomCrop(size=224),\n torchvision.transforms.ToTensor(),\n torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),\n std=(0.229, 0.224, 0.225))\n ])\n self.test_transforms = torchvision.transforms.Compose([\n torchvision.transforms.Resize(size=256),\n torchvision.transforms.CenterCrop(size=224),\n torchvision.transforms.ToTensor(),\n torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),\n std=(0.229, 0.224, 0.225))\n ])\n self.train_data = ImageDataset(csv_file=os.path.join(path['kadis'], 'train.txt'),\n img_dir=os.path.join(path['kadis'], 'dist_imgs'),\n transform=self.train_transforms,\n test=False)\n self._train_loader = torch.utils.data.DataLoader(\n self.train_data, batch_size=self._options['batch_size'],\n shuffle=True, num_workers=1, pin_memory=True)\n self.test_data = ImageDataset(csv_file=os.path.join(path['kadis'], 'test.txt'),\n img_dir=os.path.join(path['kadis'], 'dist_imgs'),\n transform=self.test_transforms,\n test=True)\n self._test_loader = torch.utils.data.DataLoader(\n self.test_data, batch_size=self._options['batch_size'],\n shuffle=False, num_workers=1, pin_memory=True)","def train(self, dataset):\n print_interval = 100\n train_accuracy = 0\n train_loss = 0\n num_examples_trained = 0\n # Put model into training mode\n self.model.train()\n # Step through dataset using pytorch data loader, and a tqdm progress bar\n for num, batch in enumerate(tqdm(dataset.loader)):\n xs, ys = batch\n batch_size = len(xs)\n num_examples_trained += batch_size\n iloss, iaccuracy = self.model(xs, ys)\n loss = iloss\n # Zero out the gradient\n self.optimizer.zero_grad()\n # Perform backwards pass (backprop)\n loss.backward()\n # Step the optimizer\n self.optimizer.step()\n train_loss += iloss.cpu().data.numpy().item() * batch_size\n train_accuracy += iaccuracy.cpu().data.numpy().item() * batch_size\n # Print results info\n if num % print_interval == 0:\n print(\"training loss: \" + str(iloss.cpu().data.numpy().item()))\n print(\"training acc: \" + str(iaccuracy.cpu().data.numpy().item()))\n train_accuracy = train_accuracy / num_examples_trained\n train_loss = train_loss / num_examples_trained\n # Increment current epoch number\n self.cur_epoch += 1\n # Return accuracy and loss\n return train_accuracy, train_loss","def train(self, epochs=1, loader=None, reset_optimizer=False):\n if reset_optimizer:\n self.optimizer = self.optimizer_fn(self.tr_model.parameters()) \n \n # start training the worker using local dataset\n self.tr_model.train() \n running_loss, samples = 0.0, 0\n \n # check if a dataloader was provided for training\n loader = self.tr_loader if not loader else loader\n end_training = False\n itr = 0\n for ep in range(epochs):\n # train next epoch\n for i, x, y in loader: \n itr += 1 \n x, y = x.to(device), y.to(device)\n \n self.optimizer.zero_grad()\n \n loss = nn.CrossEntropyLoss()(self.tr_model(x), y)\n \n running_loss += loss.item()*y.shape[0]\n samples += y.shape[0]\n \n loss.backward()\n self.optimizer.step() \n\n # check for early stopping criteria\n if (self.early_stop != -1) and (itr % 5 == 0):\n print(\"Checking early stop criteria...\")\n accuracy = self.evaluate()[\"accuracy\"]\n if accuracy >= self.early_stop:\n print(\"Stopping criteria reached for worker {}...\".format(self.id))\n end_training = True\n break\n # check if early stop criteria was reached\n if end_training:\n break\n train_stats = {\"loss\" : running_loss / samples}\n \n # return training statistics\n return train_stats","def train_nlp(model: ContinualModel, dataset: ContinualDataset,\n args: Namespace) -> None:\n model.net.to(model.device)\n results, results_mask_classes = [], []\n \n model_stash = create_stash(model, args, dataset)\n \n if args.csv_log:\n csv_logger = CsvLogger(dataset.SETTING, dataset.NAME, model.NAME)\n if args.tensorboard:\n tb_logger = TensorboardLogger(args, dataset.SETTING, model_stash)\n model_stash['tensorboard_name'] = tb_logger.get_name()\n \n dataset_copy = get_dataset(args)\n \n for t in range(dataset.N_TASKS):\n model.net.train()\n _, _ = dataset_copy.get_data_loaders()\n if model.NAME != 'icarl' and model.NAME != 'pnn':\n # for forward transfer calculation\n random_results_class, random_results_task = evaluate_nlp(model, dataset_copy)\n \n print(file=sys.stderr)\n # start time\n start_time = time.time()\n for t in range(dataset.N_TASKS):\n model.net.train()\n train_loader, test_loader = dataset.get_data_loaders()\n if hasattr(model, 'begin_task'):\n model.begin_task(dataset)\n if t:\n accs = evaluate_nlp(model, dataset, last=True)\n results[t - 1] = results[t - 1] + accs[0]\n if dataset.SETTING == 'class-il':\n results_mask_classes[t - 1] = results_mask_classes[t - 1] + accs[1]\n for epoch in range(args.n_epochs):\n for i, data in enumerate(train_loader):\n if hasattr(dataset.train_loader.dataset, 'logits'):\n # todo: to add logits\n pass\n else:\n xs, ys, x_token_idxs, x_token_masks, y_token_idxs, y_token_masks, y_idxs = data\n \n x_token_idxs = x_token_idxs.to(model.device)\n x_token_masks = x_token_masks.to(model.device)\n y_token_idxs = y_token_idxs.to(model.device)\n y_token_masks = y_token_masks.to(model.device)\n y_idxs = y_idxs.to(model.device)\n task_id = torch.tensor(t, dtype=torch.int64, requires_grad=False)\n task_id = task_id.to(model.device)\n \n if model.require_task_id:\n loss = model.observe(inputs=x_token_idxs, inputs_mask=x_token_masks, labels=y_idxs,\n labels_name=y_token_idxs, labels_mask=y_token_masks, task_labels=task_id)\n else:\n loss = model.observe(inputs=x_token_idxs, inputs_mask=x_token_masks, labels=y_idxs,\n labels_name=y_token_idxs, labels_mask=y_token_masks)\n \n progress_bar(i, len(train_loader), epoch, t, loss)\n \n if args.tensorboard:\n tb_logger.log_loss(loss, args, epoch, t, i)\n \n model_stash['batch_idx'] = i + 1\n model_stash['epoch_idx'] = epoch + 1\n model_stash['batch_idx'] = 0\n \n model_stash['task_idx'] = t + 1\n model_stash['epoch_idx'] = 0\n \n if hasattr(model, 'end_task'):\n model.end_task(dataset)\n \n # reduce the running freq\n # if (t+1) % args.eval_freq == 0:\n accs = evaluate_nlp(model, dataset)\n results.append(accs[0])\n results_mask_classes.append(accs[1])\n \n mean_acc = np.mean(accs, axis=1)\n print_mean_accuracy(mean_acc, t + 1, dataset.SETTING)\n \n # prob_model\n if args.prob_all_tasks:\n if args.prob_type != \"\":\n for prob_l in range(12):\n prob_l += 1\n if args.prob_type == \"proto\":\n p_accs = prob_proto_nlp(model, dataset, prob_l=prob_l)\n else:\n p_accs = prob_final_nlp(model, dataset, prob_l=prob_l)\n p_mean_acc = np.mean(p_accs, axis=1)\n print(\"task {} prob_l {}: mean_acc {}, masked_mean_acc {}\".format(t + 1,\n prob_l,\n round(p_mean_acc[0], 2),\n round(p_mean_acc[1], 2)))\n if args.csv_log:\n csv_logger.log_task_prob(p_mean_acc, t)\n \n model_stash['mean_accs'].append(mean_acc)\n if args.csv_log:\n csv_logger.log(mean_acc)\n if args.tensorboard:\n tb_logger.log_accuracy(np.array(accs), mean_acc, args, t)\n \n running_time = time.time() - start_time\n \n # prob_model\n if args.prob_type != \"\" and not args.prob_all_tasks:\n for prob_l in range(12):\n prob_l += 1\n if args.prob_type == \"proto\":\n accs = prob_proto_nlp(model, dataset, prob_l=prob_l)\n else:\n accs = prob_final_nlp(model, dataset, prob_l=prob_l)\n mean_acc = np.mean(accs, axis=1)\n print(\"prob_l {}: mean_acc {}, masked_mean_acc {}\".format(prob_l, mean_acc[0], mean_acc[1]))\n if args.csv_log:\n csv_logger.log_prob(mean_acc)\n \n if args.csv_log:\n csv_logger.add_bwt(results, results_mask_classes)\n csv_logger.add_running_time(running_time)\n csv_logger.add_forgetting(results, results_mask_classes)\n if model.NAME != 'icarl' and model.NAME != 'pnn':\n csv_logger.add_fwt(results, random_results_class,\n results_mask_classes, random_results_task)\n \n if args.tensorboard:\n tb_logger.close()\n if args.csv_log:\n csv_logger.write(vars(args))","def _train(args): \n\n #device = 'cuda' if torch.cuda.is_available() else 'cpu'\n device = 'cpu'\n logger.info(\"Device Type: {}\".format(device))\n\n logger.info(\"Loading SUN360 dataset\")\n transform = transforms.Compose(\n [transforms.Resize((224,224)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\n target_transform = transforms.Compose([transforms.Resize((224,224)),\n transforms.ToTensor()]) \n\n trainset = SUN360Dataset(\"imagedata.json\",transform = transform, target_transform = target_transform)\n train_loader = DataLoader(trainset, batch_size=args.batch_size,\n shuffle=True, num_workers=args.workers)\n \"\"\"\n testset = torchvision.datasets.CIFAR10(root=args.data_dir, train=False,\n download=False, transform=transform)\n test_loader = DataLoader(testset, batch_size=args.batch_size,\n shuffle=False, num_workers=args.workers)\n \"\"\" \n\n logger.info(\"Model loaded\")\n model = EfficientNet.from_name('efficientnet-b0',conv_type='Equi')\n\n if torch.cuda.device_count() > 1:\n logger.info(\"Gpu count: {}\".format(torch.cuda.device_count()))\n model = nn.DataParallel(model)\n\n model = model.to(device)\n\n criterion = CELoss().to(device)\n optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n\n for epoch in range(0, args.epochs):\n running_loss = 0.0\n for i, data in enumerate(train_loader):\n # get the inputs\n inputs, EM , CM = data\n inputs, EM, CM = inputs.to(device), EM.to(device), CM.to(device)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward + backward + optimize\n outputs = model(inputs)\n EMLoss, CMLoss = map_loss(outputs,EM,CM,criterion)\n loss = EMLoss + CMLoss\n loss.backward()\n optimizer.step()\n\n # print statistics\n running_loss += loss.item()\n if i % 2000 == 1999: # print every 2000 mini-batches\n print('[%d, %5d] loss: %.3f' %\n (epoch + 1, i + 1, running_loss / 2000))\n running_loss = 0.0\n print('Finished Training')\n return _save_model(model, args.model_dir)","def train(self, *args, **kwargs):\n raise NotImplementedError","def train(self, train_dataset):\n\n # check fine_tuning option\n model_path = os.path.join(self.check_point, 'model.pt')\n if self.fine_tune and not os.path.exists(model_path):\n raise Exception('Cannot find %s.' %model_path)\n elif self.fine_tune and os.path.exists(model_path):\n if self.verbose:\n pass\n self.model = torch.load(model_path)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n \n # capture best model\n best_val_psnr = -1\n best_model_state = self.model.state_dict()\n\n # Train the model\n for epoch in range(self.num_epochs):\n self._epoch_step(train_dataset, epoch)\n self.scheduler.step()\n\n\n\n # capture running PSNR on train and val dataset\n train_psnr, train_ssim, _, _ = self._check_PSNR(train_dataset)\n self.hist_train_psnr.append(train_psnr)\n \n\n \n # write the model to hard-disk for testing\n if not os.path.exists(self.check_point):\n os.makedirs(self.check_point)\n model_path = os.path.join(self.check_point, 'model.pt')\n torch.save(self.model, model_path)","def train():\n # YOUR TRAINING CODE GOES HERE","def train(self, X_train, y_train):\n self.model.fit(X_train, y_train)","def train(\n train_dataloader: torch.utils.data.dataloader,\n model: nn.Module,\n loss_function: nn.Module,\n optimizer: torch.optim.Optimizer,\n device: torch.device,\n scheduler=None,\n):\n model.train()\n\n total_loss = 0\n\n for step, batch in tqdm(enumerate(train_dataloader)):\n\n if step % 50 == 0 and not step == 0:\n print(\" Batch {:>5,} of {:>5,}.\".format(step, len(train_dataloader)))\n\n data = batch[\"data\"].to(device)\n label = batch[\"label\"].to(device)\n model.zero_grad()\n\n preds = model(data)\n\n loss = loss_function(preds, label)\n\n total_loss = total_loss + loss.item()\n\n loss.backward()\n\n optimizer.step()\n # scheduler.step()","def train():\n\t# 1、make dataloader\n\ttrain_loader, val_loader, num_query, num_class = make_data_loader(cfg)\n\t#print(\"num_query:{},num_class:{}\".format(num_query,num_class))\n\n\t# 2、make model\n\tmodel = build_model(cfg, num_class)\n\n\t# model.eval()\n\t# x = model(img_tensor)\n\t# print(x.shape)\n\t# 3、 make optimizer\n\toptimizer = make_optimizer(cfg, model)\n\n\t# 4、 make lr_scheduler\n\tscheduler = make_lr_scheduler(cfg, optimizer)\n\n\t# 5、 make loss_func\n\tif cfg.MODEL.PCB_NECK:\n\t\t# make loss specificially for pcb \n\t\tloss_func = get_softmax_triplet_loss_fn(cfg, num_class)\n\telse:\n\t\tloss_func = make_loss(cfg, num_class)\n\n\t# get paramters\n\tlog_period = cfg.OUTPUT.LOG_PERIOD \n\tckpt_period =cfg.OUTPUT.CHECKPOINT_PERIOD\n\teval_period = cfg.OUTPUT.EVAL_PERIOD\n\toutput_dir = cfg.OUTPUT.ROOT_DIR\n\tdevice = cfg.MODEL.DEVICE\n\tepochs = cfg.SOLVER.MAX_EPOCHS\n\tuse_gpu = device == \"cuda\"\n\tuse_neck = cfg.MODEL.NECK or cfg.MODEL.LEARN_REGION \n\t# how many batch for each log\n\tbatch_size = cfg.SOLVER.IMGS_PER_BATCH\n\tbatch_num = len(train_loader) \n\t\n\tlog_iters = batch_num // log_period\n\tpretrained = cfg.MODEL.PRETRAIN_PATH != ''\n\tparallel = cfg.MODEL.PARALLEL \t\n\tgrad_clip = cfg.DARTS.GRAD_CLIP \n\n\tfeat_norm = cfg.TEST.FEAT_NORM \n\tckpt_save_path = cfg.OUTPUT.ROOT_DIR + cfg.OUTPUT.CKPT_DIR\n\tif not os.path.exists(ckpt_save_path):\n\t\tos.makedirs(ckpt_save_path)\n\n\n\t# create *_result.xlsx\n\t# save the result for analyze\n\tname = (cfg.OUTPUT.LOG_NAME).split(\".\")[0] + \".xlsx\"\n\tresult_path = cfg.OUTPUT.ROOT_DIR + name\n\n\twb = xl.Workbook()\n\tsheet = wb.worksheets[0]\n\ttitles = ['size/M','speed/ms','final_planes', 'acc', 'mAP', 'r1', 'r5', 'r10', 'loss',\n\t\t\t 'acc', 'mAP', 'r1', 'r5', 'r10', 'loss','acc', 'mAP', 'r1', 'r5', 'r10', 'loss']\n\tsheet.append(titles)\n\tcheck_epochs = [40, 80, 120, 160, 200, 240, 280, 320, 360, epochs]\n\tvalues = []\n\n\tlogger = logging.getLogger('MobileNetReID.train')\n\t\n\t# count parameter\n\tsize = count_parameters(model)\n\tlogger.info(\"the param number of the model is {:.2f} M\".format(size))\n\t\n\tvalues.append(format(size, '.2f'))\n\tvalues.append(model.final_planes)\n\n\tlogger.info(\"Start training\")\n\t\n\t#count = 183, x, y = batch -> 11712 for train\n\tif pretrained:\n\t\tstart_epoch = model.start_epoch\n\n\tif parallel:\n\t\tmodel = nn.DataParallel(model)\n\n\tif use_gpu:\n\t\t# model = nn.DataParallel(model)\n\t\tmodel.to(device)\n\t\n\t# save the best model\n\tbest_mAP, best_r1 = 0., 0.\n\tis_best = False\n\t# batch : img, pid, camid, img_path\n\tavg_loss, avg_acc = RunningAverageMeter(), RunningAverageMeter()\n\tavg_time, global_avg_time = AverageMeter(), AverageMeter()\n\tglobal_avg_time.reset()\n\tfor epoch in range(epochs):\n\t\tscheduler.step()\n\n\t\tif pretrained and epoch < start_epoch - 1:\n\t\t\tcontinue\n\t\n\t\tmodel.train()\n\t\t# sum_loss, sum_acc = 0., 0.\n\t\tavg_loss.reset()\n\t\tavg_acc.reset()\n\t\tavg_time.reset()\n\t\tfor i, batch in enumerate(train_loader):\n\n\t\t\tt0 = time.time()\n\t\t\timgs,labels = batch\n\n\t\t\tif use_gpu:\n\t\t\t\timgs = imgs.to(device)\n\t\t\t\tlabels = labels.to(device)\n\n\t\t\tres = model(imgs)\n\t\t\t# score, feat = model(imgs)\n\t\t\t# loss = loss_func(score, feat, labels)\n\t\t\tloss, acc = compute_loss_acc(use_neck, res, labels, loss_func)\n\t\t\t\n\t\t\tloss.backward()\n\t\t\tif grad_clip != 0:\n\t\t\t\tnn.utils.clip_grad_norm(model.parameters(), grad_clip)\n\n\t\t\toptimizer.step()\n\n\t\t\toptimizer.zero_grad()\n\n\t\t\t# acc = (score.max(1)[1] == labels).float().mean()\n\n\t\t\t# sum_loss += loss\n\t\t\t# sum_acc += acc \n\t\t\tt1 = time.time()\n\t\t\tavg_time.update((t1 - t0) / batch_size)\n\t\t\tavg_loss.update(loss)\n\t\t\tavg_acc.update(acc)\n\n\t\t\t#log the info \n\t\t\tif (i+1) % log_iters == 0:\n\n\t\t\t\tlogger.info(\"epoch {}: {}/{} with loss is {:.5f} and acc is {:.3f}\".format(\n\t\t\t\t\t epoch+1, i+1, batch_num, avg_loss.avg, avg_acc.avg))\n\n\t\tlr = optimizer.state_dict()['param_groups'][0]['lr']\n\t\tlogger.info(\"end epochs {}/{} with lr: {:.5f} and avg_time is {:.3f} ms\".format(epoch+1, epochs, lr, avg_time.avg * 1000))\n\t\tglobal_avg_time.update(avg_time.avg)\n\t\t# change the lr \n\n\t\t# eval the model \n\t\tif (epoch+1) % eval_period == 0 or (epoch + 1) == epochs :\n\t\t\t\n\t\t\tmodel.eval()\n\t\t\tmetrics = R1_mAP(num_query, use_gpu = use_gpu, feat_norm = feat_norm)\n\n\t\t\twith torch.no_grad():\n\n\t\t\t\tfor vi, batch in enumerate(val_loader):\n\t\t\t\t\t\n\t\t\t\t\timgs, labels, camids = batch\n\n\t\t\t\t\tif use_gpu:\n\t\t\t\t\t\timgs = imgs.to(device)\n\n\t\t\t\t\tfeats = model(imgs)\n\t\t\t\t\tmetrics.update((feats,labels, camids))\n\n\t\t\t\t#compute cmc and mAP\n\t\t\t\tcmc, mAP = metrics.compute()\n\t\t\t\tlogger.info(\"validation results at epoch:{}\".format(epoch + 1))\n\t\t\t\tlogger.info(\"mAP:{:.2%}\".format(mAP))\n\t\t\t\tfor r in [1,5,10]:\n\t\t\t\t\tlogger.info(\"CMC curve, Rank-{:<3}:{:.2%}\".format(r,cmc[r-1]))\t\n\n\t\t\t\t# determine whether cur model is the best \n\t\t\t\tif mAP > best_mAP:\n\t\t\t\t\tis_best = True\n\t\t\t\t\tbest_mAP = mAP\n\t\t\t\t\tlogger.info(\"Get a new best mAP\")\n\t\t\t\tif cmc[0] > best_r1:\n\t\t\t\t\tis_best = True\n\t\t\t\t\tbest_r1 = cmc[0]\n\t\t\t\t\tlogger.info(\"Get a new best r1\")\n\n\t\t\t\t# add the result to sheet\n\t\t\t\tif (epoch + 1) in check_epochs:\n\t\t\t\t\tval = [avg_acc.avg, mAP, cmc[0], cmc[4], cmc[9]]\n\t\t\t\t\tchange = [format(v * 100, '.2f') for v in val]\n\t\t\t\t\tchange.append(format(avg_loss.avg, '.3f'))\n\t\t\t\t\tvalues.extend(change)\n\n\n\t\t# we hope that eval_period == ckpt_period or eval_period == k* ckpt_period where k is int\t\t\t\n\t\t# whether to save the model\n\t\tif (epoch+1) % ckpt_period == 0 or is_best:\n\n\t\t\tif parallel:\n\t\t\t\ttorch.save(model.module.state_dict(), ckpt_save_path + \"checkpoint_{}.pth\".format(epoch + 1 ))\n\t\t\telse:\n\t\t\t\ttorch.save(model.state_dict(), ckpt_save_path + \"checkpoint_{}.pth\".format(epoch + 1 ))\n\n\t\t\tlogger.info(\"checkpoint {} saved !\".format(epoch + 1))\n\n\t\t\tif is_best:\n\t\t\t\tif parallel:\n\t\t\t\t\ttorch.save(model.module.state_dict(), ckpt_save_path + \"best_ckpt.pth\")\n\t\t\t\telse:\n\t\t\t\t\ttorch.save(model.state_dict(), ckpt_save_path + \"best_ckpt.pth\")\n\t\t\t\tlogger.info(\"best checkpoint was saved\")\n\t\t\t\tis_best = False\n\t\n\tvalues.insert(1, format(global_avg_time.avg * 1000, '.2f'))\n\tsheet.append(values)\n\twb.save(result_path)\n\n\tlogger.info(\"training is end, time for per imgs is {} ms\".format(global_avg_time.avg *1000))","def train_model(model, dataloaders, criterion, optimizer, args, start_epoch=1, num_epochs=25):\n\n # Initializes Session History in the history file\n init_session_history(args)\n since = time.time()\n\n train_acc_history = []\n val_acc_history = []\n train_loss_history = []\n val_loss_history = []\n train_f1_score = []\n val_f1_score = []\n plot_epoch = []\n\n best_model_wts = copy.deepcopy(model.state_dict())\n best_acc = 0.0\n\n for epoch in range(start_epoch, num_epochs):\n\n # Each epoch has a training and validation phase\n for phase in ['train', 'val']:\n if phase == 'train':\n model.train() # Set model to training mode\n train_pred_classes = []\n train_ground_truths = []\n else:\n model.eval() # Set model to evaluate mode\n val_pred_classes = []\n val_ground_truths = []\n\n running_loss = 0.0\n running_corrects = 0\n train_n_total = 1\n\n pbar = tqdm(dataloaders[phase])\n # Iterate over data.\n for sample in pbar:\n inputs = sample[\"video\"]\n labels = sample[\"action\"]\n inputs = inputs.to(device)\n labels = labels.to(device)\n\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward\n # track history if only in train\n with torch.set_grad_enabled(phase == 'train'):\n\n outputs = model(inputs)\n loss = criterion(outputs, torch.max(labels, 1)[1])\n\n _, preds = torch.max(outputs, 1)\n #print(preds)\n #print(torch.max(labels, 1)[1])\n\n if phase == 'train':\n train_pred_classes.extend(preds.detach().cpu().numpy())\n train_ground_truths.extend(torch.max(labels, 1)[1].detach().cpu().numpy())\n else:\n val_pred_classes.extend(preds.detach().cpu().numpy())\n val_ground_truths.extend(torch.max(labels, 1)[1].detach().cpu().numpy())\n\n # backward + optimize only if in training phase\n if phase == 'train':\n loss.backward()\n optimizer.step()\n\n # statistics\n running_loss += loss.item() * inputs.size(0)\n running_corrects += torch.sum(preds == torch.max(labels, 1)[1])\n\n pbar.set_description('Phase: {} || Epoch: {} || Loss {:.5f} '.format(phase, epoch, running_loss / train_n_total))\n train_n_total += 1\n\n epoch_loss = running_loss / len(dataloaders[phase].dataset)\n epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n\n # Calculate elapsed time\n time_elapsed = time.time() - since\n print(phase, ' training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n\n # For Checkpointing and Confusion Matrix\n if phase == 'val':\n val_acc_history.append(epoch_acc)\n val_loss_history.append(epoch_loss)\n val_pred_classes = np.asarray(val_pred_classes)\n val_ground_truths = np.asarray(val_ground_truths)\n val_accuracy, val_f1, val_precision, val_recall = get_acc_f1_precision_recall(\n val_pred_classes, val_ground_truths\n )\n val_f1_score.append(val_f1)\n val_confusion_matrix = np.array_str(confusion_matrix(val_ground_truths, val_pred_classes, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))\n print('Epoch: {} || Val_Acc: {} || Val_Loss: {}'.format(\n epoch, val_accuracy, epoch_loss\n ))\n print(f'val: \\n{val_confusion_matrix}')\n\n # Deep Copy Model if best accuracy\n if epoch_acc > best_acc:\n best_acc = epoch_acc\n best_model_wts = copy.deepcopy(model.state_dict())\n\n # set current loss to val loss for write history\n val_loss = epoch_loss\n\n if phase == 'train':\n train_acc_history.append(epoch_acc)\n train_loss_history.append(epoch_loss)\n train_pred_classes = np.asarray(train_pred_classes)\n train_ground_truths = np.asarray(train_ground_truths)\n train_accuracy, train_f1, train_precision, train_recall = get_acc_f1_precision_recall(\n train_pred_classes, train_ground_truths\n )\n train_f1_score.append(train_f1)\n train_confusion_matrix = np.array_str(confusion_matrix(train_ground_truths, train_pred_classes, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))\n print('Epoch: {} || Train_Acc: {} || Train_Loss: {}'.format(\n epoch, train_accuracy, epoch_loss\n ))\n print(f'train: \\n{train_confusion_matrix}')\n plot_epoch.append(epoch)\n\n # set current loss to train loss for write history\n train_loss = epoch_loss\n\n # Save Weights\n model_name = save_weights(model, args, epoch, optimizer)\n\n # Write History after train and validation phase\n write_history(\n args.history_path,\n model_name,\n train_loss,\n val_loss,\n train_accuracy,\n val_accuracy,\n train_f1,\n val_f1,\n train_precision,\n val_precision,\n train_recall,\n val_recall,\n train_confusion_matrix,\n val_confusion_matrix\n )\n\n time_elapsed = time.time() - since\n print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n print('Best val Acc: {:4f}'.format(best_acc))\n\n # load best model weights\n model.load_state_dict(best_model_wts)\n return model, train_loss_history, val_loss_history, train_acc_history, val_acc_history, train_f1_score, val_f1_score, plot_epoch","def _train(self):\n self.network.train() # note that here we are calling torch.nn.Module train class method\n epochs_since_improvement = 0\n best_params = None\n self.calculate_validation_loss()\n best_validation_loss = self.validation_average_loss\n\n while epochs_since_improvement < 10:\n self.train_epoch()\n self.calculate_validation_loss()\n if self.validation_average_loss < best_validation_loss:\n epochs_since_improvement = 0\n best_validation_loss = self.validation_average_loss\n best_params = self.network.state_dict()\n else:\n epochs_since_improvement += 1\n LOGGER.info(\"Epochs since improvement in validation_loss: {} \\n\".format(epochs_since_improvement))\n if self.maximum_epochs_allowed is not None and self.epochs_trained >= self.maximum_epochs_allowed:\n break\n LOGGER.info(\"Training complete after {} epochs \\n\".format(self.epochs_trained))\n LOGGER.info(\"Best training loss achieved: {} \\n\".format(self.training_average_loss))\n LOGGER.info(\"Best validation loss achieved: {}\".format(self.validation_average_loss))\n self.learned_params = best_params\n self.network.load_state_dict(best_params)","def train():\n import training\n\n # Ensure output directories exist\n os.makedirs(os.path.dirname(cfg.scaler_path), exist_ok=True)\n os.makedirs(cfg.model_path, exist_ok=True)\n os.makedirs(cfg.log_path, exist_ok=True)\n\n # Load (standardized) input data and target values\n tr_x, tr_y, _ = _load_data(cfg.training_set, is_training=True)\n val_x, val_y, _ = _load_data(cfg.validation_set)\n\n # Try to create reproducible results\n np.random.seed(cfg.initial_seed)\n\n # Save free parameters to disk\n utils.log_parameters(cfg.training, os.path.join(cfg.model_path,\n 'parameters.json'))\n\n training.train(tr_x, tr_y, val_x, val_y)","def train(model, optimizer, loss_fn, dataloader, epoch):\n\n # Set the model into train mode\n model.train()\n\n train_loss = 0\n correct = 0\n total = 0\n datacount = len(dataloader)\n\n for batch_idx, (train_batch, labels_batch) in enumerate(dataloader):\n\n # move the data onto the device\n train_batch, labels_batch = train_batch.to(device), labels_batch.to(device)\n\n # # convert to torch Variables\n # train_batch, labels_batch = Variable(train_batch), Variable(labels_batch)\n\n # clear the previous grad \n optimizer.zero_grad()\n\n # compute model outputs and loss\n outputs = model(train_batch)\n loss = loss_fn(outputs, labels_batch)\n loss.backward()\n\n # after computing gradients based on current batch loss,\n # apply them to parameters\n optimizer.step()\n\n train_loss += loss.item()\n _, predicted = outputs.max(1)\n total += labels_batch.size(0)\n correct += predicted.eq(labels_batch).sum().item()\n # get learning rate\n current_lr = get_lr(optimizer=optimizer)\n\n # write to tensorboard\n writer.add_scalar('train/loss', train_loss/(batch_idx+1), (datacount * (epoch+1)) + (batch_idx+1))\n writer.add_scalar('train/accuracy', 100.*correct/total, (datacount * (epoch+1)) + (batch_idx+1))\n writer.add_scalar('Learning rate', current_lr)\n\n progress_bar(batch_idx, len(dataloader), 'Train Loss: %.3f | Train Acc: %.3f%% (%d/%d)'\n % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))","def forward_train(self, *args, **kwargs):\n pass","def training(self):\n self.model.fit(self.train_x, self.train_y)","def train(args, mesh_obj, model, opt=None, iters=10000, device='cuda', liveplot=False, run_label=None):\n criterion = lambda x, z: torch.mean(torch.relu(x) - x * z + torch.log(1 + torch.exp(-torch.abs(x))))\n\n data_iter = tqdm(range(1, iters + 1))\n if run_label is not None:\n data_iter.set_description(run_label)\n\n mesh, corners = mesh_obj\n c0, c1 = [torch.tensor(t, dtype=torch.float32) for t in corners]\n render_args_hr[0] = [torch.tensor(t, dtype=torch.float32) for t in render_args_hr[0]]\n render_args_lr[0] = [torch.tensor(t, dtype=torch.float32) for t in render_args_lr[0]]\n render_args_hr[1] = [c0, c1]\n render_args_lr[1] = [c0, c1]\n\n c1, c0 = c1.to(device), c0.to(device)\n \n step_list = []\n loss_list = []\n step_time_list = []\n test_psnr_list = []\n\n model = model.to(device)\n\n # Main training Loop\n postfix = {'loss': np.inf, 'psnr': 0., 'forward_steps': 0}\n for i in data_iter:\n start_time = time.time()\n \n inputs = torch.rand(args.train_batch_size, 3).to(device) * (c1 - c0) + c0\n z_init = torch.zeros(1, model.interm_channels).to(device)\n\n target = torch.tensor(gt_fn(inputs.cpu().numpy(), mesh), dtype=torch.bool).to(device)\n \n model_outputs = model(inputs, z_init, skip_solver=False, verbose=False, include_grad=False)\n\n pred = torch.sigmoid(model_outputs['output'].squeeze())\n loss = criterion(model_outputs['output'].squeeze(), target.float())\n loss_list.append(loss.item())\n\n if opt:\n opt.zero_grad()\n loss.backward()\n\n opt.step()\n \n postfix['forward_steps'] = model_outputs['forward_steps']\n postfix['loss'] = loss.item()\n \n step_time = time.time() - start_time\n step_time_list.append(step_time)\n\n do_log = i % args.log_freq == 0\n do_vis = i % args.vis_freq == 0\n\n if i % args.save_freq == 0:\n torch.save(model.state_dict(), f'{args.save_dir:s}/model_step{i:d}.pth')\n\n if do_log:\n summary_dict = {\n 'train_loss': loss_list,\n 'step_time': step_time_list\n }\n\n with open('{:s}/summary.pkl'.format(args.log_dir), 'wb') as summary_f:\n pickle.dump(summary_dict, summary_f)\n \n if do_vis:\n with torch.no_grad():\n depth_map, acc_map = render_rays_native_hier(model, *render_args_lr, device=device)\n normal_map = make_normals(render_args_lr[0], depth_map.cpu()) * 0.5 + 0.5\n\n fig, axes = plt.subplots(1, 3)\n fig.set_size_inches(6 * 3, 6)\n\n for ax in axes:\n ax.clear()\n ax.set_axis_off()\n\n axes[0].imshow(depth_map.squeeze().cpu().numpy())\n axes[1].imshow(acc_map.squeeze().cpu().numpy())\n axes[2].imshow(normal_map.squeeze().cpu().numpy())\n\n fig.set_tight_layout(True)\n fig.savefig(args.vis_dir + '/vis_step{:d}.png'.format(i))\n\n data_iter.set_postfix(postfix)\n\n with torch.no_grad():\n depth_map, acc_map = render_rays_native_hier(model, *render_args_hr, device=device)\n normal_map = make_normals(render_args_hr[0], depth_map.cpu()) * 0.5 + 0.5\n\n skimage.io.imsave(args.vis_dir + '/final_rendered.png', normal_map.squeeze().cpu().numpy())\n\n return {\n 'loss': loss_list,\n 'step_time': step_time_list,\n 'step': step_list,\n 'test_psnr': test_psnr_list,\n }","def train_model(\n checkpoint: int,\n checkpoint_step: int,\n data_loader: torch.utils.data.DataLoader,\n device: torch.device,\n epoch: int,\n experiment: str,\n max_norm: float,\n model: lmp.model.BaseRNNModel,\n optimizer: Union[\n torch.optim.SGD,\n torch.optim.Adam,\n ],\n vocab_size: int\n):\n # Set experiment output folder.\n file_dir = f'{lmp.path.DATA_PATH}/{experiment}'\n\n if not os.path.exists(file_dir):\n os.makedirs(file_dir)\n\n # Set experiment log folder.\n writer = torch.utils.tensorboard.SummaryWriter(\n f'{lmp.path.DATA_PATH}/log/{experiment}'\n )\n\n # Define objective function.\n criterion = torch.nn.CrossEntropyLoss()\n\n # Step = number of updates.\n # Every update must increment `step`.\n step = 0\n\n # Set model to train mode.\n model.train()\n\n # Clean up gradient in model parameters.\n model.zero_grad()\n\n # Initialize total loss.\n total_loss = 0.0\n\n for cur_epoch in range(epoch):\n\n epoch_iterator = tqdm(\n data_loader,\n desc=f'epoch: {cur_epoch}, loss: {0:.6f}'\n )\n\n for x, y in epoch_iterator:\n # Increment step for each update.\n step += 1\n\n # Continue training from previous checkpoint step.\n if step < checkpoint:\n continue\n\n # Put tensors on to specified device (CPU or GPU). Reshape `y` into\n # shape (B x S) for cross-entropy.\n # x.size = (B, S)\n # y.size = (B x S)\n x = x.to(device)\n y = y.reshape(-1).to(device)\n\n # Forward pass.\n # pred_y_logits.size = (B, S, V)\n pred_y_logits = model(x)\n\n # Reshape `pred_y_logits` into shape (B x S, V) for cross-entropy.\n pred_y_logits = pred_y_logits.reshape(-1, vocab_size)\n\n # Perform cross-entropy.\n loss = criterion(pred_y_logits, y)\n\n # Calculate total loss.\n total_loss += loss.item()\n\n # Log loss.\n epoch_iterator.set_description(\n f'epoch: {cur_epoch}, loss: {loss.item():.6f}'\n )\n\n # Backward pass.\n loss.backward()\n\n # Perform gradient clipping to avoid gradient explosion.\n torch.nn.utils.clip_grad_norm_(\n model.parameters(),\n max_norm\n )\n\n # Gradient descent.\n optimizer.step()\n\n # `torch` required manually clean up gradient.\n optimizer.zero_grad()\n\n # Save checkpoint for each `checkpoint_step`.\n if step % checkpoint_step == 0:\n torch.save(\n model.state_dict(),\n os.path.join(\n file_dir,\n f'model-{step}.pt'\n )\n )\n torch.save(\n optimizer.state_dict(),\n os.path.join(\n file_dir,\n f'optimizer-{step}.pt'\n )\n )\n # Log average loss.\n writer.add_scalar(\n f'{experiment}/loss',\n total_loss / checkpoint_step,\n step\n )\n total_loss = 0.0\n\n # Save last checkpoint.\n torch.save(\n model.state_dict(),\n os.path.join(\n file_dir,\n f'model-{step}.pt'\n )\n )\n torch.save(\n optimizer.state_dict(),\n os.path.join(\n file_dir,\n f'optimizer-{step}.pt'\n )\n )","def train():\n init_distributed_mode(args)\n save_dir = TRAIN_CFG['save_dir']\n if not os.path.exists(save_dir) and torch.distributed.get_rank() == 0:\n os.mkdir(save_dir)\n kwargs = {}\n # If augmenting data, disable Pytorch's own augmentataion\n # This has to be done manually as augmentation is embedded\n # refer : https://github.com/pytorch/vision/issues/2263\n base_path = DATASET_CFG['base_path']\n train_set = DATASET_CFG['train']\n valid_set = DATASET_CFG['valid']\n dset_mean_std = DATASET_CFG['mean_std']\n if dset_mean_std is not None:\n dataset_mean = [i/255. for i in dset_mean_std[0]]\n dataset_std = [i/255. for i in dset_mean_std[1]]\n else:\n dataset_mean, dataset_std = compute_mean_std(base_path, train_set)\n kwargs['image_mean'] = dataset_mean\n kwargs['image_std'] = dataset_std\n kwargs['min_size'] = DATASET_CFG['min_size']\n kwargs['max_size'] = DATASET_CFG['max_size']\n kwargs['box_detections_per_img'] = 300 # increase max det to max val in our benchmark\n\n # Set benchmark related parameters\n if benchmark == 'ScutHead':\n combined_cfg = {**cfg, **sh_anchors}\n elif benchmark == 'CrowdHuman':\n combined_cfg = {**cfg, **ch_anchors}\n elif benchmark == 'Combined':\n combined_cfg = {**cfg, **combined_anchors}\n else:\n raise ValueError(\"New dataset has to be registered\")\n\n # Create Model\n default_filter = False\n model = customRCNN(cfg=combined_cfg,\n use_deform=NET_CFG['use_deform'],\n ohem=NET_CFG['ohem'],\n context=NET_CFG['context'],\n custom_sampling=NET_CFG['custom_sampling'],\n default_filter=default_filter,\n soft_nms=NET_CFG['soft_nms'],\n upscale_rpn=NET_CFG['upscale_rpn'],\n median_anchors=NET_CFG['median_anchors'],\n **kwargs).cuda() \n model_without_ddp = model\n if args.distributed:\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu],\n find_unused_parameters=True)\n model_without_ddp = model.module\n\n # Create Optimizer\n params = [p for p in model.parameters() if p.requires_grad]\n optimizer = torch.optim.SGD(params, lr=HYP_CFG['learning_rate'],\n momentum=HYP_CFG['learning_rate'],\n weight_decay=HYP_CFG['weight_decay'])\n\n scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,\n milestones=TRAIN_CFG['milestones'],\n gamma=HYP_CFG['gamma'])\n # Restore from checkpoint\n pt_model = TRAIN_CFG['pretrained_model']\n if pt_model:\n model_without_ddp = restore_network(model_without_ddp, pt_model,\n only_backbone=TRAIN_CFG['only_backbone'])\n \n # Create training and vaid dataset\n dataset_param = {'mean': dataset_mean, 'std':dataset_std,\n 'shape':(kwargs['min_size'], kwargs['max_size'])}\n batch_size = HYP_CFG['batch_size']\n train_dataset = HeadDataset(train_set,\n base_path,\n dataset_param,\n train=True)\n val_dataset = HeadDataset(valid_set,\n base_path,\n dataset_param,\n train=False)\n train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)\n train_batch_sampler = torch.utils.data.BatchSampler(train_sampler,\n batch_size,\n drop_last=True)\n train_data_loader = torch.utils.data.DataLoader(train_dataset,\n batch_sampler=train_batch_sampler,\n num_workers=args.num_workers,\n collate_fn=collate_fn)\n\n val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)\n val_batch_sampler = torch.utils.data.BatchSampler(val_sampler,\n batch_size,\n drop_last=True)\n val_data_loader = torch.utils.data.DataLoader(val_dataset,\n batch_sampler=val_batch_sampler,\n num_workers=args.num_workers,\n collate_fn=collate_fn)\n # Fastforward the LR decayer\n start_epoch = TRAIN_CFG['start_epoch']\n max_epoch = TRAIN_CFG['max_epoch']\n for _ in range(0, -1):\n scheduler.step()\n\n # Start training\n print(\"======= Training for \" + str(max_epoch) + \"===========\")\n for epoch in range(start_epoch, int(max_epoch) + 1):\n if epoch % TRAIN_CFG['eval_every'] == 0:\n print(\"========= Evaluating Model ==========\")\n result_dict = evaluate(model, val_data_loader, benchmark=benchmark)\n if torch.distributed.get_rank() == 0:\n logging.info('Eval score at {0} epoch is {1}'.format(str(epoch),\n result_dict))\n \n train_one_epoch(model, optimizer, train_data_loader,\n device, epoch, print_freq=1000)\n scheduler.step()\n if torch.distributed.get_rank() == 0:\n print(\"Saving model\")\n torch.save(model.state_dict(), osp.join(save_dir,\n TRAIN_CFG['exp_name'] + '_epoch_' + str(epoch) + '.pth'))","def setup(self):\n print(\"setup\")\n \n self.modelToUse = 1\n if self.train:\n print(\"train\")\n else:\n print(\"no train\")\n \n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n self.envSize = 17\n \n #init model\n if self.train or not os.path.isfile(\"my-saved-model.pt\"):\n self.logger.info(\"Setting up model from scratch.\")\n if self.modelToUse == 0:\n self.policy_net = Model_global_view(self.envSize, self.envSize, 6).to(device)\n self.model = Model_global_view(self.envSize, self.envSize, 6).to(device)\n elif self.modelToUse == 1:\n self.policy_net = Model_local_view(self.envSize, self.envSize, 6).to(device)\n self.model = Model_local_view(self.envSize, self.envSize, 6).to(device)\n else:\n self.policy_net = Model_combined_view(self.envSize, self.envSize, 6).to(device)\n self.model = Model_combined_view(self.envSize, self.envSize, 6).to(device)\n self.model.load_state_dict(self.policy_net.state_dict())\n self.model.eval()\n else:\n self.logger.info(\"Loading model from saved state.\")\n with open(\"my-saved-model.pt\", \"rb\") as file:\n if self.modelToUse == 0:\n self.model = Model_global_view(self.envSize, self.envSize, 6)\n elif self.modelToUse == 1:\n self.model = Model_local_view(self.envSize, self.envSize, 6)\n else:\n self.model = Model_combined_view(self.envSize, self.envSize, 6)\n if torch.cuda.is_available():\n self.model.load_state_dict(torch.load(file))\n self.model.to(device)\n else:\n self.model.load_state_dict(torch.load(file, map_location=device))","def train(self):\r\n \r\n args = self.args\r\n model = self.model\r\n dataset = self.dataset\r\n train_state = self.train_state\r\n optimizer = self.optimizer\r\n scheduler = self.scheduler\r\n train_bar = self.train_bar\r\n val_bar = self.val_bar\r\n epoch_bar = self.epoch_bar\r\n \r\n for epoch_index in range(args.num_epochs):\r\n train_state['epoch_index'] = epoch_index\r\n \r\n # Iterate over training dataset\r\n \r\n running_loss,running_acc = self.train_loop(epoch_index, args, \r\n model, dataset, \r\n optimizer, train_bar)\r\n \r\n train_state['train_loss'].append(running_loss)\r\n train_state['train_acc'].append(running_acc)\r\n \r\n running_loss,running_acc = self.val_loop(epoch_index, args, model, \r\n dataset, optimizer, val_bar)\r\n \r\n \r\n train_state['val_loss'].append(running_loss)\r\n train_state['val_acc'].append(running_acc)\r\n \r\n print(\"Epoch \"+str(epoch_index+1)+\": Running loss=\"+ \\\r\n str(running_loss)+\", Running Acc=\"+str(running_acc))\r\n \r\n train_state = update_train_state(args=args, model=model, \r\n train_state=train_state)\r\n \r\n scheduler.step(train_state['val_loss'][-1])\r\n \r\n if train_state['stop_early']:\r\n break\r\n \r\n train_bar.n = 0\r\n val_bar.n = 0\r\n epoch_bar.set_postfix(best_val=train_state['early_stopping_best_val'] )\r\n epoch_bar.update()\r\n \r\n state_dict = torch.load(train_state['model_filename'])\r\n model.load_state_dict(state_dict)\r\n return model","def training(self) -> None:\n self.compile_model()\n self.train_epoch()\n self.agent.save()","def _train_model(self):\n raise NotImplementedError()","def self_training(args):\n\n print('load pre-trained model from [%s]' % args.load_model, file=sys.stderr)\n params = torch.load(args.load_model, map_location=lambda storage, loc: storage)\n vocab = params['vocab']\n transition_system = params['transition_system']\n saved_args = params['args']\n saved_state = params['state_dict']\n\n # transfer arguments\n saved_args.cuda = args.cuda\n saved_args.save_to = args.save_to\n saved_args.train_file = args.train_file\n saved_args.unlabeled_file = args.unlabeled_file\n saved_args.dev_file = args.dev_file\n saved_args.load_decode_results = args.load_decode_results\n args = saved_args\n\n update_args(args)\n\n model = Parser(saved_args, vocab, transition_system)\n model.load_state_dict(saved_state)\n\n if args.cuda: model = model.cuda()\n model.train()\n optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n\n print('load unlabeled data [%s]' % args.unlabeled_file, file=sys.stderr)\n unlabeled_data = Dataset.from_bin_file(args.unlabeled_file)\n\n print('load decoding results of unlabeled data [%s]' % args.load_decode_results, file=sys.stderr)\n decode_results = pickle.load(open(args.load_decode_results))\n\n labeled_data = Dataset.from_bin_file(args.train_file)\n dev_set = Dataset.from_bin_file(args.dev_file)\n\n print('Num. examples in unlabeled data: %d' % len(unlabeled_data), file=sys.stderr)\n assert len(unlabeled_data) == len(decode_results)\n self_train_examples = []\n for example, hyps in zip(unlabeled_data, decode_results):\n if hyps:\n hyp = hyps[0]\n sampled_example = Example(idx='self_train-%s' % example.idx,\n src_sent=example.src_sent,\n tgt_code=hyp.code,\n tgt_actions=hyp.action_infos,\n tgt_ast=hyp.tree)\n self_train_examples.append(sampled_example)\n print('Num. self training examples: %d, Num. labeled examples: %d' % (len(self_train_examples), len(labeled_data)),\n file=sys.stderr)\n\n train_set = Dataset(examples=labeled_data.examples + self_train_examples)\n\n print('begin training, %d training examples, %d dev examples' % (len(train_set), len(dev_set)), file=sys.stderr)\n print('vocab: %s' % repr(vocab), file=sys.stderr)\n\n epoch = train_iter = 0\n report_loss = report_examples = 0.\n history_dev_scores = []\n num_trial = patience = 0\n while True:\n epoch += 1\n epoch_begin = time.time()\n\n for batch_examples in train_set.batch_iter(batch_size=args.batch_size, shuffle=True):\n batch_examples = [e for e in batch_examples if len(e.tgt_actions) <= args.decode_max_time_step]\n\n train_iter += 1\n optimizer.zero_grad()\n\n loss = -model.score(batch_examples)\n # print(loss.data)\n loss_val = torch.sum(loss).data[0]\n report_loss += loss_val\n report_examples += len(batch_examples)\n loss = torch.mean(loss)\n\n loss.backward()\n\n # clip gradient\n if args.clip_grad > 0.:\n grad_norm = torch.nn.utils.clip_grad_norm(model.parameters(), args.clip_grad)\n\n optimizer.step()\n\n if train_iter % args.log_every == 0:\n print('[Iter %d] encoder loss=%.5f' %\n (train_iter,\n report_loss / report_examples),\n file=sys.stderr)\n\n report_loss = report_examples = 0.\n\n print('[Epoch %d] epoch elapsed %ds' % (epoch, time.time() - epoch_begin), file=sys.stderr)\n # model_file = args.save_to + '.iter%d.bin' % train_iter\n # print('save model to [%s]' % model_file, file=sys.stderr)\n # model.save(model_file)\n\n # perform validation\n print('[Epoch %d] begin validation' % epoch, file=sys.stderr)\n eval_start = time.time()\n eval_results = evaluation.evaluate(dev_set.examples, model, args, verbose=True)\n dev_acc = eval_results['accuracy']\n print('[Epoch %d] code generation accuracy=%.5f took %ds' % (epoch, dev_acc, time.time() - eval_start), file=sys.stderr)\n is_better = history_dev_scores == [] or dev_acc > max(history_dev_scores)\n history_dev_scores.append(dev_acc)\n\n if is_better:\n patience = 0\n model_file = args.save_to + '.bin'\n print('save currently the best model ..', file=sys.stderr)\n print('save model to [%s]' % model_file, file=sys.stderr)\n model.save(model_file)\n # also save the optimizers' state\n torch.save(optimizer.state_dict(), args.save_to + '.optim.bin')\n elif epoch == args.max_epoch:\n print('reached max epoch, stop!', file=sys.stderr)\n exit(0)\n elif patience < args.patience:\n patience += 1\n print('hit patience %d' % patience, file=sys.stderr)\n\n if patience == args.patience:\n num_trial += 1\n print('hit #%d trial' % num_trial, file=sys.stderr)\n if num_trial == args.max_num_trial:\n print('early stop!', file=sys.stderr)\n exit(0)\n\n # decay lr, and restore from previously best checkpoint\n lr = optimizer.param_groups[0]['lr'] * args.lr_decay\n print('load previously best model and decay learning rate to %f' % lr, file=sys.stderr)\n\n # load model\n params = torch.load(args.save_to + '.bin', map_location=lambda storage, loc: storage)\n model.load_state_dict(params['state_dict'])\n if args.cuda: model = model.cuda()\n\n # load optimizers\n if args.reset_optimizer:\n print('reset optimizer', file=sys.stderr)\n optimizer = torch.optim.Adam(model.inference_model.parameters(), lr=lr)\n else:\n print('restore parameters of the optimizers', file=sys.stderr)\n optimizer.load_state_dict(torch.load(args.save_to + '.optim.bin'))\n\n # set new lr\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n # reset patience\n patience = 0","def train(self, training_steps=10):","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(model: torch.nn.Module,\n trainingset_dataloader: torch.utils.data.DataLoader, trainingset_eval_dataloader: torch.utils.data.DataLoader,\n validationset_eval_dataloader: torch.utils.data.DataLoader,\n results_directory: str = \"results\", n_updates: int = int(1e5), show_progress: bool = True,\n load_file: str = None, device: torch.device = torch.device('cuda:0'), rnd_seed: int = 0,\n num_torch_threads: int = 3, learning_rate: float = 1e-4, l1_weight_decay: float = 0,\n l2_weight_decay: float = 0, log_training_stats_at: int = int(1e2), evaluate_at: int = int(5e3)):\n # Append current timestamp to results directory\n results_directory = os.path.join(results_directory, datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S'))\n os.makedirs(results_directory, exist_ok=True)\n \n # Read config file and set up results folder\n logfile = os.path.join(results_directory, 'log.txt')\n checkpointdir = os.path.join(results_directory, 'checkpoint')\n os.makedirs(checkpointdir, exist_ok=True)\n tensorboarddir = os.path.join(results_directory, 'tensorboard')\n os.makedirs(tensorboarddir, exist_ok=True)\n \n # Prepare tensorboard writer\n writer = SummaryWriter(log_dir=tensorboarddir)\n \n # Print all outputs to logfile and terminal\n tee_print = TeePrint(logfile)\n tprint = tee_print.tee_print\n \n # Set up PyTorch and numpy random seeds\n try:\n torch.set_num_threads(num_torch_threads)\n torch.manual_seed(rnd_seed)\n np.random.seed(rnd_seed)\n \n # Send model to device\n model.to(device)\n \n # Define loss function\n mean_cross_entropy = torch.nn.BCEWithLogitsLoss().to(device)\n \n # Get optimizer (eps needs to be at about 1e-4 to be numerically stable with 16 bit float)\n optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=l2_weight_decay, eps=1e-4)\n \n # Create a checkpoint dictionary with objects we want to have saved and loaded if needed\n state = dict(model=model, optimizer=optimizer, update=0, best_validation_loss=np.inf)\n \n # Setup the SaverLoader class to save/load our checkpoint dictionary to files or to RAM objects\n saver_loader = SaverLoader(save_dict=state, device=device, save_dir=checkpointdir,\n n_savefiles=1, # keep only the latest checkpoint\n n_inmem=1 # save checkpoint only in RAM\n )\n \n # Load previous checkpoint dictionary, if load_file is specified\n if load_file is not None:\n state.update(saver_loader.load_from_file(loadname=load_file, verbose=True))\n tprint(f\"Loaded checkpoint from file {load_file}\")\n update, best_validation_loss = state['update'], state['best_validation_loss']\n \n # Save checkpoint dictionary to RAM object\n saver_loader.save_to_ram(savename=str(update))\n \n #\n # Start training\n #\n try:\n tprint(\"Training model...\")\n update_progess_bar = tqdm(total=n_updates, disable=not show_progress, position=0,\n desc=f\"loss={np.nan:6.4f}\")\n while update < n_updates:\n for data in trainingset_dataloader:\n # Get samples as lists\n labels, inputs, sequence_lengths, counts_per_sequence, sample_ids = data\n \n # Apply attention-based sequence reduction and create minibatch\n with torch.no_grad():\n labels, inputs, sequence_lengths, n_sequences = model.reduce_and_stack_minibatch(\n labels, inputs, sequence_lengths, counts_per_sequence)\n \n # Reset gradients\n optimizer.zero_grad()\n \n # Calculate predictions from reduced sequences\n logit_outputs = model(inputs, n_sequences)\n \n # Calculate losses\n pred_loss = mean_cross_entropy(logit_outputs, labels[..., -1])\n l1reg_loss = (torch.mean(torch.stack([p.abs().float().mean() for p in model.parameters()])))\n loss = pred_loss + l1reg_loss * l1_weight_decay\n \n # Perform update\n loss.backward()\n optimizer.step()\n \n update += 1\n update_progess_bar.update()\n update_progess_bar.set_description(desc=f\"loss={loss.item():6.4f}\", refresh=True)\n \n # Add to tensorboard\n if update % log_training_stats_at == 0:\n tb_group = 'training/'\n writer.add_scalar(tag=tb_group+'pred_loss', scalar_value=pred_loss, global_step=update)\n writer.add_scalar(tag=tb_group+'l1reg_loss', scalar_value=l1reg_loss, global_step=update)\n writer.add_scalar(tag=tb_group+'loss', scalar_value=loss, global_step=update)\n writer.add_histogram(tag=tb_group+'logit_outputs', values=logit_outputs, global_step=update)\n \n # Calculate scores and loss on training set and validation set\n if update % evaluate_at == 0 or update == n_updates or update == 1:\n print(\" Calculating training score...\")\n roc_auc, bacc, f1, scoring_loss = evaluate(model=model, dataloader=trainingset_eval_dataloader)\n print(f\" ...done!\")\n tprint(f\"[training_inference] u: {update:07d}; roc_auc: {roc_auc:6.4f}; bacc: {bacc:6.4f}; \"\n f\"f1: {f1:6.4f}; scoring_loss: {scoring_loss:6.4f}\")\n \n tb_group = 'training_inference/'\n writer.add_scalar(tag=tb_group+'roc_auc', scalar_value=roc_auc, global_step=update)\n writer.add_scalar(tag=tb_group+'bacc', scalar_value=bacc, global_step=update)\n writer.add_scalar(tag=tb_group+'f1', scalar_value=f1, global_step=update)\n writer.add_scalar(tag=tb_group+'scoring_loss', scalar_value=scoring_loss, global_step=update)\n \n print(\" Calculating validation score...\")\n roc_auc, bacc, f1, scoring_loss = evaluate(model=model, dataloader=validationset_eval_dataloader)\n print(f\" ...done!\")\n tprint(f\"[validation] u: {update:07d}; roc_auc: {roc_auc:6.4f}; bacc: {bacc:6.4f}; \"\n f\"f1: {f1:6.4f}; scoring_loss: {scoring_loss:6.4f}\")\n \n tb_group = 'validation/'\n writer.add_scalar(tag=tb_group+'roc_auc', scalar_value=roc_auc, global_step=update)\n writer.add_scalar(tag=tb_group+'bacc', scalar_value=bacc, global_step=update)\n writer.add_scalar(tag=tb_group+'f1', scalar_value=f1, global_step=update)\n writer.add_scalar(tag=tb_group+'scoring_loss', scalar_value=scoring_loss, global_step=update)\n writer.add_histogram(tag=tb_group+'weights',\n values=model.sequence_embedding_16bit.conv_aas.weight.cpu().detach(),\n global_step=update)\n writer.add_histogram(tag=tb_group+'biases',\n values=model.sequence_embedding_16bit.conv_aas.bias.cpu().detach(),\n global_step=update)\n \n # If we have a new best loss on the validation set, we save the model as new best model\n if best_validation_loss > scoring_loss:\n best_validation_loss = scoring_loss\n tprint(f\" New best validation loss: {scoring_loss}\")\n # Save current state as RAM object\n state['update'] = update\n state['best_validation_loss'] = scoring_loss\n # Save checkpoint dictionary with currently best model to RAM\n saver_loader.save_to_ram(savename=str(update))\n # This would save to disk every time a new best model is found, which can be slow\n # saver_loader.save_to_file(filename=f'best_so_far_u{update}.tar.gzip')\n \n if update >= n_updates:\n break\n update_progess_bar.close()\n finally:\n # In any case, save the current model and best model to a file\n saver_loader.save_to_file(filename=f'lastsave_failed_u{update}.tar.gzip')\n state.update(saver_loader.load_from_ram()) # load best model so far\n saver_loader.save_to_file(filename=f'best_failed_u{update}.tar.gzip')\n print('Finished Training!')\n except Exception as e:\n with open(logfile, 'a') as lf:\n print(f\"Exception: {e}\", file=lf)\n raise e\n finally:\n close_all() # Clean up","def train(model, optimizer, epochs=1):\n model = model.to(device=device) # move the model parameters to CPU/GPU\n for e in range(epochs):\n for t, (x, y) in enumerate(dataloader_train):\n model.train() # put model to training mode\n\n x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\n y = y.to(device=device, dtype=torch.long)\n\n scores = model(x)\n \n loss = F.multi_margin_loss(scores, y) \n if t == 0:\n losses.append(loss.item())\n\n\n optimizer.zero_grad()\n loss.backward()\n\n optimizer.step()\n\n if t % print_every == 0:\n print('Iteration %d, loss = %.4f' % (t, loss.item()))\n check_accuracy(dataloader_val, model)\n print()\n # losses.append(loss.item())","def train_step(args, model: torch.nn.Module, batch: Tuple, meters: AverageMeterSet, epoch: int, batch_idx: int):\n labeled_batch, unlabeled_batch = batch\n labeled, targets = labeled_batch\n unlabeled_k, _ = unlabeled_batch\n\n # One hot labels\n targets = torch.zeros(args.batch_size, args.num_classes).scatter_(\n 1, targets.view(-1, 1), 1\n )\n\n unlabeled_k = [u_k.to(args.device) for u_k in unlabeled_k]\n labeled = labeled.to(args.device)\n targets = targets.to(args.device)\n\n # Disable batch-norm running_mean and running_var updates for pseduo-label forward passes\n set_bn_running_updates(model, enable=False)\n with torch.no_grad():\n preds = [\n torch.softmax(model(u_k.to(args.device)), dim=1) for u_k in unlabeled_k\n ]\n avg_preds = torch.stack(preds).mean(dim=0)\n sharpened_preds = torch.pow(avg_preds, 1 / args.temperature)\n unlabeled_targets = sharpened_preds / sharpened_preds.sum(dim=-1, keepdim=True)\n unlabeled_targets = unlabeled_targets.detach()\n\n all_inputs = torch.cat([labeled] + unlabeled_k, dim=0)\n all_targets = torch.cat(\n [targets] + [unlabeled_targets for _ in range(len(unlabeled_k))], dim=0\n )\n\n mixed_input, mixed_targets = mixup(all_inputs, all_targets, args.alpha)\n\n # Interleave labeled and unlabeled samples to avoid biased batch norm calculation\n mixed_input = list(torch.split(mixed_input, args.batch_size))\n mixed_input = interleave(mixed_input, args.batch_size)\n\n # Only update running batch-norm parameters for first batch of mixed batches\n set_bn_running_updates(model, enable=True)\n logits = [model(mixed_input[0])]\n set_bn_running_updates(model, enable=False)\n for input in mixed_input[1:]:\n logits.append(model(input))\n\n # Put interleaved samples back - reverses interleaving applied before\n logits = interleave(logits, args.batch_size)\n logits_x = logits[0]\n logits_u = torch.cat(logits[1:], dim=0)\n\n # Cross entropy loss for labeled samples\n labeled_loss = -torch.sum(\n F.log_softmax(logits_x, dim=1) * mixed_targets[: args.batch_size], dim=1\n )\n # L2-distance loss for unlabeled samples\n unlabeled_loss = torch.mean(\n (torch.softmax(logits_u, dim=1) - mixed_targets[args.batch_size :]) ** 2\n )\n\n # Update unlabeled loss weight based on current step (linear rampup to max. value over first 16 epochs)\n step = epoch * args.iters_per_epoch + (batch_idx + 1)\n wu = (\n args.wu * linear_rampup(step, 16 * args.iters_per_epoch)\n if not args.resume\n else args.wu\n )\n\n # Total loss\n loss = torch.mean(labeled_loss) + wu * unlabeled_loss\n\n meters.update(\"total_loss\", loss.item(), targets.size(0))\n meters.update(\"labeled_loss\", torch.mean(labeled_loss).item(), targets.size(0))\n meters.update(\"unlabeled_loss\", unlabeled_loss.item(), targets.size(0))\n meters.update(\"wu\", wu, 1)\n return loss","def train_model(\n face_model,\n frame_model,\n audio_model,\n combination_model,\n dataloader,\n optimizer,\n criterion,\n num_epochs,\n checkpoint_path,\n save_freq,\n eval_freq,\n device=torch.device(\"cpu\"),\n verbose=False,\n):\n assert isinstance(face_model, torch.nn.Module)\n assert isinstance(frame_model, torch.nn.Module)\n assert isinstance(audio_model, torch.nn.Module)\n assert isinstance(combination_model, torch.nn.Module)\n assert isinstance(num_epochs, int)\n assert isinstance(checkpoint_path, str)\n assert isinstance(save_freq, int)\n assert isinstance(eval_freq, int)\n assert isinstance(device, torch.device)\n assert isinstance(verbose, bool)\n\n criterion_type = \"sinkhorn\" if isinstance(criterion, type(SamplesLoss())) else \"bce\"\n\n Path(checkpoint_path).mkdir(parents=True, exist_ok=True)\n losses = list()\n meter = AverageMeter()\n\n if verbose is False:\n print(\"\".join(\"-\" * 80))\n print(\"Epoch 0\")\n print(\"\".join(\"-\" * 80))\n progress_bar = tqdm(total=len(dataloader))\n for epoch in range(num_epochs):\n face_model.train()\n frame_model.train()\n audio_model.train()\n combination_model.train()\n\n for batch_idx, batch in enumerate(dataloader):\n face_model.train()\n frame_model.train()\n audio_model.train()\n combination_model.train()\n\n frames, audio_stft, label = batch\n frames = frames[:, :75]\n frames = frames.float().to(device)\n audio_stft = audio_stft.view(audio_stft.shape[0], -1).float().to(device)\n label = label.to(device)\n n_frames = frames.shape[0]\n\n try:\n landmarks, face_embeddings = face_model(frames)\n\n extracted_lips = models.AugmentedLipNet._crop_lips_batch(\n frames, landmarks\n )\n extracted_lips = (\n extracted_lips.permute(0, -1, 1, 2, 3).float().to(device)\n )\n frame_embeddings = frame_model(extracted_lips)\n\n audio_embeddings = audio_model(audio_stft)\n\n concat_embeddings = torch.cat(\n (face_embeddings, frame_embeddings, audio_embeddings[:, None, :]),\n axis=1,\n )\n concat_embeddings = concat_embeddings[:, :, None, :].float().to(device)\n prediction = combination_model(concat_embeddings)\n\n del frames\n del audio_stft\n del extracted_lips\n del face_embeddings\n del frame_embeddings\n del audio_embeddings\n del concat_embeddings\n\n torch.cuda.empty_cache()\n\n optimizer.zero_grad()\n\n loss = criterion(prediction, label)\n loss.backward()\n\n optimizer.step()\n losses.append(loss.item())\n meter.update(loss.item(), n_frames)\n\n except (ValueError, TypeError):\n print(\"No landmarks detected\")\n pass\n finally:\n if verbose:\n pass\n else:\n progress_bar.update(1)\n torch.cuda.empty_cache()\n\n if (epoch + 1) % save_freq == 0:\n # Save the model.\n print(\"Saving model weights.\")\n description = \"Epoch: {}, Loss: {}\".format(epoch, meter.avg)\n filename = \"{}/checkpoint_{}_{}.pth\".format(\n checkpoint_path, epoch, criterion_type\n )\n save_checkpoints(\n face_model,\n frame_model,\n audio_model,\n combination_model,\n description,\n filename,\n )\n \"\"\" Need to write the evaluation script first.\n if (epoch + 1) % eval_freq == 0:\n # Evaluate the model.\n print(\"Evaluating the model\")\n description = \n \"\"\"\n\n if verbose is False:\n print(\"\".join(\"-\" * 80))\n print(\"Epoch {}\".format(epoch + 1))\n print(\"\".join(\"-\" * 80))\n progress_bar.refresh()\n progress_bar.reset()\n\n losses = np.array(losses)\n np.save(\"./losses\", losses)\n progress_bar.close()","def main(args):\n # Use CUDA\n use_cuda = args.use_cuda and torch.cuda.is_available()\n device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n # Fix random seed\n torch.manual_seed(args.seed)\n\n # Generate token-to-index and index-to-token mapping\n tok2id, id2tok = data_loader.build_or_load_vocab(\n args.train, overwrite=False)\n\n print(\"*\" * 5)\n print(args)\n\n # Create DataLoader() objects\n params = {\n \"batch_size\": args.batch_size,\n \"collate_fn\": data_loader.collate_fn,\n \"shuffle\": args.shuffle,\n \"num_workers\": args.num_workers,\n }\n # train_dataset = data_loader.SNLIDataSet(args.train, tok2id)\n # train_loader = torch.utils.data.DataLoader(train_dataset, **params)\n val_dataset = data_loader.SNLIDataSet(args.val, tok2id)\n val_loader = torch.utils.data.DataLoader(val_dataset, **params)\n\n # Initialize model\n if args.model == \"rnn\": # RNN model\n model = RNN(\n vocab_size=const.MAX_VOCAB_SIZE, # Vocabulary size\n emb_dim=const.EMB_DIM, # Embedding dimensions\n hidden_dim=args.hidden_dim, # Hidden dimensions\n dropout_prob=args.dropout_prob, # Dropout probability\n padding_idx=const.PAD_IDX, # Padding token index\n num_classes=const.NUM_CLASSES, # Number of class labels\n id2tok=id2tok, # Vocabulary\n ).to(device)\n # Load model weights from disk\n model.load_state_dict(torch.load(const.MODELS + \"rnn.pt\"))\n model.eval()\n elif args.model == \"cnn\": # CNN model\n model = CNN(\n vocab_size=const.MAX_VOCAB_SIZE, # Vocabulary size\n emb_dim=const.EMB_DIM, # Embedding dimensions\n hidden_dim=args.hidden_dim, # Hidden dimensions\n kernel_size=args.kernel_size, # Kernel size\n dropout_prob=args.dropout_prob, # Dropout probability\n padding_idx=const.PAD_IDX, # Padding token index\n num_classes=const.NUM_CLASSES, # Number of class labels\n id2tok=id2tok, # Vocabulary\n ).to(device)\n # Load model weights from disk\n model.load_state_dict(torch.load(const.MODELS + \"cnn.pt\"))\n model.eval()\n else:\n print(\"Invalid model specification, exiting\")\n exit()\n\n # Criterion\n criterion = torch.nn.CrossEntropyLoss()\n # Model parameters\n params = [p for p in model.parameters() if p.requires_grad]\n\n # Inspect correct/incorrect predictions\n if args.inspect:\n right, wrong = eval_model(val_loader, model, device, criterion,\n inspect=True)\n print(\"\\nValidation premises with correct predictions:\\n\")\n for i, item in enumerate(right):\n text = \" \".join([id2tok[idx] for idx in item if idx > 0])\n print(\"#{}\\n {}\".format(i + 1, text))\n print(\"\\nValidation premises with incorrect predictions:\\n\")\n for i, item in enumerate(wrong):\n text = \" \".join([id2tok[idx] for idx in item if idx > 0])\n print(\"#{}\\n {}\".format(i + 1, text))\n return\n\n # Validation\n val_acc, _ = eval_model(val_loader, model, device, criterion)\n print(\"\\n Validation accuracy: {}\".format(val_acc))\n\n print(\"*\" * 5 + \"\\n\")","def train(params):\n p, x = load_simulated_data()\n\n # calculate means\n p_mean = p.mean(axis=(0, 1))\n p_std = p.std(axis=(0, 1))\n\n x_mean = x.mean(axis=(0, 1))\n x_std = x.std(axis=(0, 1))\n\n # TODO - does this make sense?\n # delta = x[:,2::2] - x[:,:-2:2]\n # the number to look ahead\n delta = x[:, 1:] - x[:, :-1]\n delta_mean = delta.mean(axis=(0, 1))\n delta_std = delta.std(axis=(0, 1))\n\n # send to torch tensors\n p_mean, p_std = torch.Tensor(p_mean).to(device), torch.Tensor(p_std).to(device)\n x_mean, x_std = torch.Tensor(x_mean).to(device), torch.Tensor(x_std).to(device)\n delta_mean, delta_std = (\n torch.Tensor(delta_mean).to(device),\n torch.Tensor(delta_std).to(device),\n )\n\n # parameters\n buffer_size = int(params[\"buffer size\"])\n activation = params[\"activation\"]\n\n # train val split\n training_split = 0.5\n n = len(p)\n k = int(n * training_split)\n train_p, val_p = p[:k], p[k:]\n train_x, val_x = x[:k], x[k:]\n\n n_ahead = 1\n train_dataset = LookaheadDataset(states=train_x, actions=train_p, n_ahead=n_ahead)\n val_dataset = LookaheadDataset(states=val_x, actions=val_p, n_ahead=n_ahead)\n\n action_size = len(train_dataset[0][0][0])\n state_size = len(train_dataset[0][1])\n output_size = len(train_dataset[0][2][0])\n\n model_path = params.get(\"model path\", None)\n dropout = params[\"dropout\"]\n hidden_layers = int(params[\"hidden layers\"])\n hidden_size = int(params[\"hidden size\"])\n\n model = Network(\n action_size=action_size,\n state_size=state_size,\n output_size=output_size,\n hidden_layers=hidden_layers,\n hidden_size=hidden_size,\n dropout=dropout,\n activation=activation,\n action_mean=p_mean,\n action_std=p_std,\n state_mean=x_mean,\n state_std=x_std,\n output_mean=delta_mean,\n output_std=delta_std,\n )\n\n model.to(device)\n if params.get(\"load\", False):\n model.load_state_dict(torch.load(model_path))\n\n learning_rate = params[\"learning rate\"]\n batch_size = int(params[\"batch size\"])\n\n optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n\n train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)\n\n train_losses = []\n val_losses = []\n\n best_loss = np.inf\n print_info = params.get(\"print\", False)\n\n epochs = int(params[\"epochs\"])\n max_batches = np.inf\n if print_info:\n loop = tqdm(total=min(len(train_dataloader), max_batches) * epochs)\n\n def step(state, deltas):\n s = state + deltas\n return s\n\n for epoch in range(epochs):\n model.train()\n # new_n_ahead = min((epoch + 1) * 5, 100)\n new_n_ahead = 10\n if new_n_ahead != n_ahead:\n n_ahead = new_n_ahead\n if print_info:\n print(n_ahead)\n train_dataset = LookaheadDataset(\n states=train_x, actions=train_p, n_ahead=n_ahead\n )\n val_dataset = LookaheadDataset(states=val_x, actions=val_p, n_ahead=n_ahead)\n train_dataloader = DataLoader(\n train_dataset, batch_size=batch_size, shuffle=True\n )\n val_dataloader = DataLoader(\n val_dataset, batch_size=batch_size, shuffle=True\n )\n for b, (a, s, d) in enumerate(train_dataloader):\n s = s.float().to(device)\n a = a.float().to(device)\n d = d.float().to(device)\n\n d_est = torch.zeros(d.shape).to(device)\n\n for i in range(n_ahead):\n d_hat = model(a[:, i], s)\n if i == 0:\n # d_est[:,i] = d_est[:,i] + d_hat\n d_est[:, i] = d_hat\n else:\n d_est[:, i] = d_est[:, i - 1] + d_hat\n s = s + d_hat\n\n # normalize d\n d = (d - delta_mean) / delta_std\n d_est = (d_est - delta_mean) / delta_std\n\n loss = loss_function(d, d_est)\n if print_info:\n if not val_losses:\n loop.set_description(\"loss: {:.3f}\".format(loss.item()))\n else:\n loop.set_description(\n \"loss: {:.4f}, val loss: {:.4f}\".format(\n loss.item(), val_losses[-1]\n )\n )\n train_losses.append(loss.item())\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n if print_info:\n loop.update(1)\n if b > max_batches:\n break\n with torch.no_grad():\n model.eval()\n epoch_losses = []\n for b, (a, s, d) in enumerate(val_dataloader):\n s = s.float().to(device)\n a = a.float().to(device)\n d = d.float().to(device)\n\n d_est = torch.zeros(d.shape).to(device)\n\n for i in range(n_ahead):\n d_hat = model(a[:, i], s)\n if i == 0:\n # d_est[:,i] = d_est[:,i] + d_hat\n d_est[:, i] = d_hat\n else:\n d_est[:, i] = d_est[:, i - 1] + d_hat\n s = s + d_hat\n\n # normalize d\n d = (d - delta_mean) / delta_std\n d_est = (d_est - delta_mean) / delta_std\n\n loss = loss_function(d, d_est)\n\n epoch_losses.append(loss.item())\n if b > max_batches:\n break\n val_losses.append(np.mean(epoch_losses))\n\n if np.mean(epoch_losses) < best_loss:\n best_loss = np.mean(epoch_losses)\n if model_path:\n torch.save(model.state_dict(), model_path)\n if print_info:\n print(\"Best Val Loss: {:.4}\".format(best_loss))\n n_ahead = 100\n val_dataset = LookaheadDataset(states=val_x, actions=val_p, n_ahead=n_ahead)\n val_dataloader = DataLoader(val_dataset, batch_size=100, shuffle=True)\n\n # calculate HZ\n start = time()\n with torch.no_grad():\n model.eval()\n for b, (a, s, d) in enumerate(val_dataloader):\n s = s.float().to(device)\n a = a.float().to(device)\n d = d.float().to(device)\n\n d_est = torch.zeros(d.shape).to(device)\n\n for i in range(n_ahead):\n d_hat = model(a[:, i], s)\n if i == 0:\n # d_est[:,i] = d_est[:,i] + d_hat\n d_est[:, i] = d_hat\n else:\n d_est[:, i] = d_est[:, i - 1] + d_hat\n s = s + d_hat\n elapsed = time() - start\n speed = elapsed / len(val_dataloader)\n return val_losses[-1].item(), speed","def train(forward_fn, optimizer, scaler, batch, device, opt):\n # Zero gradients\n optimizer.zero_grad()\n\n # Data\n x = batch.to(device)\n nt, n = x.shape[0], x.shape[1]\n\n # Forward (inference)\n x_, y, z, _, q_y_0_params, q_z_params, p_z_params, res = forward_fn(x, nt, dt=1 / opt.n_euler_steps)\n\n # Loss\n # NLL\n nll = utils.neg_logprob(x_, x, scale=opt.obs_scale).sum()\n # y_0 KL\n q_y_0 = utils.make_normal_from_raw_params(q_y_0_params)\n kl_y_0 = distrib.kl_divergence(q_y_0, distrib.Normal(0, 1)).sum()\n # z KL\n q_z, p_z = utils.make_normal_from_raw_params(q_z_params), utils.make_normal_from_raw_params(p_z_params)\n kl_z = distrib.kl_divergence(q_z, p_z).sum()\n # ELBO\n loss = nll + opt.beta_y * kl_y_0 + opt.beta_z * kl_z\n # L2 regularization of residuals\n if opt.l2_res > 0:\n l2_res = torch.norm(res, p=2, dim=2).sum()\n loss += opt.l2_res * l2_res\n # Batch average\n loss /= n\n\n # Backward and weight update\n if opt.torch_amp:\n with torch_amp.autocast(enabled=False):\n scaler.scale(loss).backward()\n scaler.step(optimizer)\n scaler.update()\n else:\n if opt.apex_amp:\n with apex_amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n else:\n loss.backward()\n optimizer.step()\n\n # Logs\n with torch.no_grad():\n loss = loss.item()\n nll = nll.sum().item() / n\n kl_y_0 = kl_y_0.item() / n\n kl_z = kl_z.item() / n\n\n return loss, nll, kl_y_0, kl_z","def train(\n # fmt: off\n lang: (\"Model language\", \"positional\", None, str),\n output_path: (\"Output directory to store model in\", \"positional\", None, Path),\n train_path: (\"Location of JSON-formatted training data\", \"positional\", None, Path),\n dev_path: (\"Location of JSON-formatted development data\", \"positional\", None, Path),\n raw_text: (\"Path to jsonl file with unlabelled text documents.\", \"option\", \"rt\", Path) = None,\n base_model: (\"Name of model to update (optional)\", \"option\", \"b\", str) = None,\n pipeline: (\"Comma-separated names of pipeline components\", \"option\", \"p\", str) = \"tagger,parser,ner\",\n vectors: (\"Model to load vectors from\", \"option\", \"v\", str) = None,\n replace_components: (\"Replace components from base model\", \"flag\", \"R\", bool) = False,\n n_iter: (\"Number of iterations\", \"option\", \"n\", int) = 30,\n n_early_stopping: (\"Maximum number of training epochs without dev accuracy improvement\", \"option\", \"ne\", int) = None,\n n_examples: (\"Number of examples\", \"option\", \"ns\", int) = 0,\n use_gpu: (\"Use GPU\", \"option\", \"g\", int) = -1,\n version: (\"Model version\", \"option\", \"V\", str) = \"0.0.0\",\n meta_path: (\"Optional path to meta.json to use as base.\", \"option\", \"m\", Path) = None,\n init_tok2vec: (\"Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.\", \"option\", \"t2v\", Path) = None,\n parser_multitasks: (\"Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'\", \"option\", \"pt\", str) = \"\",\n entity_multitasks: (\"Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'\", \"option\", \"et\", str) = \"\",\n noise_level: (\"Amount of corruption for data augmentation\", \"option\", \"nl\", float) = 0.0,\n orth_variant_level: (\"Amount of orthography variation for data augmentation\", \"option\", \"ovl\", float) = 0.0,\n eval_beam_widths: (\"Beam widths to evaluate, e.g. 4,8\", \"option\", \"bw\", str) = \"\",\n gold_preproc: (\"Use gold preprocessing\", \"flag\", \"G\", bool) = False,\n learn_tokens: (\"Make parser learn gold-standard tokenization\", \"flag\", \"T\", bool) = False,\n textcat_multilabel: (\"Textcat classes aren't mutually exclusive (multilabel)\", \"flag\", \"TML\", bool) = False,\n textcat_arch: (\"Textcat model architecture\", \"option\", \"ta\", str) = \"bow\",\n textcat_positive_label: (\"Textcat positive label for binary classes with two labels\", \"option\", \"tpl\", str) = None,\n tag_map_path: (\"Location of JSON-formatted tag map\", \"option\", \"tm\", Path) = None,\n verbose: (\"Display more information for debug\", \"flag\", \"VV\", bool) = False,\n debug: (\"Run data diagnostics before training\", \"flag\", \"D\", bool) = False,\n # fmt: on\n):\n util.fix_random_seed()\n util.set_env_log(verbose)\n\n # Make sure all files and paths exists if they are needed\n train_path = util.ensure_path(train_path)\n dev_path = util.ensure_path(dev_path)\n meta_path = util.ensure_path(meta_path)\n output_path = util.ensure_path(output_path)\n if raw_text is not None:\n raw_text = list(srsly.read_jsonl(raw_text))\n if not train_path or not train_path.exists():\n msg.fail(\"Training data not found\", train_path, exits=1)\n if not dev_path or not dev_path.exists():\n msg.fail(\"Development data not found\", dev_path, exits=1)\n if meta_path is not None and not meta_path.exists():\n msg.fail(\"Can't find model meta.json\", meta_path, exits=1)\n meta = srsly.read_json(meta_path) if meta_path else {}\n if output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]:\n msg.warn(\n \"Output directory is not empty\",\n \"This can lead to unintended side effects when saving the model. \"\n \"Please use an empty directory or a different path instead. If \"\n \"the specified output path doesn't exist, the directory will be \"\n \"created for you.\",\n )\n if not output_path.exists():\n output_path.mkdir()\n msg.good(f\"Created output directory: {output_path}\")\n\n tag_map = {}\n if tag_map_path is not None:\n tag_map = srsly.read_json(tag_map_path)\n # Take dropout and batch size as generators of values -- dropout\n # starts high and decays sharply, to force the optimizer to explore.\n # Batch size starts at 1 and grows, so that we make updates quickly\n # at the beginning of training.\n dropout_rates = util.decaying(\n util.env_opt(\"dropout_from\", 0.2),\n util.env_opt(\"dropout_to\", 0.2),\n util.env_opt(\"dropout_decay\", 0.0),\n )\n batch_sizes = util.compounding(\n util.env_opt(\"batch_from\", 100.0),\n util.env_opt(\"batch_to\", 1000.0),\n util.env_opt(\"batch_compound\", 1.001),\n )\n\n if not eval_beam_widths:\n eval_beam_widths = [1]\n else:\n eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(\",\")]\n if 1 not in eval_beam_widths:\n eval_beam_widths.append(1)\n eval_beam_widths.sort()\n has_beam_widths = eval_beam_widths != [1]\n\n default_dir = Path(__file__).parent.parent / \"ml\" / \"models\" / \"defaults\"\n\n # Set up the base model and pipeline. If a base model is specified, load\n # the model and make sure the pipeline matches the pipeline setting. If\n # training starts from a blank model, intitalize the language class.\n pipeline = [p.strip() for p in pipeline.split(\",\")]\n msg.text(f\"Training pipeline: {pipeline}\")\n disabled_pipes = None\n pipes_added = False\n if use_gpu >= 0:\n activated_gpu = None\n try:\n activated_gpu = set_gpu(use_gpu)\n except Exception as e:\n msg.warn(f\"Exception: {e}\")\n if activated_gpu is not None:\n msg.text(f\"Using GPU: {use_gpu}\")\n else:\n msg.warn(f\"Unable to activate GPU: {use_gpu}\")\n msg.text(\"Using CPU only\")\n use_gpu = -1\n if base_model:\n msg.text(f\"Starting with base model '{base_model}'\")\n nlp = util.load_model(base_model)\n if nlp.lang != lang:\n msg.fail(\n f\"Model language ('{nlp.lang}') doesn't match language \"\n f\"specified as `lang` argument ('{lang}') \",\n exits=1,\n )\n if vectors:\n msg.text(f\"Loading vectors from model '{vectors}'\")\n _load_vectors(nlp, vectors)\n\n nlp.select_pipes(disable=[p for p in nlp.pipe_names if p not in pipeline])\n for pipe in pipeline:\n # first, create the model.\n # Bit of a hack after the refactor to get the vectors into a default config\n # use train-from-config instead :-)\n if pipe == \"parser\":\n config_loc = default_dir / \"parser_defaults.cfg\"\n elif pipe == \"tagger\":\n config_loc = default_dir / \"tagger_defaults.cfg\"\n elif pipe == \"ner\":\n config_loc = default_dir / \"ner_defaults.cfg\"\n elif pipe == \"textcat\":\n config_loc = default_dir / \"textcat_defaults.cfg\"\n elif pipe == \"senter\":\n config_loc = default_dir / \"senter_defaults.cfg\"\n else:\n raise ValueError(f\"Component {pipe} currently not supported.\")\n pipe_cfg = util.load_config(config_loc, create_objects=False)\n if vectors:\n pretrained_config = {\n \"@architectures\": \"spacy.VocabVectors.v1\",\n \"name\": vectors,\n }\n pipe_cfg[\"model\"][\"tok2vec\"][\"pretrained_vectors\"] = pretrained_config\n\n if pipe == \"parser\":\n pipe_cfg[\"learn_tokens\"] = learn_tokens\n elif pipe == \"textcat\":\n pipe_cfg[\"exclusive_classes\"] = not textcat_multilabel\n pipe_cfg[\"architecture\"] = textcat_arch\n pipe_cfg[\"positive_label\"] = textcat_positive_label\n\n if pipe not in nlp.pipe_names:\n msg.text(f\"Adding component to base model '{pipe}'\")\n nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))\n pipes_added = True\n elif replace_components:\n msg.text(f\"Replacing component from base model '{pipe}'\")\n nlp.replace_pipe(pipe, nlp.create_pipe(pipe, config=pipe_cfg))\n pipes_added = True\n else:\n if pipe == \"textcat\":\n textcat_cfg = nlp.get_pipe(\"textcat\").cfg\n base_cfg = {\n \"exclusive_classes\": textcat_cfg[\"exclusive_classes\"],\n \"architecture\": textcat_cfg[\"architecture\"],\n \"positive_label\": textcat_cfg[\"positive_label\"],\n }\n if base_cfg != pipe_cfg:\n msg.fail(\n f\"The base textcat model configuration does\"\n f\"not match the provided training options. \"\n f\"Existing cfg: {base_cfg}, provided cfg: {pipe_cfg}\",\n exits=1,\n )\n msg.text(f\"Extending component from base model '{pipe}'\")\n disabled_pipes = nlp.select_pipes(\n disable=[p for p in nlp.pipe_names if p not in pipeline]\n )\n else:\n msg.text(f\"Starting with blank model '{lang}'\")\n lang_cls = util.get_lang_class(lang)\n nlp = lang_cls()\n\n if vectors:\n msg.text(f\"Loading vectors from model '{vectors}'\")\n _load_vectors(nlp, vectors)\n\n for pipe in pipeline:\n # first, create the model.\n # Bit of a hack after the refactor to get the vectors into a default config\n # use train-from-config instead :-)\n if pipe == \"parser\":\n config_loc = default_dir / \"parser_defaults.cfg\"\n elif pipe == \"tagger\":\n config_loc = default_dir / \"tagger_defaults.cfg\"\n elif pipe == \"morphologizer\":\n config_loc = default_dir / \"morphologizer_defaults.cfg\"\n elif pipe == \"ner\":\n config_loc = default_dir / \"ner_defaults.cfg\"\n elif pipe == \"textcat\":\n config_loc = default_dir / \"textcat_defaults.cfg\"\n elif pipe == \"senter\":\n config_loc = default_dir / \"senter_defaults.cfg\"\n else:\n raise ValueError(f\"Component {pipe} currently not supported.\")\n pipe_cfg = util.load_config(config_loc, create_objects=False)\n if vectors:\n pretrained_config = {\n \"@architectures\": \"spacy.VocabVectors.v1\",\n \"name\": vectors,\n }\n pipe_cfg[\"model\"][\"tok2vec\"][\"pretrained_vectors\"] = pretrained_config\n\n if pipe == \"parser\":\n pipe_cfg[\"learn_tokens\"] = learn_tokens\n elif pipe == \"textcat\":\n pipe_cfg[\"exclusive_classes\"] = not textcat_multilabel\n pipe_cfg[\"architecture\"] = textcat_arch\n pipe_cfg[\"positive_label\"] = textcat_positive_label\n\n pipe = nlp.create_pipe(pipe, config=pipe_cfg)\n nlp.add_pipe(pipe)\n\n # Update tag map with provided mapping\n nlp.vocab.morphology.tag_map.update(tag_map)\n\n # Multitask objectives\n multitask_options = [(\"parser\", parser_multitasks), (\"ner\", entity_multitasks)]\n for pipe_name, multitasks in multitask_options:\n if multitasks:\n if pipe_name not in pipeline:\n msg.fail(\n f\"Can't use multitask objective without '{pipe_name}' in \"\n f\"the pipeline\"\n )\n pipe = nlp.get_pipe(pipe_name)\n for objective in multitasks.split(\",\"):\n pipe.add_multitask_objective(objective)\n\n # Prepare training corpus\n msg.text(f\"Counting training words (limit={n_examples})\")\n corpus = GoldCorpus(train_path, dev_path, limit=n_examples)\n n_train_words = corpus.count_train()\n\n if base_model and not pipes_added:\n # Start with an existing model, use default optimizer\n optimizer = create_default_optimizer()\n else:\n # Start with a blank model, call begin_training\n cfg = {\"device\": use_gpu}\n optimizer = nlp.begin_training(lambda: corpus.train_examples, **cfg)\n nlp._optimizer = None\n\n # Load in pretrained weights (TODO: this may be broken in the config rewrite)\n if init_tok2vec is not None:\n components = _load_pretrained_tok2vec(nlp, init_tok2vec)\n msg.text(f\"Loaded pretrained tok2vec for: {components}\")\n\n # Verify textcat config\n if \"textcat\" in pipeline:\n textcat_labels = nlp.get_pipe(\"textcat\").cfg.get(\"labels\", [])\n if textcat_positive_label and textcat_positive_label not in textcat_labels:\n msg.fail(\n f\"The textcat_positive_label (tpl) '{textcat_positive_label}' \"\n f\"does not match any label in the training data.\",\n exits=1,\n )\n if textcat_positive_label and len(textcat_labels) != 2:\n msg.fail(\n \"A textcat_positive_label (tpl) '{textcat_positive_label}' was \"\n \"provided for training data that does not appear to be a \"\n \"binary classification problem with two labels.\",\n exits=1,\n )\n train_data = corpus.train_data(\n nlp,\n noise_level=noise_level,\n gold_preproc=gold_preproc,\n max_length=0,\n ignore_misaligned=True,\n )\n train_labels = set()\n if textcat_multilabel:\n multilabel_found = False\n for ex in train_data:\n train_labels.update(ex.gold.cats.keys())\n if list(ex.gold.cats.values()).count(1.0) != 1:\n multilabel_found = True\n if not multilabel_found and not base_model:\n msg.warn(\n \"The textcat training instances look like they have \"\n \"mutually-exclusive classes. Remove the flag \"\n \"'--textcat-multilabel' to train a classifier with \"\n \"mutually-exclusive classes.\"\n )\n if not textcat_multilabel:\n for ex in train_data:\n train_labels.update(ex.gold.cats.keys())\n if list(ex.gold.cats.values()).count(1.0) != 1 and not base_model:\n msg.warn(\n \"Some textcat training instances do not have exactly \"\n \"one positive label. Modifying training options to \"\n \"include the flag '--textcat-multilabel' for classes \"\n \"that are not mutually exclusive.\"\n )\n nlp.get_pipe(\"textcat\").cfg[\"exclusive_classes\"] = False\n textcat_multilabel = True\n break\n if base_model and set(textcat_labels) != train_labels:\n msg.fail(\n f\"Cannot extend textcat model using data with different \"\n f\"labels. Base model labels: {textcat_labels}, training data \"\n f\"labels: {list(train_labels)}\",\n exits=1,\n )\n if textcat_multilabel:\n msg.text(\n f\"Textcat evaluation score: ROC AUC score macro-averaged across \"\n f\"the labels '{', '.join(textcat_labels)}'\"\n )\n elif textcat_positive_label and len(textcat_labels) == 2:\n msg.text(\n f\"Textcat evaluation score: F1-score for the \"\n f\"label '{textcat_positive_label}'\"\n )\n elif len(textcat_labels) > 1:\n if len(textcat_labels) == 2:\n msg.warn(\n \"If the textcat component is a binary classifier with \"\n \"exclusive classes, provide '--textcat_positive_label' for \"\n \"an evaluation on the positive class.\"\n )\n msg.text(\n f\"Textcat evaluation score: F1-score macro-averaged across \"\n f\"the labels '{', '.join(textcat_labels)}'\"\n )\n else:\n msg.fail(\n \"Unsupported textcat configuration. Use `spacy debug-data` \"\n \"for more information.\"\n )\n\n # fmt: off\n row_head, output_stats = _configure_training_output(pipeline, use_gpu, has_beam_widths)\n row_widths = [len(w) for w in row_head]\n row_settings = {\"widths\": row_widths, \"aligns\": tuple([\"r\" for i in row_head]), \"spacing\": 2}\n # fmt: on\n print(\"\")\n msg.row(row_head, **row_settings)\n msg.row([\"-\" * width for width in row_settings[\"widths\"]], **row_settings)\n try:\n iter_since_best = 0\n best_score = 0.0\n for i in range(n_iter):\n train_data = corpus.train_dataset(\n nlp,\n noise_level=noise_level,\n orth_variant_level=orth_variant_level,\n gold_preproc=gold_preproc,\n max_length=0,\n ignore_misaligned=True,\n )\n if raw_text:\n random.shuffle(raw_text)\n raw_batches = util.minibatch(\n (nlp.make_doc(rt[\"text\"]) for rt in raw_text), size=8\n )\n words_seen = 0\n with tqdm.tqdm(total=n_train_words, leave=False) as pbar:\n losses = {}\n for batch in util.minibatch_by_words(train_data, size=batch_sizes):\n if not batch:\n continue\n try:\n nlp.update(\n batch,\n sgd=optimizer,\n drop=next(dropout_rates),\n losses=losses,\n )\n except ValueError as e:\n err = \"Error during training\"\n if init_tok2vec:\n err += \" Did you provide the same parameters during 'train' as during 'pretrain'?\"\n msg.fail(err, f\"Original error message: {e}\", exits=1)\n if raw_text:\n # If raw text is available, perform 'rehearsal' updates,\n # which use unlabelled data to reduce overfitting.\n raw_batch = list(next(raw_batches))\n nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)\n docs = [ex.doc for ex in batch]\n if not int(os.environ.get(\"LOG_FRIENDLY\", 0)):\n pbar.update(sum(len(doc) for doc in docs))\n words_seen += sum(len(doc) for doc in docs)\n with nlp.use_params(optimizer.averages):\n util.set_env_log(False)\n epoch_model_path = output_path / f\"model{i}\"\n nlp.to_disk(epoch_model_path)\n nlp_loaded = util.load_model_from_path(epoch_model_path)\n for beam_width in eval_beam_widths:\n for name, component in nlp_loaded.pipeline:\n if hasattr(component, \"cfg\"):\n component.cfg[\"beam_width\"] = beam_width\n dev_dataset = list(\n corpus.dev_dataset(\n nlp_loaded,\n gold_preproc=gold_preproc,\n ignore_misaligned=True,\n )\n )\n nwords = sum(len(ex.doc) for ex in dev_dataset)\n start_time = timer()\n scorer = nlp_loaded.evaluate(dev_dataset, verbose=verbose)\n end_time = timer()\n if use_gpu < 0:\n gpu_wps = None\n cpu_wps = nwords / (end_time - start_time)\n else:\n gpu_wps = nwords / (end_time - start_time)\n with use_ops(\"numpy\"):\n nlp_loaded = util.load_model_from_path(epoch_model_path)\n for name, component in nlp_loaded.pipeline:\n if hasattr(component, \"cfg\"):\n component.cfg[\"beam_width\"] = beam_width\n dev_dataset = list(\n corpus.dev_dataset(\n nlp_loaded,\n gold_preproc=gold_preproc,\n ignore_misaligned=True,\n )\n )\n start_time = timer()\n scorer = nlp_loaded.evaluate(dev_dataset, verbose=verbose)\n end_time = timer()\n cpu_wps = nwords / (end_time - start_time)\n acc_loc = output_path / f\"model{i}\" / \"accuracy.json\"\n srsly.write_json(acc_loc, scorer.scores)\n\n # Update model meta.json\n meta[\"lang\"] = nlp.lang\n meta[\"pipeline\"] = nlp.pipe_names\n meta[\"spacy_version\"] = f\">={about.__version__}\"\n if beam_width == 1:\n meta[\"speed\"] = {\n \"nwords\": nwords,\n \"cpu\": cpu_wps,\n \"gpu\": gpu_wps,\n }\n meta.setdefault(\"accuracy\", {})\n for component in nlp.pipe_names:\n for metric in _get_metrics(component):\n meta[\"accuracy\"][metric] = scorer.scores[metric]\n else:\n meta.setdefault(\"beam_accuracy\", {})\n meta.setdefault(\"beam_speed\", {})\n for component in nlp.pipe_names:\n for metric in _get_metrics(component):\n meta[\"beam_accuracy\"][metric] = scorer.scores[metric]\n meta[\"beam_speed\"][beam_width] = {\n \"nwords\": nwords,\n \"cpu\": cpu_wps,\n \"gpu\": gpu_wps,\n }\n meta[\"vectors\"] = {\n \"width\": nlp.vocab.vectors_length,\n \"vectors\": len(nlp.vocab.vectors),\n \"keys\": nlp.vocab.vectors.n_keys,\n \"name\": nlp.vocab.vectors.name,\n }\n meta.setdefault(\"name\", f\"model{i}\")\n meta.setdefault(\"version\", version)\n meta[\"labels\"] = nlp.meta[\"labels\"]\n meta_loc = output_path / f\"model{i}\" / \"meta.json\"\n srsly.write_json(meta_loc, meta)\n util.set_env_log(verbose)\n\n progress = _get_progress(\n i,\n losses,\n scorer.scores,\n output_stats,\n beam_width=beam_width if has_beam_widths else None,\n cpu_wps=cpu_wps,\n gpu_wps=gpu_wps,\n )\n if i == 0 and \"textcat\" in pipeline:\n textcats_per_cat = scorer.scores.get(\"textcats_per_cat\", {})\n for cat, cat_score in textcats_per_cat.items():\n if cat_score.get(\"roc_auc_score\", 0) < 0:\n msg.warn(\n f\"Textcat ROC AUC score is undefined due to \"\n f\"only one value in label '{cat}'.\"\n )\n msg.row(progress, **row_settings)\n # Early stopping\n if n_early_stopping is not None:\n current_score = _score_for_model(meta)\n if current_score < best_score:\n iter_since_best += 1\n else:\n iter_since_best = 0\n best_score = current_score\n if iter_since_best >= n_early_stopping:\n msg.text(\n f\"Early stopping, best iteration is: {i - iter_since_best}\"\n )\n msg.text(\n f\"Best score = {best_score}; Final iteration score = {current_score}\"\n )\n break\n except Exception as e:\n msg.warn(f\"Aborting and saving final best model. Encountered exception: {e}\")\n finally:\n best_pipes = nlp.pipe_names\n if disabled_pipes:\n disabled_pipes.restore()\n with nlp.use_params(optimizer.averages):\n final_model_path = output_path / \"model-final\"\n nlp.to_disk(final_model_path)\n meta_loc = output_path / \"model-final\" / \"meta.json\"\n final_meta = srsly.read_json(meta_loc)\n final_meta.setdefault(\"accuracy\", {})\n final_meta[\"accuracy\"].update(meta.get(\"accuracy\", {}))\n final_meta.setdefault(\"speed\", {})\n final_meta[\"speed\"].setdefault(\"cpu\", None)\n final_meta[\"speed\"].setdefault(\"gpu\", None)\n meta.setdefault(\"speed\", {})\n meta[\"speed\"].setdefault(\"cpu\", None)\n meta[\"speed\"].setdefault(\"gpu\", None)\n # combine cpu and gpu speeds with the base model speeds\n if final_meta[\"speed\"][\"cpu\"] and meta[\"speed\"][\"cpu\"]:\n speed = _get_total_speed(\n [final_meta[\"speed\"][\"cpu\"], meta[\"speed\"][\"cpu\"]]\n )\n final_meta[\"speed\"][\"cpu\"] = speed\n if final_meta[\"speed\"][\"gpu\"] and meta[\"speed\"][\"gpu\"]:\n speed = _get_total_speed(\n [final_meta[\"speed\"][\"gpu\"], meta[\"speed\"][\"gpu\"]]\n )\n final_meta[\"speed\"][\"gpu\"] = speed\n # if there were no speeds to update, overwrite with meta\n if (\n final_meta[\"speed\"][\"cpu\"] is None\n and final_meta[\"speed\"][\"gpu\"] is None\n ):\n final_meta[\"speed\"].update(meta[\"speed\"])\n # note: beam speeds are not combined with the base model\n if has_beam_widths:\n final_meta.setdefault(\"beam_accuracy\", {})\n final_meta[\"beam_accuracy\"].update(meta.get(\"beam_accuracy\", {}))\n final_meta.setdefault(\"beam_speed\", {})\n final_meta[\"beam_speed\"].update(meta.get(\"beam_speed\", {}))\n srsly.write_json(meta_loc, final_meta)\n msg.good(\"Saved model to output directory\", final_model_path)\n with msg.loading(\"Creating best model...\"):\n best_model_path = _collate_best_model(final_meta, output_path, best_pipes)\n msg.good(\"Created best model\", best_model_path)","def train(model, ids, data, scaler):\n if model.model_type == 'torch':\n train_ids, val_ids, _, _ = train_test_split(ids, ids, test_size=0.1, random_state=1)\n train_loader = double_loader(data, train_ids, batch_size=model.batch_size)\n val_loader = double_loader(data, val_ids, batch_size=len(val_ids))\n \n regressor = copy.deepcopy(model.model) \n optimiser = model.optimiser(regressor.parameters(), lr=model.lr)\n loss_function = torch.nn.MSELoss()\n name = model.name.replace(' ','_')\n early_stopping = EarlyStopping(name,regressor)\n \n for epoch in range(model.num_epochs):\n #train\n for (sol,solv,targets) in train_loader:\n if model.data_type == 'sentences':\n sol, solv = sol.to(device), solv.to(device)\n targets = targets.view(-1,1)\n targets = scaler.transform(targets)\n optimiser.zero_grad()\n outputs = regressor(sol,solv).to(device)\n cuda_targets = targets.to(device)\n loss = loss_function(outputs, cuda_targets)\n loss.backward()\n optimiser.step()\n #evaluate\n for (sol,solv,targets) in val_loader:\n if model.data_type == 'sentences':\n sol, solv = sol.to(device), solv.to(device)\n targets = targets.view(-1,1)\n targets = scaler.transform(targets)\n outputs = regressor(sol,solv).to(device)\n cuda_targets = targets.to(device)\n loss = loss_function(outputs, cuda_targets)\n val_loss = loss.item()\n #early stopping\n early_stopping.store(val_loss, regressor)\n if early_stopping.stop:\n #print(\"Stopping at epoch \"+str(epoch))\n break\n regressor.load_state_dict(torch.load('checkpoints/'+name+'.pt'))\n else:\n regressor = sklearn.base.clone(model.model)\n targets = scaler.transform(data[1][ids])\n regressor.fit(data[0][ids], targets)\n return regressor","def train(args, model, device, train_loader, optimizer, epoch, writer):\n model.train()\n running_loss = 0\n for batch_idx, (data, target) in enumerate(train_loader):\n data, target = data.to(device), target.to(device)\n optimizer.zero_grad()\n output = model(data)\n loss = F.nll_loss(output, target)\n running_loss += loss\n loss.backward()\n optimizer.step()\n if batch_idx % args.log_interval == 0:\n writer.add_scalar('training loss', loss / args.log_interval, epoch * len(train_loader) + batch_idx)\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n epoch, batch_idx * len(data), len(train_loader.dataset),\n 100. * batch_idx / len(train_loader), loss.item()))\n running_loss = 0","def train_model(args: argparse.Namespace, hp: HParams, extension_architecture: str, timestamp: str,\n logger: logging.Logger) -> None:\n # 1. Check if we have to train a single tier or a complete model (with several tiers)\n if args.tier is not None:\n # 1.1 Argument tier was defined. Only that tier will be trained.\n logging.info(f\"Training single tier of the model: Tier {args.tier}\")\n\n # 2. Setup tensorboard logging\n # 2.1 Create tensorboard logs directory (tensorboard requires a different folder for each\n # run of the model, in this case every run to train a tier) so we add the extension of the\n # network's architecture of this run and the timestamp to identify it completely\n tensorboard_dir = f\"{hp.logging.dir_log_tensorboard}{extension_architecture}_\" \\\n f\"{timestamp}_tier{args.tier}\"\n Path(tensorboard_dir).mkdir(parents=True, exist_ok=True)\n # 2.2 Create tensorboard writer\n tensorboardwriter = TensorboardWriter(hp, tensorboard_dir)\n\n # 3. Start training of the tier\n train_tier(args, hp, args.tier, extension_architecture, timestamp, tensorboardwriter,\n logger)\n\n tensorboardwriter.close()\n\n else:\n # 1.2 Argument tier was not defined. Train all tiers of the model.\n logging.info(\"Training all tiers of the model\")\n\n for tier in range(1, hp.network.n_tiers + 1):\n # 2. Setup tensorboard logging (one for every tier)\n # 2.1 Create tensorboard logs directory (tensorboard requires a different folder for\n # each run of the model, in this case every run to train a tier) so we add the extension\n # of the network's architecture of this run and the timestamp to identify it completely\n tensorboard_dir = hp.logging.dir_log_tensorboard + extension_architecture \\\n + f\"_{timestamp}_tier{tier}\"\n Path(tensorboard_dir).mkdir(parents=True, exist_ok=True)\n # 2.2 Create tensorboard writer\n tensorboardwriter = TensorboardWriter(hp, tensorboard_dir)\n\n # 3. Start training of the tier\n train_tier(args, hp, tier, extension_architecture, timestamp, tensorboardwriter, logger)\n\n tensorboardwriter.close()\n del tensorboardwriter","def train_model(model, train_dataloader, epoch, optimizer):\r\n model.train() # Set model to train mode\r\n total_epoch_loss = 0\r\n steps = 0\r\n\r\n for idx, (input_ids, input_mask, segment_ids, label_ids) in enumerate(train_dataloader):\r\n input_ids, input_mask, segment_ids, label_ids = input_ids.to(device), input_mask.to(device), segment_ids.to(device), label_ids.to(device)\r\n loss = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)\r\n loss.backward()\r\n total_epoch_loss += loss.item()\r\n\r\n torch.nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=1)\r\n optimizer.step()\r\n model.zero_grad()\r\n\r\n steps += 1\r\n\r\n if steps % 100 == 0:\r\n print(f'Epoch: {epoch + 1}, Iteration: {(idx + 1)}/{len(train_dataloader)}, Training Loss: {total_epoch_loss/steps:.4f}')\r\n\r\n return total_epoch_loss/len(train_dataloader)","def train(self, X_train, y_train):\n\n self.model_pipeline.fit(X_train, y_train)","def train(config: Config, device: torch.device, resume: Optional[ResumeInfo]) -> None:\n # Load datasets\n print(colored(\"loading training datasets:\", attrs=[\"bold\"]))\n dataset_factory = DatasetFactory()\n datasets, preprocessors = dataset_factory.create(config)\n print(f\"train: {len(datasets.train)}\")\n print(f\"val: {len(datasets.val)}\")\n print(f\"test: {len(datasets.test)}\")\n\n # Create model runner\n print(colored(\"model:\", attrs=[\"bold\"]))\n runner_factory = RunnerFactory()\n runner = runner_factory.create(config, device, preprocessors, datasets, resume)\n print(f\"{runner.model=}\")\n print(f\"{runner.criterion=}\")\n print(f\"{runner.optimiser=}\")\n\n print(colored(\"training:\", attrs=[\"bold\"]))\n runner.train()","def train_model(dataset):\n\n # clear the session so that we can train more than one model\n K.clear_session()\n\n # initialize the model\n model = initalizer.init_nn()\n\n # fit the model\n model.fit(dataset, epochs=40)\n\n return model","def train(self, x, t):\n self.model.fit(x, t)","def train(self, x, t):\n self.model.fit(x, t)","def train(self, x, t):\n self.model.fit(x, t)","def train(self, x, t):\n self.model.fit(x, t)","def train(self, x, t):\n self.model.fit(x, t)","def train(self, **kwargs):\n self.solver.train(**kwargs)","def train(self):\n model = TreeTrainer.train(self)\n model['latent'] = self._latent\n model['suffstats'] = {\n 'vert_ss': self._vert_ss,\n 'edge_ss': self._edge_ss,\n 'feat_ss': self._feat_ss,\n }\n return model","def train(epoch):\n model.train()\n acc_loss = 0\n acc_nll = 0\n acc_kl_theta_loss = 0\n acc_kl_eta_loss = 0\n acc_kl_alpha_loss = 0\n acc_pred_loss = 0 # classification loss\n cnt = 0\n\n indices = torch.randperm(args.num_docs_train)\n indices = torch.split(indices, args.batch_size)\n \n for idx, ind in enumerate(indices):\n\n optimizer.zero_grad()\n model.zero_grad() \n \n data_batch, times_batch, sources_batch, labels_batch = data.get_batch(\n train_tokens, train_counts, ind, train_sources, train_labels, \n args.vocab_size, args.emb_size, temporal=True, times=train_times) \n\n sums = data_batch.sum(1).unsqueeze(1)\n\n if args.bow_norm:\n normalized_data_batch = data_batch / sums\n else:\n normalized_data_batch = data_batch\n\n # print(\"forward passing ...\")\n\n loss, nll, kl_alpha, kl_eta, kl_theta, pred_loss = model(data_batch, normalized_data_batch, \n times_batch, sources_batch, labels_batch, train_rnn_inp, args.num_docs_train)\n\n # set_trace()\n\n # print(\"forward done.\")\n # print(\"backward passing ...\")\n\n # set_trace()\n\n loss.backward()\n\n # print(\"backward done.\")\n\n if args.clip > 0:\n torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)\n optimizer.step()\n\n # set_trace()\n\n acc_loss += torch.sum(loss).item()\n acc_nll += torch.sum(nll).item()\n acc_kl_theta_loss += torch.sum(kl_theta).item()\n acc_kl_eta_loss += torch.sum(kl_eta).item()\n acc_kl_alpha_loss += torch.sum(kl_alpha).item()\n\n acc_pred_loss += torch.sum(pred_loss).item()\n\n cnt += 1\n\n cur_loss = round(acc_loss / cnt, 2) \n cur_nll = round(acc_nll / cnt, 2) \n cur_kl_theta = round(acc_kl_theta_loss / cnt, 2) \n cur_kl_eta = round(acc_kl_eta_loss / cnt, 2) \n cur_kl_alpha = round(acc_kl_alpha_loss / cnt, 2)\n\n cur_pred_loss = round(acc_pred_loss / cnt, 2) \n\n if idx % args.log_interval == 0 and idx > 0:\n\n lr = optimizer.param_groups[0]['lr']\n print('Epoch: {} .. batch: {}/{} .. LR: {} .. KL_theta: {} .. KL_eta: {} .. KL_alpha: {} .. Rec_loss: {} .. Pred_loss: {} .. NELBO: {}'.format(\n epoch, idx, len(indices), lr, cur_kl_theta, cur_kl_eta, cur_kl_alpha, cur_nll, cur_pred_loss, cur_loss))\n \n\n lr = optimizer.param_groups[0]['lr']\n print('*'*100)\n print('Epoch----->{} .. LR: {} .. KL_theta: {} .. KL_eta: {} .. KL_alpha: {} .. Rec_loss: {} .. Pred_loss: {} .. NELBO: {}'.format(\n epoch, lr, cur_kl_theta, cur_kl_eta, cur_kl_alpha, cur_nll, cur_pred_loss, cur_loss))\n print('*'*100)","def training(model, trainloader, in_args_epochs, in_args_gpu, \n in_args_learning_rate):\n if in_args_gpu == True:\n model.to('cuda')\n epochs = in_args_epochs\n criterion = nn.NLLLoss()\n optimizer = optim.Adam(model.classifier.parameters(), \n lr=in_args_learning_rate)\n print_every = 100\n steps = 0\n for e in range(epochs):\n running_loss = 0\n for ii, (inputs, labels) in enumerate(trainloader):\n steps += 1\n if in_args_gpu == True:\n inputs, labels = inputs.to('cuda'), labels.to('cuda')\n optimizer.zero_grad()\n # Forward and backward passes\n outputs = model.forward(inputs)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n running_loss += loss.item()\n if steps % print_every == 0:\n print(\"Epoch: {}/{}... \".format(e+1, epochs),\n \"Training Loss: {:.4f}\".format(running_loss/print_every))\n running_loss = 0","def train_model(\n fname: Path,\n save_name: Path,\n batch_size: int = 32,\n warmup_steps: int = 100,\n steps: int = 1000,\n num_heads: int = 2,\n model_dim: int = 128,\n key_dim: int = 128,\n value_dim: int = 128,\n dropout: float = 0.1,\n num_mask: int = 9,\n):\n seqs = read_fasta(fname)\n\n X = seqs_to_integer(seqs)\n\n X = torch.from_numpy(X).type(torch.LongTensor)\n\n X_train, X_test = random_split(X)\n\n train_params = {\n \"batch_size\": batch_size,\n \"lr\": 0.0005,\n \"weight_decay\": 0.0,\n \"warmup_steps\": warmup_steps,\n \"steps\": steps,\n }\n model_params[\"n_head\"] = num_heads\n model_params[\"d_model\"] = model_dim\n model_params[\"d_k\"] = key_dim\n model_params[\"d_v\"] = value_dim\n model_params[\"dropout\"] = dropout\n model_params[\"num_mask\"] = num_mask\n\n model = BERT(**model_params)\n\n optimizer = Adam(\n model.parameters(),\n lr=train_params[\"lr\"],\n weight_decay=train_params[\"weight_decay\"],\n )\n\n scheduler = WarmupAnnealLR(optimizer, warmup_steps=train_params[\"warmup_steps\"])\n\n train(\n model,\n X_train,\n X_test,\n save_name,\n batch_size=train_params[\"batch_size\"],\n optimizer=optimizer,\n scheduler=scheduler,\n steps=train_params[\"steps\"],\n pbar_increment=10,\n )","def train(self):\r\n self.speaker2index_and_index2speaker()\r\n \"\"\"Initialize history matrix\"\"\"\r\n self.history = np.random.normal(loc=0, scale=0.1, size=(len(self.s2i), config.train.class_history))\r\n \"\"\"\"\"\"\r\n \"\"\"\"\"\"\r\n iterations = 0\r\n \"\"\"Get train/test\"\"\"\r\n if WORDSPLIT:\r\n train, test = self.get_train_test_wordsplit()\r\n elif UTTERANCE_SPLIT:\r\n train, test, val = self.get_train_test_utterance_split()\r\n wordlist = joblib.load('wordlist.pkl')\r\n dictionary = joblib.load('dict.pkl')\r\n phones = joblib.load('phones.pkl')\r\n metadata_help = {'wordlist': wordlist, 'dictionary': dictionary, 'phones': phones}\r\n p2c = utils.phone2class(phones)\r\n c2p = utils.class2phone(phones)\r\n \"\"\"CTC loss\"\"\"\r\n # self.ctc_loss = nn.CTCLoss(blank=p2c[config.data.PAD_token], reduction='mean')\r\n self.ctc_loss = nn.CTCLoss(blank=p2c[config.data.PAD_token], reduction='none')\r\n for epoch in range(config.train.num_epochs):\r\n \"\"\"Make dataloader\"\"\"\r\n train_data = Dataset({'files': train, 'mode': 'train', 'metadata_help': metadata_help})\r\n train_gen = data.DataLoader(train_data, batch_size=config.train.batch_size,\r\n shuffle=True, collate_fn=train_data.collate, drop_last=True)\r\n val_data = Dataset({'files': val, 'mode': 'train', 'metadata_help': metadata_help})\r\n val_gen = data.DataLoader(val_data, batch_size=config.train.batch_size,\r\n shuffle=True, collate_fn=val_data.collate, drop_last=True)\r\n\r\n for batch_number, features in enumerate(train_gen):\r\n spectrograms = features['spectrograms']\r\n phones = features['phones']\r\n input_lengths = features['input_lengths']\r\n target_lengths = features['target_lengths']\r\n metadata = features[\"metadata\"]\r\n batch_speakers = [x['speaker'] for x in metadata]\r\n self.G = self.G.train()\r\n\r\n #ipdb.set_trace()\r\n \"\"\"Make input_lengths and target_lengths torch ints\"\"\"\r\n input_lengths = input_lengths.to(torch.int32)\r\n target_lengths = target_lengths.to(torch.int32)\r\n phones = phones.to(torch.int32)\r\n\r\n outputs = self.G(spectrograms)\r\n\r\n outputs = outputs.permute(1, 0, 2) # swap batch and sequence length dimension for CTC loss\r\n\r\n loss = self.ctc_loss(log_probs=outputs, targets=phones,\r\n input_lengths=input_lengths, target_lengths=target_lengths)\r\n\r\n \"\"\"Update the loss history\"\"\"\r\n self.update_history(loss, batch_speakers)\r\n if epoch >= config.train.regular_epochs:\r\n loss_weights = self.get_loss_weights(batch_speakers, type=types[0])\r\n else:\r\n loss_weights = self.get_loss_weights(batch_speakers, type=types[1])\r\n loss = loss * loss_weights\r\n\r\n # Backward and optimize.\r\n self.reset_grad()\r\n # loss.backward()\r\n loss.sum().backward()\r\n self.g_optimizer.step()\r\n\r\n if iterations % self.log_step == 0:\r\n print(str(iterations) + ', loss: ' + str(loss.sum().item()))\r\n if self.use_tensorboard:\r\n self.logger.scalar_summary('loss', loss.sum().item(), iterations)\r\n\r\n if iterations % self.model_save_step == 0:\r\n \"\"\"Calculate validation loss\"\"\"\r\n val_loss = self.val_loss(val=val_gen, iterations=iterations)\r\n print(str(iterations) + ', val_loss: ' + str(val_loss))\r\n if self.use_tensorboard:\r\n self.logger.scalar_summary('val_loss', val_loss, iterations)\r\n \"\"\"Save model checkpoints.\"\"\"\r\n if iterations % self.model_save_step == 0:\r\n G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(iterations))\r\n torch.save({'model': self.G.state_dict(),\r\n 'optimizer': self.g_optimizer.state_dict()}, G_path)\r\n print('Saved model checkpoints into {}...'.format(self.model_save_dir))\r\n\r\n iterations += 1","def _train(self, dataset):\n net = buildNetwork(\n dataset.params_length,\n self._default_hidden_layers,\n 1 # a binary classifier only requires one output layer\n )\n ds = SupervisedDataSet(dataset)\n trainer = BackpropTrainer(net, ds)\n trainer.trainUntilConvergence()\n net.activate(params.as_serialized)","def forward_train(self, preds_T: torch.Tensor) -> torch.Tensor:\n raise NotImplementedError","def train_nlu(data, config, model_dir):\n # Read the config_spacy.json as JSON \n with open(config) as f: config = json.load(f)\n\n training_data = load_data(data)\n trainer = Trainer(RasaNLUModelConfig(config))\n trainer.train(training_data)\n\n model_directory = trainer.persist(model_dir, fixed_model_name= \"weathernlu\")","def train(self):\n self.ae_train(self.net0, self.ae0_optimizer, self.train0_loader, self.val_loader, name='Net0')\n self.ae_train(self.net1, self.ae1_optimizer, self.train1_loader, self.val_loader, name='Net1')\n self.ae_train(self.net2, self.ae2_optimizer, self.train2_loader, self.val_loader, name='Net2')\n\n self.classifier_train(self.net0, self.optimizer0, self.train0_loader, self.val_loader, name='Net0')\n self.classifier_train(self.net1, self.optimizer1, self.train1_loader, self.val_loader, name='Net1')\n self.classifier_train(self.net2, self.optimizer2, self.train2_loader, self.val_loader, name='Net2')","def train(self):\n # For debugging\n torch.autograd.set_detect_anomaly(True)\n # Move variables to device if haven't done so\n self.user_features = self.move_to_cuda(self.user_features, self.device)\n self.item_features = self.move_to_cuda(self.item_features, self.device)\n self.labels = self.move_to_cuda(self.labels, self.device)\n self.model = self.model.to(self.device)\n # Pretrain\n if self.pretrain_bm > 0:\n self.pretrain_bm_net(self.pretrain_bm)\n if self.pretrain_nc > 0:\n self.pretrain_nc_net(self.pretrain_nc)\n # optimizers\n optims = MultipleOptimizer(torch.optim.Adam(self.model.bm_net.parameters(), lr=self.lr),\n torch.optim.Adam(self.model.nc_net.parameters(), lr=self.lr, weight_decay=self.weight_decay))\n # optims = torch.optim.Adam(self.model.parameters(), lr = self.lr)\n\n # criterion = nn.CrossEntropyLoss()\n criterion = F.nll_loss\n best_test_auc = 0.\n best_val_auc = 0.\n best_res = None\n cnt_wait = 0\n patience = 50\n # Training...\n for epoch in range(self.n_epochs):\n self.model.train()\n self.model.zero_grad()\n input_adj = self.adj.clone()\n input_adj = input_adj.to(self.device)\n nc_logits, modified_adj, bm_loss, nc_loss = self.model(input_adj, self.user_features, self.item_features, self.n_epochs, epoch)\n loss = nc_loss + bm_loss * self.alpha\n optims.zero_grad()\n loss.backward()\n # for name, params in self.model.named_parameters():\n # \tif params.requires_grad:\n # \t\tprint(f'{name}: requires grad')\n # \t\tprint(torch.sum(params.grad))\n optims.step()\n # Computation Graph\n # Validation\n self.model.eval()\n with torch.no_grad():\n # input_adj = self.adj.clone()\n # input_adj = input_adj.to(self.device)\n # nc_logits_eval_original, _ = self.model.nc_net(input_adj, self.user_features, self.item_features)\n # input_adj = self.adj.clone()\n # input_adj = input_adj.to(self.device)\n nc_logits_eval_modified, _, _, _ = self.model(input_adj, self.user_features, self.item_features, self.n_epochs, epoch)\n training_res = self.eval_node_cls(nc_logits[self.train_nid].detach(), self.labels[self.train_nid], self.n_classes)\n # res = self.eval_node_cls(nc_logits_eval_original[self.val_nid], self.labels[self.val_nid], self.n_classes)\n res_modified = self.eval_node_cls(nc_logits_eval_modified[self.val_nid], self.labels[self.val_nid], self.n_classes)\n if res_modified['auc'] > best_val_auc:\n cnt_wait = 0\n best_val_auc = res_modified['auc']\n # res_test = self.eval_node_cls(nc_logits_eval_original[self.test_nid], self.labels[self.test_nid], self.n_classes)\n res_test_modified = self.eval_node_cls(nc_logits_eval_modified[self.test_nid], self.labels[self.test_nid], self.n_classes)\n if res_test_modified['auc'] > best_test_auc:\n best_test_auc = res_test_modified['auc']\n best_res = res_test_modified\n self.logger.info('Eland Training, Epoch [{}/{}]: loss {:.4f}, train_auc: {:.4f}, val_auc {:.4f}, test_auc {:.4f}, test_ap {:.4f}'\n .format(epoch+1, self.n_epochs, loss.item(), training_res['auc'], res_modified['auc'], res_test_modified['auc'], res_test_modified['ap']))\n else:\n cnt_wait += 1\n self.logger.info('Eland Training, Epoch [{}/{}]: loss {:.4f}, train_auc: {:.4f}, val_auc {:.4f}'\n .format(epoch+1, self.n_epochs, loss.item(), training_res['auc'], res_modified['auc']))\n\n if cnt_wait >= patience:\n self.logger.info('Early stop!')\n break\n self.logger.info('Best Test Results: auc {:.4f}, ap {:.4f}'.format(best_res['auc'], best_res['ap']))\n\n return best_res['auc'], best_res['ap']","def train(model: ContinualModel, dataset: ContinualDataset,\n args: Namespace) -> None:\n model.net.to(model.device)\n results, results_mask_classes = [], []\n \n model_stash = create_stash(model, args, dataset)\n \n if args.csv_log:\n csv_logger = CsvLogger(dataset.SETTING, dataset.NAME, model.NAME)\n if args.tensorboard:\n tb_logger = TensorboardLogger(args, dataset.SETTING, model_stash)\n model_stash['tensorboard_name'] = tb_logger.get_name()\n \n dataset_copy = get_dataset(args)\n for t in range(dataset.N_TASKS):\n model.net.train()\n _, _ = dataset_copy.get_data_loaders()\n if model.NAME != 'icarl' and model.NAME != 'pnn':\n random_results_class, random_results_task = evaluate(model, dataset_copy)\n \n print(file=sys.stderr)\n for t in range(dataset.N_TASKS):\n model.net.train()\n train_loader, test_loader = dataset.get_data_loaders()\n if hasattr(model, 'begin_task'):\n model.begin_task(dataset)\n if t:\n accs = evaluate(model, dataset, last=True)\n results[t - 1] = results[t - 1] + accs[0]\n if dataset.SETTING == 'class-il':\n results_mask_classes[t - 1] = results_mask_classes[t - 1] + accs[1]\n for epoch in range(args.n_epochs):\n for i, data in enumerate(train_loader):\n if hasattr(dataset.train_loader.dataset, 'logits'):\n inputs, labels, not_aug_inputs, logits = data\n inputs = inputs.to(model.device)\n labels = labels.to(model.device)\n not_aug_inputs = not_aug_inputs.to(model.device)\n logits = logits.to(model.device)\n loss = model.observe(inputs, labels, not_aug_inputs, logits)\n else:\n inputs, labels, not_aug_inputs = data\n inputs, labels = inputs.to(model.device), labels.to(\n model.device)\n not_aug_inputs = not_aug_inputs.to(model.device)\n loss = model.observe(inputs, labels, not_aug_inputs)\n \n progress_bar(i, len(train_loader), epoch, t, loss)\n \n if args.tensorboard:\n tb_logger.log_loss(loss, args, epoch, t, i)\n \n model_stash['batch_idx'] = i + 1\n model_stash['epoch_idx'] = epoch + 1\n model_stash['batch_idx'] = 0\n model_stash['task_idx'] = t + 1\n model_stash['epoch_idx'] = 0\n \n if hasattr(model, 'end_task'):\n model.end_task(dataset)\n \n accs = evaluate(model, dataset)\n results.append(accs[0])\n results_mask_classes.append(accs[1])\n \n mean_acc = np.mean(accs, axis=1)\n print_mean_accuracy(mean_acc, t + 1, dataset.SETTING)\n \n model_stash['mean_accs'].append(mean_acc)\n if args.csv_log:\n csv_logger.log(mean_acc)\n if args.tensorboard:\n tb_logger.log_accuracy(np.array(accs), mean_acc, args, t)\n \n if args.csv_log:\n csv_logger.add_bwt(results, results_mask_classes)\n csv_logger.add_forgetting(results, results_mask_classes)\n if model.NAME != 'icarl' and model.NAME != 'pnn':\n csv_logger.add_fwt(results, random_results_class,\n results_mask_classes, random_results_task)\n \n if args.tensorboard:\n tb_logger.close()\n if args.csv_log:\n csv_logger.write(vars(args))","def train(self, X, y):\n self.model.fit(X, y)"],"string":"[\n \"def train(train_loader : torch.utils.data.DataLoader, model : nn.Module, criterion : nn.Module, optimizer : torch.optim.Optimizer) -> logger.Result:\",\n \"def trainNet():\",\n \"def train(args, model, train_data_loader, dev_data_loader, device):\\n\\n\\tmodel.train()\\n\\toptimizer = torch.optim.Adam(model.parameters())\\n\\tprint_loss_total = 0\\n\\tepoch_loss_total = 0\\n\\tstart = time.time()\\n\\n\\t#### modify the following code to complete the training funtion\\n\\n\\tbest_train_acc, best_dev_acc = 0.0, 0.0\\n\\n\\tfor idx, batch in enumerate(train_data_loader):\\n\\t\\tquestion_feature_vec = batch['feature_vec'].to(device)\\n\\t\\tquestion_len = batch['len'].to(device)\\n\\t\\tlabels = batch['labels'].to(device)\\n\\n\\t\\t#### Your code here ----\\n\\n\\t\\t# zero out\\n\\t\\toptimizer.zero_grad()\\n\\n\\t\\t# get output from model\\n\\t\\tlogits = model(question_feature_vec, question_len)\\n\\n\\t\\t# use loss_fn defined above to calculate loss\\n\\t\\tloss = loss_fn(logits, labels)\\n\\n\\t\\t# use accuracy_fn defined above to calculate 'error' and number of examples ('num_examples') used to\\n\\t\\t# calculate accuracy below.\\n\\t\\terror, num_examples = accuracy_fn(logits, labels)\\n\\n\\t\\t# backprop\\n\\t\\tloss.backward()\\n\\t\\toptimizer.step()\\n\\n\\t\\t###Your code ends ---\\n\\t\\taccuracy = 1 - error / num_examples\\n\\t\\tclip_grad_norm_(model.parameters(), 5)\\n\\t\\tprint_loss_total += loss.data.numpy()\\n\\t\\tepoch_loss_total += loss.data.numpy()\\n\\n\\t\\tif (idx + 1) % args.checkpoint == 0 and idx > 0:\\n\\t\\t\\tprint_loss_avg = print_loss_total / args.checkpoint\\n\\n\\t\\t\\tdev_acc = evaluate(dev_data_loader, model, device)\\n\\n\\t\\t\\tprint('number of steps: %d, train loss: %.5f, train acc: %.3f, dev acc: %.3f, time: %.5f'\\n\\t\\t\\t % (idx + 1, print_loss_avg, accuracy, dev_acc, time.time() - start))\\n\\t\\t\\tprint_loss_total = 0\\n\\t\\t\\tif accuracy > best_train_acc:\\n\\t\\t\\t\\tbest_train_acc = accuracy\\n\\t\\t\\tif dev_acc > best_dev_acc:\\n\\t\\t\\t\\tbest_dev_acc = dev_acc\\n\\n\\treturn best_train_acc, best_dev_acc\",\n \"def run(self) -> None:\\n self.model = self.trainer.train_model(self.model, self.data)\",\n \"def train(model, optimizer: torch.optim, data: torch_geometric.data.Data):\\n model.train()\\n optimizer.zero_grad()\\n F.nll_loss(model()[data.train_mask], data.y[data.train_mask]).backward()\\n optimizer.step()\\n\\n model.eval()\",\n \"def train(args, model, data_loader, optimizer):\\n # Keep track of average accuracy and loss\\n avg_loss = utils.AverageMeter()\\n\\n # Set training mode\\n model.train()\\n\\n # for inputs, target in tqdm(data_loader):\\n for inputs, targets in tqdm(data_loader):\\n # Prep\\n inputs = utils.wrap_variables(inputs, cuda=args.cuda)\\n targets = utils.wrap_variables(targets, cuda=args.cuda)\\n\\n # Run forward\\n predictions, log_likelihood = model(**inputs)\\n\\n # Loss = -NLL\\n loss = -log_likelihood\\n avg_loss.update(loss.data[0], len(inputs['x_tags']))\\n\\n # Run backward\\n optimizer.zero_grad()\\n loss.backward()\\n optimizer.step()\\n\\n return {'loss': avg_loss}\",\n \"def train_model(self, *args, **kwargs):\\n self.model.train(self.training, *args, **kwargs)\",\n \"def train(self):\\n\\t\\tself.model.fit(self.training_data, self.training_labels)\",\n \"def train(args: Dict):\\n device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n print('use device: %s' % device)\\n\\n train_data_src = read_corpus(args['--train-src'], source='src')\\n train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')\\n\\n dev_data_src = read_corpus(args['--dev-src'], source='src')\\n dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')\\n\\n train_data = list(zip(train_data_src, train_data_tgt))\\n dev_data = list(zip(dev_data_src, dev_data_tgt))\\n\\n train_batch_size = int(args['--batch-size'])\\n N = int(args['--N'])\\n d_model = int(args['--d_model'])\\n d_ff = int(args['--d_ff'])\\n h = int(args['--h'])\\n dropout = float(args['--dropout'])\\n\\n valid_niter = int(args['--valid-niter'])\\n log_every = int(args['--log-every'])\\n model_save_path = args['--save-to']\\n lr=float(args['--lr'])\\n\\n vocab = Vocab.load(args['--vocab'])\\n vocab_mask = torch.ones(len(vocab.tgt))\\n vocab_mask[vocab.tgt['<pad>']] = 0\\n\\n model = make_model(len(vocab.src), len(vocab.tgt), N, d_model, d_ff, h, dropout)\\n model = model.to(device)\\n\\n optimizer = NoamOpt(model.src_embed[0].d_model, 1, 400,\\n torch.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.98), eps=1e-9))\\n\\n num_trial = 0\\n train_iter = patience = cum_loss = report_loss = cum_tgt_words = report_tgt_words = 0\\n cum_exmaples = report_examples = epoch = valid_num = 0\\n hist_valid_scores = []\\n train_time = begin_time = time.time()\\n print('begin Maximum Likelihood Training')\\n\\n while True:\\n epoch += 1\\n for src_sents, tgt_sents in batch_iter(train_data, batch_size=train_batch_size, shuffle=True):\\n train_iter += 1\\n optimizer.zero_grad()\\n batch_size = len(src_sents)\\n\\n example_losses = - model(src_sents, tgt_sents) #(batch_size,)\\n batch_loss = example_losses.sum()\",\n \"def train():\\n model.train()\\n for batch_index, (xb, yb) in enumerate(train_dl):\\n loss = loss_func(model(xb), yb)\\n\\n loss.backward()\\n opt.step()\\n opt.zero_grad()\",\n \"def train(self, *args, **kwargs):\\n # Handle overload of train() method\\n if len(args) < 1 or (len(args) == 1 and type(args[0]) == bool):\\n return nn.Sequential.train(self, *args, **kwargs)\\n\\n #\\n # Parse training arguments\\n #\\n\\n training_data = args[0]\\n arguments = {\\n \\\"validation_data\\\": None,\\n \\\"batch_size\\\": 256,\\n \\\"sigma_noise\\\": None,\\n \\\"adversarial_training\\\": False,\\n \\\"delta_at\\\": 0.01,\\n \\\"initial_learning_rate\\\": 1e-2,\\n \\\"momentum\\\": 0.0,\\n \\\"convergence_epochs\\\": 5,\\n \\\"learning_rate_decay\\\": 2.0,\\n \\\"learning_rate_minimum\\\": 1e-6,\\n \\\"maximum_epochs\\\": 1,\\n \\\"training_split\\\": 0.9,\\n \\\"gpu\\\": False,\\n \\\"optimizer\\\": None,\\n \\\"learning_rate_scheduler\\\": None\\n }\\n argument_names = arguments.keys()\\n for a, n in zip(args[1:], argument_names):\\n arguments[n] = a\\n for k in kwargs:\\n if k in arguments:\\n arguments[k] = kwargs[k]\\n else:\\n raise ValueError(\\\"Unknown argument to {}.\\\".print(k))\\n\\n validation_data = arguments[\\\"validation_data\\\"]\\n batch_size = arguments[\\\"batch_size\\\"]\\n sigma_noise = arguments[\\\"sigma_noise\\\"]\\n adversarial_training = arguments[\\\"adversarial_training\\\"]\\n delta_at = arguments[\\\"delta_at\\\"]\\n initial_learning_rate = arguments[\\\"initial_learning_rate\\\"]\\n convergence_epochs = arguments[\\\"convergence_epochs\\\"]\\n learning_rate_decay = arguments[\\\"learning_rate_decay\\\"]\\n learning_rate_minimum = arguments[\\\"learning_rate_minimum\\\"]\\n maximum_epochs = arguments[\\\"maximum_epochs\\\"]\\n training_split = arguments[\\\"training_split\\\"]\\n gpu = arguments[\\\"gpu\\\"]\\n momentum = arguments[\\\"momentum\\\"]\\n optimizer = arguments[\\\"optimizer\\\"]\\n learning_rate_scheduler = arguments[\\\"learning_rate_scheduler\\\"]\\n\\n #\\n # Determine device to use\\n #\\n if torch.cuda.is_available() and gpu:\\n device = torch.device(\\\"cuda\\\")\\n else:\\n device = torch.device(\\\"cpu\\\")\\n self.to(device)\\n\\n #\\n # Handle input data\\n #\\n try:\\n x, y = handle_input(training_data, device)\\n training_data = BatchedDataset((x, y), batch_size)\\n except:\\n pass\\n\\n self.train()\\n if not optimizer:\\n self.optimizer = optim.SGD(\\n self.parameters(), lr=initial_learning_rate, momentum=momentum\\n )\\n else:\\n self.optimizer = optimizer\\n self.criterion.to(device)\\n\\n if not optimizer and not learning_rate_scheduler:\\n scheduler = ReduceLROnPlateau(\\n self.optimizer,\\n factor=1.0 / learning_rate_decay,\\n patience=convergence_epochs,\\n min_lr=learning_rate_minimum,\\n )\\n else:\\n scheduler = learning_rate_scheduler\\n\\n training_errors = []\\n validation_errors = []\\n\\n #\\n # Training loop\\n #\\n\\n for i in range(maximum_epochs):\\n err = 0.0\\n n = 0\\n for j, (x, y) in enumerate(training_data):\\n\\n x = x.to(device)\\n y = y.to(device)\\n\\n shape = x.size()\\n shape = (shape[0], 1) + shape[2:]\\n y = y.reshape(shape)\\n\\n self.optimizer.zero_grad()\\n y_pred = self(x)\\n c = self.criterion(y_pred, y)\\n c.backward()\\n self.optimizer.step()\\n\\n err += c.item() * x.size()[0]\\n n += x.size()[0]\\n\\n if adversarial_training:\\n self.optimizer.zero_grad()\\n x_adv = self._make_adversarial_samples(x, y, delta_at)\\n y_pred = self(x)\\n c = self.criterion(y_pred, y)\\n c.backward()\\n self.optimizer.step()\\n\\n if j % 100:\\n print(\\n \\\"Epoch {} / {}: Batch {} / {}, Training error: {:.3f}\\\".format(\\n i, maximum_epochs, j, len(training_data), err / n\\n ),\\n end=\\\"\\\\r\\\",\\n )\\n\\n # Save training error\\n training_errors.append(err / n)\\n\\n lr = [group[\\\"lr\\\"] for group in self.optimizer.param_groups][0]\\n\\n val_err = 0.0\\n if not validation_data is None:\\n n = 0\\n for x, y in validation_data:\\n x = x.to(device).detach()\\n y = y.to(device).detach()\\n\\n shape = x.size()\\n shape = (shape[0], 1) + shape[2:]\\n y = y.reshape(shape)\\n\\n y_pred = self(x)\\n c = self.criterion(y_pred, y)\\n\\n val_err += c.item() * x.size()[0]\\n n += x.size()[0]\\n validation_errors.append(val_err / n)\\n\\n print(\\n \\\"Epoch {} / {}: Training error: {:.3f}, Validation error: {:.3f}, Learning rate: {:.5f}\\\".format(\\n i,\\n maximum_epochs,\\n training_errors[-1],\\n validation_errors[-1],\\n lr,\\n )\\n )\\n if scheduler:\\n scheduler.step()\\n else:\\n scheduler.step()\\n print(\\n \\\"Epoch {} / {}: Training error: {:.3f}, Learning rate: {:.5f}\\\".format(\\n i, maximum_epochs, training_errors[-1], lr\\n )\\n )\\n\\n self.training_errors += training_errors\\n self.validation_errors += validation_errors\\n self.eval()\\n return {\\n \\\"training_errors\\\": self.training_errors,\\n \\\"validation_errors\\\": self.validation_errors,\\n }\",\n \"def train(self):\\n\\n # Ensure everything is sent to GPU if being trained on the cloud\\n if self.local == False:\\n torch.set_default_tensor_type(torch.cuda.FloatTensor)\\n print(\\\"\\\\n \\\\n EVERYTHING TO CUDA \\\\n \\\\n\\\")\\n\\n # Load weights if applicable\\n if self.load_weights == True:\\n start_epoch, loss = self.load_checkpoint(\\n self.model, self.optimizer, self.model_name\\n )\\n start_epoch += 1\\n print(\\\"\\\\n \\\\n [WEIGHTS LOADED]\\\")\\n else:\\n start_epoch = 0\\n\\n # Start Training Loop\\n for epoch in range(start_epoch, self.epochs + 1):\\n\\n # TRAIN\\n if epoch > 0:\\n\\n # Set model to training mode\\n self.model.train()\\n\\n # Initialize loss and counter that will allow model weights to be saved and overwritten every 10 minibatches\\n train_loss = 0\\n counter = 0\\n\\n # Iterate through train set\\n for (\\n image1,\\n image2,\\n annotation1,\\n annotation2,\\n landmark1,\\n landmark2,\\n ) in tqdm(self.train_loader, desc=\\\"Train Epoch \\\" + str(epoch)):\\n\\n # image tensors and bounding box and label tensors to device\\n image1 = image1.to(self.device)\\n image2 = image2.to(self.device)\\n\\n # Forward pass of model\\n x1_hat, x2_hat, z1, z2 = self.model(image1, image2)\\n\\n # Calculate loss from one pass and append to training loss\\n loss = self.loss_fcn(\\n image1, image2, x1_hat, x2_hat, z1.detach(), z2.detach()\\n )\\n train_loss += loss.item()\\n\\n # Clear optimizer gradient\\n self.optimizer.zero_grad()\\n\\n # Backprop\\n loss.backward()\\n\\n # Take a step with optimizer\\n self.optimizer.step()\\n\\n # Save/overwrite model weights every 10 minibatches\\n if counter % 10 == 0:\\n self.save_checkpoint(\\n self.model,\\n self.optimizer,\\n self.model_name,\\n epoch,\\n train_loss,\\n )\\n\\n print(\\n f\\\"====> Epoch: {epoch} Average train loss: {train_loss / len(self.train_loader.dataset):.4f}\\\\n\\\"\\n )\\n\\n # Save entire model as .pt after every epoch of training\\n if self.local == True:\\n torch.save(\\n self.model,\\n os.path.join(self.save_path, self.model_name + \\\".pt\\\"),\\n )\\n else:\\n torch.save(\\n self.model, self.model_name + \\\"_epoch\\\" + str(epoch) + \\\".pt\\\"\\n )\\n print(\\\"SAVED MODEL EPOCH \\\" + str(epoch))\\n\\n # Evaluate on Validation Set after each epoch\\n with torch.no_grad():\\n\\n # Set model to evaluation mode\\n self.model.eval()\\n\\n # Iterate through validation set\\n for image1, image2, annotation1, annotation2 in tqdm(\\n self.val_loader, desc=\\\"Validation Epoch \\\" + str(epoch)\\n ):\\n\\n # Initialize validation loss\\n val_loss = 0\\n\\n # Send images to device\\n image1 = image1.to(self.device)\\n image2 = image2.to(self.device)\\n\\n # Forward pass of model\\n x1_hat, x2_hat, z1, z2 = self.model(image1, image2)\\n\\n # Calculate loss and append to validation loss\\n loss = self.loss_fcn(\\n image1, image2, x1_hat, x2_hat, z1.detach(), z2.detach()\\n )\\n val_loss += loss\\n\\n print(\\n f\\\"====> Epoch: {epoch} Average test loss: {val_loss / len(self.val_loader.dataset):.4f}\\\\n\\\"\\n )\\n\\n print(\\\"[DONE EPOCH{}]\\\".format(epoch))\\n\\n print(\\\"[DONE TRAINING]\\\")\\n\\n # Save model after all epochs are finished\\n if self.local == True:\\n torch.save(\\n self.model, os.path.join(self.save_path, self.model_name + \\\".pt\\\")\\n )\\n else:\\n torch.save(self.model, self.model_name + \\\".pt\\\")\",\n \"def train_model(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def train(data_loader, model, optimizer, device):\\n\\t# put the model in train mode\\n\\tmodel.train()\\n\\t# go over every batch of data in data loader\\n\\tfor data in data_loader:\\n\\t\\t# remember we have image and targets in our dataset class\\n\\t\\tinputs = data['image']\\n\\t\\ttargets = data['targets']\\n\\t\\t# remove inputs/targets to cuda/cpu device\\n\\t\\tinputs = inputs.to(device,dtype = torch.float)\\n\\t\\ttargets = targets.to(device,dtype = torch.float)\\n\\t\\t# zero grad the optimizer\\n\\toptimizer.zero_grad()\\n\\t# do the forward step of model\\n\\toutputs = model(inputs)\\n\\t# calculate loss\\n\\tloss = nn.BCEWithLogitsLoss()(outputs, targets.view(-1, 1)) \\n\\t# backward step the loss\\n\\tloss.backward()\\n\\t# step optimizer\\n\\toptimizer.step()\\n\\t# if you have a scheduler, you either need to\\n\\t# step it here or you have to step it after\\n\\t# the epoch. here, we are not using any learning # rate scheduler\",\n \"def train(self):\\n self.emission_model(self.train_data)\\n self.transition_model(self.train_data)\",\n \"def train(self):\\n print('Model training...')\\n self.model.train()\\n self.likelihood.train()\\n optimizer = torch.optim.Adam(\\n [{'params': self.model.parameters()}], lr=self.lr)\\n mll = gpytorch.mlls.ExactMarginalLogLikelihood(\\n self.likelihood, self.model)\\n for i in range(self.steps):\\n optimizer.zero_grad()\\n output = self.model(self.X)\\n loss = -mll(output, self.y)\\n loss.backward()\\n self.lscales.append(\\n self.model.covar_module.base_kernel.base_kernel.lengthscale.tolist()[0]\\n )\\n self.noise_all.append(\\n self.model.likelihood.noise_covar.noise.item())\\n if self.verbose:\\n print('iter: {} ...'.format(i),\\n 'loss: {} ...'.format(np.around(loss.item(), 4)),\\n 'length: {} ...'.format(np.around(self.lscales[-1], 4)),\\n 'noise: {} ...'.format(np.around(self.noise_all[-1], 7)))\\n optimizer.step()\\n return\",\n \"def train(self, train_loader):\\n\\n self.model.train()\\n with torch.enable_grad():\\n return self._iteration(train_loader)\",\n \"def train():\\n pass\",\n \"def train(args, data_loader, model, global_stats):\\n # Initialize meters + timers\\n train_loss = AverageMeter()\\n epoch_time = Timer()\\n \\n for batch_idx, (input_idxs, target_idxs, input_tokens, target_tokens) in enumerate(data_loader):\\n # input_idxs and target_idxs have dim (batch_size x max_len)\\n # they are NOT sorted by length\\n\\n lengths = (input_idxs != 0).long().sum(dim=1)\\n sorted_lengths, order = torch.sort(lengths, descending=True)\\n\\n input_variable = Variable(input_idxs[order, :][:, :max(lengths)])\\n target_variable = Variable(target_idxs[order, :])\\n \\n model.optimizer.zero_grad()\\n output_log_probs, output_ses = model(input_variable,\\n list(sorted_lengths),\\n targets=target_variable)\\n \\n batch_size = input_variable.shape[0]\\n flattened_outputs = output_log_probs.view(batch_size * model.max_length, -1)\\n \\n batch_loss = model.citerion(flattened_outputs, target_variable.contiguous().view(-1))\\n batch_loss.backward()\\n model.optimizer.step()\\n \\n model.updates += 1\\n \\n train_loss.update(batch_loss[0], batch_size)\\n \\n if batch_idx % args.display_iter == 0:\\n logger.info('train: Epoch = %d | iter = %d/%d | ' %\\n (global_stats['epoch'], batch_idx, len(data_loader)) +\\n 'loss = %.2f | elapsed time = %.2f (s)' %\\n (train_loss.avg, global_stats['timer'].time()))\\n train_loss.reset()\\n \\n logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' %\\n (global_stats['epoch'], epoch_time.time()))\\n \\n # Checkpoint\\n if args.checkpoint:\\n model.checkpoint(args.model_file + '.checkpoint',\\n global_stats['epoch'] + 1)\",\n \"def train(self):\\n train_dataloader = self.get_train_dataloader()\\n\\n if self.args.max_steps > 0:\\n t_total = self.args.max_steps\\n num_train_epochs = (\\n self.args.max_steps // (len(train_dataloader) // self.args.gradient_accumulation_steps) + 1\\n )\\n else:\\n t_total = int(len(train_dataloader) // self.args.gradient_accumulation_steps * self.args.num_train_epochs)\\n num_train_epochs = self.args.num_train_epochs\\n\\n lr_scheduler = orttrainer.optim.LinearWarmupLRScheduler(t_total, self.args.warmup_steps / float(t_total))\\n\\n loss_scaler = amp.DynamicLossScaler() if self.args.fp16 else None\\n device = self.args.device.type\\n\\n device = f\\\"{device}:{self.args.device.index}\\\" if self.args.device.index else f\\\"{device}:0\\\"\\n options = orttrainer.ORTTrainerOptions(\\n {\\n \\\"batch\\\": {\\\"gradient_accumulation_steps\\\": self.args.gradient_accumulation_steps},\\n \\\"device\\\": {\\\"id\\\": device},\\n \\\"mixed_precision\\\": {\\\"enabled\\\": self.args.fp16, \\\"loss_scaler\\\": loss_scaler},\\n \\\"debug\\\": {\\n \\\"deterministic_compute\\\": True,\\n },\\n \\\"utils\\\": {\\\"grad_norm_clip\\\": False},\\n \\\"distributed\\\": {\\n # we are running single node multi gpu test. thus world_rank = local_rank\\n # and world_size = self.args.n_gpu\\n \\\"world_rank\\\": max(0, self.args.local_rank),\\n \\\"world_size\\\": int(self.world_size),\\n \\\"local_rank\\\": max(0, self.args.local_rank),\\n \\\"allreduce_post_accumulation\\\": True,\\n },\\n \\\"lr_scheduler\\\": lr_scheduler,\\n }\\n )\\n\\n param_optimizer = list(self.model.named_parameters())\\n params = [\\n {\\n \\\"params\\\": [n for n, p in param_optimizer if \\\"bias\\\" in n or \\\"LayerNorm.weight\\\" in n],\\n \\\"weight_decay_mode\\\": 1,\\n },\\n {\\n \\\"params\\\": [n for n, p in param_optimizer if not (\\\"bias\\\" in n or \\\"LayerNorm.weight\\\" in n)],\\n \\\"weight_decay_mode\\\": 1,\\n },\\n ]\\n\\n optim_config = optim.AdamConfig(params=params, lr=2e-5, do_bias_correction=True)\\n self.model = orttrainer.ORTTrainer(self.model, self.model_desc, optim_config, options=options)\\n\\n # Train!\\n logger.info(\\\"***** Running training *****\\\")\\n logger.info(\\\" Num examples = %d\\\", len(train_dataloader.dataset))\\n logger.info(\\\" Num Epochs = %d\\\", num_train_epochs)\\n logger.info(\\\" Instantaneous batch size per GPU = %d\\\", self.args.per_gpu_train_batch_size)\\n logger.info(\\n \\\" Total train batch size (w. parallel, distributed & accumulation) = %d\\\",\\n self.args.train_batch_size\\n * self.args.gradient_accumulation_steps\\n * (torch.distributed.get_world_size() if self.args.local_rank != -1 else 1),\\n )\\n logger.info(\\\" Gradient Accumulation steps = %d\\\", self.args.gradient_accumulation_steps)\\n logger.info(\\\" Total optimization steps = %d\\\", t_total)\\n\\n global_step = 0\\n epochs_trained = 0\\n steps_trained_in_current_epoch = 0\\n\\n tr_loss = 0.0\\n logging_loss = 0.0\\n train_iterator = trange(\\n epochs_trained,\\n int(num_train_epochs),\\n desc=\\\"Epoch\\\",\\n disable=self.args.local_rank not in [-1, 0],\\n )\\n\\n for _epoch in train_iterator:\\n epoch_iterator = tqdm(train_dataloader, desc=\\\"Iteration\\\", disable=self.args.local_rank not in [-1, 0])\\n for step, inputs in enumerate(epoch_iterator):\\n # Skip past any already trained steps if resuming training\\n if steps_trained_in_current_epoch > 0:\\n steps_trained_in_current_epoch -= 1\\n continue\\n\\n tr_loss += self._training_step(self.model, inputs)\\n\\n if (step + 1) % self.args.gradient_accumulation_steps == 0 or (\\n len(epoch_iterator) <= self.args.gradient_accumulation_steps and (step + 1) == len(epoch_iterator)\\n ):\\n global_step += 1\\n\\n if self.args.local_rank in [-1, 0]:\\n if (self.args.logging_steps > 0 and global_step % self.args.logging_steps == 0) or (\\n global_step == 1 and self.args.logging_first_step\\n ):\\n logs = {}\\n if self.args.evaluate_during_training:\\n results = self.evaluate()\\n for key, value in results.items():\\n eval_key = f\\\"eval_{key}\\\"\\n logs[eval_key] = value\\n\\n loss_scalar = (tr_loss - logging_loss) / self.args.logging_steps\\n\\n logs[\\\"loss\\\"] = loss_scalar\\n logging_loss = tr_loss\\n\\n epoch_iterator.write(json.dumps({**logs, **{\\\"step\\\": global_step}}))\\n\\n if self.args.max_steps > 0 and global_step > self.args.max_steps:\\n epoch_iterator.close()\\n break\\n if self.args.max_steps > 0 and global_step > self.args.max_steps:\\n train_iterator.close()\\n break\\n\\n logger.info(\\\"\\\\n\\\\nTraining completed. \\\\n\\\\n\\\")\\n return TrainOutput(global_step, tr_loss / global_step)\",\n \"def trainModel( self, featureTrain, classTrain):\",\n \"def train(model, data_loader, optimizer, epoch, train_mloss, train_rloss, train_acc, learning_rate, lr_wr, output_tensor):\\r\\n print('===> Training mode')\\r\\n\\r\\n num_batches = len(data_loader) # iteration per epoch. e.g: 469\\r\\n total_step = args.epochs * num_batches\\r\\n epoch_tot_acc = 0\\r\\n\\r\\n # Switch to train mode\\r\\n model.train()\\r\\n\\r\\n if args.cuda:\\r\\n # When we wrap a Module in DataParallel for multi-GPUs\\r\\n model = model.module\\r\\n\\r\\n start_time = timer()\\r\\n\\r\\n for batch_idx, (data, target) in enumerate(tqdm(data_loader, unit='batch')):\\r\\n batch_size = data.size(0)\\r\\n global_step = batch_idx + (epoch * num_batches) - num_batches\\r\\n\\r\\n labels = target\\r\\n target_one_hot = utils.one_hot_encode(target, length=args.num_classes)\\r\\n assert target_one_hot.size() == torch.Size([batch_size, 10])\\r\\n\\r\\n data, target = Variable(data), Variable(target_one_hot)\\r\\n\\r\\n if args.cuda:\\r\\n data = data.to(args.device)\\r\\n target = target.to(args.device)\\r\\n labels = labels.to(args.device)\\r\\n\\r\\n # Train step - forward, backward and optimize\\r\\n optimizer.zero_grad()\\r\\n #utils.exponential_decay_LRR(optimizer, args.lr, global_step, args.decay_steps, args.decay_rate, args.staircase)\\r\\n # learning rate policies\\r\\n if args.find_lr:\\r\\n utils.find_lr(optimizer, global_step)\\r\\n\\r\\n elif args.exp_decay_lr:\\r\\n utils.exponential_decay_LRR(\\r\\n optimizer, args.lr, global_step, args.decay_steps, args.decay_rate, args.staircase)\\r\\n\\r\\n elif args.one_cycle_policy:\\r\\n utils.one_cycle_policy(optimizer, args.lr, global_step, total_step)\\r\\n\\r\\n elif args.warm_restarts:\\r\\n # lr_wr.update_lr(optimizer, num_batches)\\r\\n lr_wr.update_lr(optimizer)\\r\\n\\r\\n output, reconstruction = model(data, labels, True)\\r\\n # utils.write_tensor(output, output_tensor)\\r\\n loss, margin_loss, recon_loss = loss_func(\\r\\n output, target, args.regularization_scale, reconstruction, data, args.device, batch_size)\\r\\n loss.backward()\\r\\n optimizer.step()\\r\\n\\r\\n for param_group in optimizer.param_groups:\\r\\n lr_temp = param_group['lr']\\r\\n learning_rate.write('%.10f \\\\n' % lr_temp)\\r\\n\\r\\n # Calculate accuracy for each step and average accuracy for each epoch\\r\\n acc = utils.accuracy(output, labels, args.cuda)\\r\\n epoch_tot_acc += acc\\r\\n epoch_avg_acc = epoch_tot_acc / (batch_idx + 1)\\r\\n\\r\\n train_mloss.write('%.6f \\\\n' % margin_loss)\\r\\n train_rloss.write('%.6f \\\\n' % recon_loss)\\r\\n train_acc.write('%.6f \\\\n' % acc)\\r\\n\\r\\n # Print losses\\r\\n if batch_idx % args.log_interval == 0:\\r\\n template = 'Epoch {}/{}, ' \\\\\\r\\n 'Step {}/{}: ' \\\\\\r\\n '[Total loss: {:.6f},' \\\\\\r\\n '\\\\tMargin loss: {:.6f},' \\\\\\r\\n '\\\\tReconstruction loss: {:.6f},' \\\\\\r\\n '\\\\tBatch accuracy: {:.6f},' \\\\\\r\\n '\\\\tAccuracy: {:.6f}]'\\r\\n tqdm.write(template.format(\\r\\n epoch,\\r\\n args.epochs,\\r\\n global_step,\\r\\n total_step,\\r\\n loss.data.item(),\\r\\n margin_loss.data.item(),\\r\\n recon_loss.data.item() if args.use_reconstruction_loss else 0,\\r\\n acc,\\r\\n epoch_avg_acc))\\r\\n\\r\\n # Print time elapsed for an epoch\\r\\n end_time = timer()\\r\\n\\r\\n global avg_training_time_per_epoch\\r\\n\\r\\n avg_training_time_per_epoch = (avg_training_time_per_epoch * (epoch - 1) + end_time - start_time) / epoch\\r\\n\\r\\n print('Time elapsed for epoch {}: {:.0f}s.'.format(epoch, end_time - start_time))\",\n \"def train(args: dict, lr_index: int, dataloaders: Tuple[DataLoader]):\\n train_dataloader, val_dataloader = dataloaders\\n device = torch.device('cuda') if args.gpu else torch.device('cpu')\\n model = get_model(args, device)\\n if args.label_weights is None:\\n label_weights = None\\n else:\\n label_weights = args.label_weights.to(device)\\n loss_criterion = nn.CrossEntropyLoss(weight=label_weights)\\n optimizer = torch.optim.AdamW(\\n model.parameters(), lr=args.learning_rates[lr_index]\\n )\\n train_losses = []\\n val_losses = []\\n stats = {\\\"true\\\": [], \\\"pred\\\": []}\\n for epoch in range(args.epochs):\\n start = time.time()\\n train_loss = train_epoch(\\n model, loss_criterion, optimizer, train_dataloader, device\\n )\\n val_loss, epoch_stats = val_epoch(\\n model, loss_criterion, val_dataloader, device\\n )\\n train_losses.append(train_loss)\\n val_losses.append(val_loss)\\n stats[\\\"true\\\"] += epoch_stats[\\\"true\\\"]\\n stats[\\\"pred\\\"] += epoch_stats[\\\"pred\\\"]\\n duration = (time.time() - start) / 60 # epoch duration in minutes\\n print_training_update(\\n epoch, duration, lr_index, (train_loss, val_loss), epoch_stats\\n )\\n recent_10_percent = int(0.9*args.epochs)\\n return (\\n model,\\n np.mean(train_losses[recent_10_percent:]),\\n np.mean(val_losses[recent_10_percent:]),\\n training_metric(stats)\\n )\",\n \"def train(self):\\n self.mode = \\\"train\\\"\\n self.online_net.train()\",\n \"def train(self):\\n self.mode = \\\"train\\\"\\n self.online_net.train()\",\n \"def train(self, dataset, model_dir):\\n raise NotImplementedError\",\n \"def train(epochs, batch_size, lr, verbose):\\n # autograd globally off\\n torch.set_grad_enabled(False)\\n # generate training and testing datasets\\n train_data, train_label = generate_data()\\n test_data, test_label = generate_data()\\n # normalize data be centered at 0\\n train_data, test_data = normalize(train_data, test_data)\\n\\n if verbose:\\n print(\\\"--- Dataset ---\\\")\\n print(\\\"Train X: \\\", train_data.size(), \\\" | Train y: \\\", train_label.size())\\n print(\\\" Test X: \\\", test_data.size(), \\\" | Test y: \\\", test_label.size())\\n\\n layers =[]\\n # input layer (2 input units)\\n linear1 = Linear(2, 25, bias= True, weight_init=xavier_uniform)\\n\\n # 3 hidden layers (each 25 units)\\n linear2 = Linear(25, 25, bias= True, weight_init=xavier_uniform)\\n linear3 = Linear(25, 25, bias= True, weight_init=xavier_uniform)\\n linear4 = Linear(25, 25, bias= True, weight_init=xavier_uniform)\\n\\n # output layer (2 output units)\\n linear5 = Linear(25, 2, bias= True, weight_init=xavier_uniform)\\n\\n\\n layers.append(linear1)\\n layers.append(Relu())\\n layers.append(linear2)\\n layers.append(Relu())\\n layers.append(linear3)\\n layers.append(Relu())\\n layers.append(linear4)\\n layers.append(Tanh())\\n layers.append(linear5)\\n\\n model = Sequential(layers)\\n if verbose: print(\\\"Number of model parameters: {}\\\".format(sum([len(p) for p in model.param()])))\\n\\n criterion = MSE()\\n optimizer = SGD(model, lr=lr)\\n\\n train_losses, test_losses = [], []\\n train_accuracies, test_accuracies = [], []\\n train_errors, test_errors = [], []\\n\\n if verbose: print(\\\"--- Training ---\\\")\\n for epoch in range(1, epochs+1):\\n if verbose:print(\\\"Epoch: {}\\\".format(epoch))\\n\\n # TRAINING\\n for batch_idx in range(0, train_data.size(0), batch_size):\\n # axis 0, start from batch_idx until batch_idx+batch_size\\n output = model.forward(train_data.narrow(0, batch_idx, batch_size))\\n\\n # Calculate loss\\n loss = criterion.forward(output, train_label.narrow(0, batch_idx, batch_size))\\n train_losses.append(loss)\\n if verbose: print(\\\"Train Loss: {:.2f}\\\".format(loss.item()))\\n\\n # put to zero weights and bias\\n optimizer.zero_grad()\\n\\n ## Backpropagation\\n # Calculate grad of loss\\n loss_grad = criterion.backward()\\n\\n # Grad of the model\\n model.backward(loss_grad)\\n\\n # Update parameters\\n optimizer.step()\\n\\n train_prediction = model.forward(train_data)\\n acc = accuracy(train_prediction, train_label)\\n train_accuracies.append(acc)\\n train_errors.append(1-acc)\\n if verbose: print(\\\"Train Accuracy: {:.2f}\\\".format(acc.item()))\\n\\n # EVALUATION\\n for batch_idx in range(0, test_data.size(0), batch_size):\\n # axis 0, start from batch_idx until batch_idx+batch_size\\n output = model.forward(test_data.narrow(0, batch_idx, batch_size))\\n\\n # Calculate loss\\n loss = criterion.forward(output, test_label.narrow(0, batch_idx, batch_size))\\n test_losses.append(loss)\\n if verbose: print(\\\"Test Loss: {:.2f}\\\".format(loss.item()))\\n\\n test_prediction = model.forward(test_data)\\n acc = accuracy(test_prediction, test_label)\\n test_accuracies.append(acc) \\n test_errors.append(1-acc)\\n if verbose: print(\\\"Test Accuracy: {:.2f}\\\".format(acc.item()))\\n\\n return train_losses, test_losses, train_accuracies, test_accuracies, train_errors, test_errors\",\n \"def train(settings=None):\\n if not settings:\\n settings = Settings()\\n train_transform = torchvision.transforms.Compose([transforms.RandomlySelectPatchAndRescale(),\\n transforms.RandomHorizontalFlip(),\\n transforms.NegativeOneToOneNormalizeImage(),\\n transforms.NumpyArraysToTorchTensors()])\\n validation_transform = torchvision.transforms.Compose([transforms.RandomlySelectPatchAndRescale(),\\n transforms.NegativeOneToOneNormalizeImage(),\\n transforms.NumpyArraysToTorchTensors()])\\n\\n train_dataset = CrowdDatasetWithUnlabeled(settings.train_dataset_path, 'train', transform=train_transform)\\n train_dataset_loader = torch.utils.data.DataLoader(train_dataset, batch_size=settings.batch_size, shuffle=True,\\n num_workers=settings.number_of_data_loader_workers)\\n validation_dataset = CrowdDataset(settings.validation_dataset_path, 'validation', transform=validation_transform)\\n validation_dataset_loader = torch.utils.data.DataLoader(validation_dataset, batch_size=settings.batch_size,\\n shuffle=False,\\n num_workers=settings.number_of_data_loader_workers)\\n\\n gan = GAN()\\n gpu(gan)\\n D = gan.D\\n G = gan.G\\n discriminator_optimizer = Adam(D.parameters())\\n generator_optimizer = Adam(G.parameters())\\n\\n step = 0\\n epoch = 0\\n\\n if settings.load_model_path:\\n d_model_state_dict, d_optimizer_state_dict, epoch, step = load_trainer(prefix='discriminator',\\n settings=settings)\\n D.load_state_dict(d_model_state_dict)\\n discriminator_optimizer.load_state_dict(d_optimizer_state_dict)\\n discriminator_optimizer.param_groups[0].update({'lr': settings.learning_rate, 'weight_decay': settings.weight_decay})\\n if settings.load_model_path:\\n g_model_state_dict, g_optimizer_state_dict, _, _ = load_trainer(prefix='generator',\\n settings=settings)\\n G.load_state_dict(g_model_state_dict)\\n generator_optimizer.load_state_dict(g_optimizer_state_dict)\\n generator_optimizer.param_groups[0].update({'lr': settings.learning_rate})\\n\\n running_scalars = defaultdict(float)\\n validation_running_scalars = defaultdict(float)\\n running_example_count = 0\\n datetime_string = datetime.datetime.now().strftime(\\\"y%Ym%md%dh%Hm%Ms%S\\\")\\n trial_directory = os.path.join(settings.log_directory, settings.trial_name + ' ' + datetime_string)\\n os.makedirs(trial_directory, exist_ok=True)\\n summary_writer = SummaryWriter(os.path.join(trial_directory, 'train'))\\n validation_summary_writer = SummaryWriter(os.path.join(trial_directory, 'validation'))\\n print('Starting training...')\\n step_time_start = datetime.datetime.now()\\n while epoch < settings.number_of_epochs:\\n for examples, unlabeled_examples in train_dataset_loader:\\n unlabeled_images = unlabeled_examples[0]\\n # Real image discriminator processing.\\n discriminator_optimizer.zero_grad()\\n images, labels, _ = examples\\n images, labels = Variable(gpu(images)), Variable(gpu(labels))\\n current_batch_size = images.data.shape[0]\\n predicted_labels, predicted_counts = D(images)\\n real_feature_layer = D.feature_layer\\n density_loss = torch.abs(predicted_labels - labels).pow(settings.loss_order).sum(1).sum(1).mean()\\n count_loss = torch.abs(predicted_counts - labels.sum(1).sum(1)).pow(settings.loss_order).mean()\\n loss = count_loss + (density_loss * 10)\\n loss.backward()\\n running_scalars['Labeled/Loss'] += loss.data[0]\\n running_scalars['Labeled/Count Loss'] += count_loss.data[0]\\n running_scalars['Labeled/Density Loss'] += density_loss.data[0]\\n running_scalars['Labeled/Count ME'] += (predicted_counts - labels.sum(1).sum(1)).mean().data[0]\\n # Unlabeled.\\n _ = D(gpu(images))\\n labeled_feature_layer = D.feature_layer\\n _ = D(gpu(Variable(unlabeled_images)))\\n unlabeled_feature_layer = D.feature_layer\\n unlabeled_loss = feature_distance_loss(unlabeled_feature_layer, labeled_feature_layer,\\n scale=False) * settings.unlabeled_loss_multiplier\\n unlabeled_loss.backward()\\n # Fake.\\n _ = D(gpu(Variable(unlabeled_images)))\\n unlabeled_feature_layer = D.feature_layer\\n z = torch.from_numpy(MixtureModel([norm(-settings.mean_offset, 1), norm(settings.mean_offset, 1)]).rvs(\\n size=[current_batch_size, 100]).astype(np.float32))\\n # z = torch.randn(settings.batch_size, noise_size)\\n fake_examples = G(gpu(Variable(z)))\\n _ = D(fake_examples.detach())\\n fake_feature_layer = D.feature_layer\\n fake_loss = feature_distance_loss(unlabeled_feature_layer, fake_feature_layer,\\n order=1).neg() * settings.fake_loss_multiplier\\n fake_loss.backward()\\n # Feature norm loss.\\n _ = D(gpu(Variable(unlabeled_images)))\\n unlabeled_feature_layer = D.feature_layer\\n feature_norm_loss = (unlabeled_feature_layer.norm(dim=1).mean() - 1).pow(2)\\n feature_norm_loss.backward()\\n # Gradient penalty.\\n if settings.gradient_penalty_on:\\n alpha = gpu(Variable(torch.rand(2)))\\n alpha = alpha / alpha.sum(0)\\n interpolates = (alpha[0] * gpu(Variable(unlabeled_images, requires_grad=True)) +\\n alpha[1] * gpu(Variable(fake_examples.detach().data, requires_grad=True)))\\n _ = D(interpolates)\\n interpolates_predictions = D.feature_layer\\n gradients = torch.autograd.grad(outputs=interpolates_predictions, inputs=interpolates,\\n grad_outputs=gpu(torch.ones(interpolates_predictions.size())),\\n create_graph=True, only_inputs=True)[0]\\n gradient_penalty = ((gradients.norm(dim=1) - 1) ** 2).mean() * settings.gradient_penalty_multiplier\\n gradient_penalty.backward()\\n # Discriminator update.\\n discriminator_optimizer.step()\\n # Generator.\\n if step % 1 == 0:\\n generator_optimizer.zero_grad()\\n _ = D(gpu(Variable(unlabeled_images)))\\n unlabeled_feature_layer = D.feature_layer.detach()\\n z = torch.randn(current_batch_size, 100)\\n fake_examples = G(gpu(Variable(z)))\\n _ = D(fake_examples)\\n fake_feature_layer = D.feature_layer\\n generator_loss = feature_distance_loss(unlabeled_feature_layer, fake_feature_layer)\\n generator_loss.backward()\\n generator_optimizer.step()\\n\\n running_example_count += images.size()[0]\\n if step % settings.summary_step_period == 0 and step != 0:\\n comparison_image = viewer.create_crowd_images_comparison_grid(cpu(images), cpu(labels),\\n cpu(predicted_labels))\\n summary_writer.add_image('Comparison', comparison_image, global_step=step)\\n fake_images_image = torchvision.utils.make_grid(fake_examples.data[:9], nrow=3)\\n summary_writer.add_image('Fake', fake_images_image, global_step=step)\\n print('\\\\rStep {}, {}...'.format(step, datetime.datetime.now() - step_time_start), end='')\\n step_time_start = datetime.datetime.now()\\n for name, running_scalar in running_scalars.items():\\n mean_scalar = running_scalar / running_example_count\\n summary_writer.add_scalar(name, mean_scalar, global_step=step)\\n running_scalars[name] = 0\\n running_example_count = 0\\n for validation_examples in validation_dataset_loader:\\n images, labels, _ = validation_examples\\n images, labels = Variable(gpu(images)), Variable(gpu(labels))\\n predicted_labels, predicted_counts = D(images)\\n density_loss = torch.abs(predicted_labels - labels).pow(settings.loss_order).sum(1).sum(1).mean()\\n count_loss = torch.abs(predicted_counts - labels.sum(1).sum(1)).pow(settings.loss_order).mean()\\n count_mae = torch.abs(predicted_counts - labels.sum(1).sum(1)).mean()\\n count_me = (predicted_counts - labels.sum(1).sum(1)).mean()\\n validation_running_scalars['Labeled/Density Loss'] += density_loss.data[0]\\n validation_running_scalars['Labeled/Count Loss'] += count_loss.data[0]\\n validation_running_scalars['Test/Count MAE'] += count_mae.data[0]\\n validation_running_scalars['Labeled/Count ME'] += count_me.data[0]\\n comparison_image = viewer.create_crowd_images_comparison_grid(cpu(images), cpu(labels),\\n cpu(predicted_labels))\\n validation_summary_writer.add_image('Comparison', comparison_image, global_step=step)\\n for name, running_scalar in validation_running_scalars.items():\\n mean_scalar = running_scalar / len(validation_dataset)\\n validation_summary_writer.add_scalar(name, mean_scalar, global_step=step)\\n validation_running_scalars[name] = 0\\n step += 1\\n epoch += 1\\n if epoch != 0 and epoch % settings.save_epoch_period == 0:\\n save_trainer(trial_directory, D, discriminator_optimizer, epoch, step, prefix='discriminator')\\n save_trainer(trial_directory, G, generator_optimizer, epoch, step, prefix='generator')\\n save_trainer(trial_directory, D, discriminator_optimizer, epoch, step, prefix='discriminator')\\n save_trainer(trial_directory, G, generator_optimizer, epoch, step, prefix='generator')\\n print('Finished Training')\\n return trial_directory\",\n \"def train(self):\\n # self.recognizer.train()\\n self.detector.train()\\n self.shared_conv.train()\",\n \"def train(train_dataset: torch.utils.data.Dataset, test_dataset: torch.utils.data.Dataset,\\n training_config: dict = train_config, global_config: dict = global_config):\\n\\n for path in global_config.values():\\n create_dirs(path)\\n\\n # wrap datasets with Dataloader classes\\n train_loader = torch.utils.data.DataLoader(train_dataset,\\n **training_config[\\\"DATA_LOADER_CONFIG\\\"])\\n test_loader = torch.utils.data.DataLoader(test_dataset,\\n **training_config[\\\"DATA_LOADER_CONFIG\\\"])\\n\\n # model name & paths\\n name = \\\"_\\\".join([train_config[\\\"DATE\\\"], train_config[\\\"SESSION_NAME\\\"]])\\n modelpath = os.path.join(global_config[\\\"WEIGHT_DIR\\\"], name)\\n\\n # instantiate model\\n model = training_config[\\\"MODEL\\\"](**training_config[\\\"MODEL_CONFIG\\\"])\\n\\n optimizer = training_config[\\\"OPTIMIZER\\\"](model.parameters(),\\n **training_config[\\\"OPTIMIZER_CONFIG\\\"])\\n\\n # set up ignite engine\\n training_config[\\\"METRICS\\\"].update({\\\"loss\\\" : Loss(training_config[\\\"LOSS\\\"])})\\n trainer = create_supervised_trainer(model=model, optimizer=optimizer,\\n loss_fn=training_config[\\\"LOSS\\\"],\\n device=training_config[\\\"DEVICE\\\"])\\n evaluator = create_supervised_evaluator(model,\\n metrics=training_config[\\\"METRICS\\\"],\\n device=training_config[\\\"DEVICE\\\"])\\n\\n\\n # tensorboardX setup\\n log_dir = os.path.join(global_config[\\\"LOG_DIR\\\"], \\\"tensorboardx\\\", name)\\n create_dirs(log_dir)\\n writer = SummaryWriter(logdir=log_dir)\\n\\n # log using the logging tool\\n logger = log.Log(training_config, run_name=train_config['SESSION_NAME'])\\n\\n @trainer.on(Events.ITERATION_COMPLETED)\\n def log_training(engine):\\n iteration = (engine.state.iteration - 1) % len(train_loader) + 1\\n writer.add_scalar(\\\"training/loss\\\", engine.state.output, engine.state.iteration)\\n if iteration % 4 == 0:\\n print(\\\"\\\\repoch[{}] iteration[{}/{}] loss: {:.2f} \\\".format(engine.state.epoch,\\n iteration, len(train_loader),\\n engine.state.output), end=\\\"\\\")\\n\\n # generic evaluation function\\n def evaluate(engine, loader):\\n evaluator.run(loader)\\n metrics = evaluator.state.metrics\\n return metrics\\n\\n # training data metrics\\n @trainer.on(Events.EPOCH_COMPLETED)\\n def log_training_results(engine):\\n print(\\\"\\\\ntraining results - epoch {}\\\".format(engine.state.epoch))\\n metrics = evaluate(engine, train_loader)\\n print(metrics)\\n for key, value in metrics.items():\\n logger.log_metric(key, value)\\n writer.add_scalar(\\\"training/avg_{}\\\".format(key), value, engine.state.epoch)\\n\\n # test data metrics\\n @trainer.on(Events.EPOCH_COMPLETED)\\n def log_validation_results(engine):\\n print(\\\"test results - epoch {}\\\".format(engine.state.epoch))\\n metrics = evaluate(engine, test_loader)\\n print(metrics)\\n for key, value in metrics.items():\\n writer.add_scalar(\\\"validation/avg_{}\\\".format(key), value, engine.state.epoch)\\n\\n # model checkpointing\\n @trainer.on(Events.EPOCH_COMPLETED)\\n def model_checkpoint(engine):\\n torch.save(model.state_dict(), modelpath + \\\".pth\\\")\\n print(\\\"Checkpoint saved to {}\\\".format(modelpath + \\\".pth\\\"))\\n\\n # training iteration\\n try:\\n trainer.run(train_loader, max_epochs=training_config[\\\"EPOCHS\\\"])\\n except KeyboardInterrupt:\\n torch.save(model.state_dict(), modelpath + \\\".pth\\\")\\n print(\\\"Model saved to {}\\\".format(modelpath + \\\".pth\\\"))\\n raise KeyboardInterrupt\\n\\n # write weights\\n torch.save(model.state_dict(), modelpath + \\\".pth\\\")\\n\\n # write csv log file\\n log_content = training_config.copy()\\n evaluator.run(test_loader)\\n log_content[\\\"VAL_METRICS\\\"] = evaluator.state.metrics\\n log_path = os.path.join(global_config[\\\"LOG_DIR\\\"], training_config[\\\"LOGFILE\\\"])\\n write_log(log_path, log_content)\\n\\n logger.end_run()\\n \\n return evaluator.state.metrics[\\\"training/avg_loss\\\"]\",\n \"def train(self):\\n start_time = time()\\n self.model.train()\\n\\n for step, sample in enumerate(self.train_loader):\\n self.optimizer.zero_grad()\\n\\n x, _, _ = sample\\n x = x.to(self.device)\\n\\n y_pred = self.model.forward(x)\\n loss = nn.MSELoss()(y_pred, x)\\n loss.backward()\\n self.train_losses.append(loss.item())\\n\\n self.optimizer.step(None)\\n\\n # print an incredible progress bar\\n print(f'\\\\r{self.progress_bar} │ Loss: {np.mean(self.train_losses):.6f}', end='')\\n self.progress_bar.inc()\\n\\n # log average loss of this epoch\\n mean_epoch_loss = np.mean(self.train_losses)\\n self.sw.add_scalar(tag='train_loss', scalar_value=mean_epoch_loss, global_step=self.epoch)\\n self.train_losses = []\\n\\n # log epoch duration\\n print(f' │ T: {time() - start_time:.2f} s')\",\n \"def training(self):\\n \\n best_valid_loss = np.inf\\n c = 0\\n \\n self.train_loader, self.test_loader = self.get_train_test_loaders()\\n \\n print('Training the {} model with the following architecture:'.format(self.model_name))\\n print(summary(self.model, (3, self.image_width, self.image_height)))\\n print('*'*100)\\n print('Starting the training...')\\n print('*'*100)\\n \\n # Create the model save dir if it already doesn't exist\\n if not os.path.exists(self.model_save_dir):\\n os.makedirs(self.model_save_dir)\\n \\n for epoch in range(self.n_epochs):\\n\\n print(f'Epoch: {epoch+1:02}')\\n\\n start_time = time.time()\\n\\n train_loss = self.train(self.train_loader)\\n valid_loss = self.evaluate(self.test_loader)\\n\\n epoch_mins, epoch_secs = self.epoch_time(start_time, time.time())\\n\\n c+=1\\n if valid_loss < best_valid_loss:\\n best_valid_loss = valid_loss\\n torch.save(self.model.state_dict(), os.path.join(self.model_save_dir, '{}_trained.pt'.format(self.model_name)))\\n c=0\\n\\n if c>4:\\n #decrease lr if loss does not decrease after 5 steps\\n self.scheduler.step()\\n c=0\\n\\n print(f'Time: {epoch_mins}m {epoch_secs}s') \\n print(f'Train Loss: {train_loss:.3f}')\\n print(f'Val Loss: {valid_loss:.3f}')\\n print('-'*60)\\n print('The best validation loss is', best_valid_loss)\\n print('*'*100)\",\n \"def set_train(self):\\n self.model.train()\",\n \"def train(self):\\r\\n print(\\\"Starting training now\\\")\\r\\n cuda = True if torch.cuda.is_available() else False\\r\\n if cuda:\\r\\n self.model.cuda()\\r\\n\\r\\n # Construct optimizer after the model moved to GPU\\r\\n self.optm = self.make_optimizer()\\r\\n self.lr_scheduler = self.make_lr_scheduler(self.optm)\\r\\n\\r\\n dim_x = self.flags.dim_x\\r\\n dim_y = self.flags.dim_y\\r\\n dim_z = self.flags.dim_z\\r\\n dim_tot = self.flags.dim_tot\\r\\n\\r\\n # Time keeping\\r\\n tk = time_keeper(time_keeping_file=os.path.join(self.ckpt_dir, 'training time.txt'))\\r\\n\\r\\n for epoch in range(self.flags.train_step):\\r\\n # Set to Training Mode\\r\\n train_loss = 0\\r\\n self.model.train()\\r\\n # If MMD on x-space is present from the start, the model can get stuck.\\r\\n # Instead, ramp it up exponetially.\\r\\n loss_factor = min(1., 2. * 0.002 ** (1. - (float(epoch) / self.flags.train_step)))\\r\\n\\r\\n for j, (x, y) in enumerate(self.train_loader):\\r\\n batch_size = len(x)\\r\\n\\r\\n ######################\\r\\n # Preparing the data #\\r\\n ######################\\r\\n # Pad the x, y with zero_noise\\r\\n y_clean = y.clone() # keep a copy of y for backward\\r\\n x_pad = self.flags.zeros_noise_scale * torch.randn(batch_size,\\r\\n dim_tot - dim_x)\\r\\n y_pad = self.flags.zeros_noise_scale * torch.randn(batch_size,\\r\\n dim_tot - dim_y - dim_z)\\r\\n z = torch.randn(batch_size, dim_z)\\r\\n if cuda:\\r\\n x = x.cuda() # Put data onto GPU\\r\\n y = y.cuda() # Put data onto GPU\\r\\n x_pad = x_pad.cuda()\\r\\n y_pad = y_pad.cuda()\\r\\n y_clean = y_clean.cuda()\\r\\n z = z.cuda()\\r\\n\\r\\n # Concate the x and y with pads and add y with small purtubation\\r\\n y += self.flags.y_noise_scale * torch.randn(batch_size, dim_y, device=device)\\r\\n\\r\\n x, y = torch.cat((x, x_pad), dim=1), torch.cat((z, y_pad, y), dim=1)\\r\\n\\r\\n ################\\r\\n # Forward step #\\r\\n ################\\r\\n self.optm.zero_grad() # Zero the gradient first\\r\\n ypred = self.model(x) # Get the Ypred\\r\\n\\r\\n\\r\\n # Do the MSE loss for reconstruction, Doesn't compare z part (only pad and y itself)\\r\\n MSE_loss_y = self.make_loss(logit=ypred[:, dim_z:], labels=y[:, dim_z:])\\r\\n\\r\\n # Use the maximum likelihood method\\r\\n log_det = self.model.log_jacobian(x=x)\\r\\n #print(\\\"The log determinant is\\\", log_det)\\r\\n Forward_loss = 0.5 * (MSE_loss_y / self.flags.lambda_mse + torch.mean(torch.pow(z,2))) - torch.mean(log_det)\\r\\n Forward_loss.backward()\\r\\n\\r\\n ######################\\r\\n # Gradient Clipping #\\r\\n ######################\\r\\n for parameter in self.model.parameters():\\r\\n parameter.grad.data.clamp_(-self.flags.grad_clamp, self.flags.grad_clamp)\\r\\n\\r\\n #########################\\r\\n # Descent your gradient #\\r\\n #########################\\r\\n self.optm.step() # Move one step the optimizer\\r\\n\\r\\n # MLE training\\r\\n train_loss += Forward_loss \\r\\n\\r\\n # Calculate the avg loss of training\\r\\n train_avg_loss = train_loss.cpu().data.numpy() / (j + 1)\\r\\n\\r\\n if epoch % self.flags.eval_step == 0: # For eval steps, do the evaluations and tensor board\\r\\n # Record the training loss to the tensorboard\\r\\n self.log.add_scalar('Loss/total_train', train_avg_loss, epoch)\\r\\n self.log.add_scalar('Loss/MSE_y_train', MSE_loss_y, epoch)\\r\\n\\r\\n # Set to Evaluation Mode\\r\\n self.model.eval()\\r\\n print(\\\"Doing Evaluation on the model now\\\")\\r\\n\\r\\n test_loss = 0\\r\\n for j, (x, y) in enumerate(self.test_loader): # Loop through the eval set\\r\\n batch_size = len(x)\\r\\n\\r\\n ######################\\r\\n # Preparing the data #\\r\\n ######################\\r\\n # Pad the x, y with zero_noise\\r\\n y_clean = y.clone() # keep a copy of y for backward\\r\\n x_pad = self.flags.zeros_noise_scale * torch.randn(batch_size,\\r\\n dim_tot - dim_x)\\r\\n y_pad = self.flags.zeros_noise_scale * torch.randn(batch_size,\\r\\n dim_tot - dim_y - dim_z)\\r\\n z = torch.randn(batch_size, dim_z)\\r\\n if cuda:\\r\\n x = x.cuda() # Put data onto GPU\\r\\n y = y.cuda() # Put data onto GPU\\r\\n x_pad = x_pad.cuda()\\r\\n y_pad = y_pad.cuda()\\r\\n y_clean = y_clean.cuda()\\r\\n z = z.cuda()\\r\\n\\r\\n # Concate the x and y with pads and add y with small purtubation\\r\\n y += self.flags.y_noise_scale * torch.randn(batch_size, dim_y, device=device)\\r\\n\\r\\n x, y = torch.cat((x, x_pad), dim=1), torch.cat((z, y_pad, y), dim=1)\\r\\n\\r\\n ################\\r\\n # Forward step #\\r\\n ################\\r\\n self.optm.zero_grad() # Zero the gradient first\\r\\n ypred = self.model(x) # Get the Ypred\\r\\n # Do the MSE loss for reconstruction, Doesn't compare z part (only pad and y itself)\\r\\n MSE_loss_y = self.make_loss(logit=ypred[:, dim_z:], labels=y[:, dim_z:])\\r\\n\\r\\n log_det = self.model.log_jacobian(x=x)\\r\\n #print(\\\"The log determinant is\\\", log_det)\\r\\n Forward_loss = 0.5 * (MSE_loss_y / self.flags.lambda_mse + torch.mean(torch.pow(z,2))) - torch.mean(log_det)\\r\\n test_loss += Forward_loss\\r\\n # Aggregate the other loss (in np form)\\r\\n\\r\\n # Record the testing loss to the tensorboard\\r\\n test_avg_loss = test_loss.cpu().data.numpy() / (j+1)\\r\\n\\r\\n self.log.add_scalar('Loss/total_test', test_avg_loss, epoch)\\r\\n self.log.add_scalar('Loss/MSE_y_test', MSE_loss_y, epoch)\\r\\n\\r\\n print(\\\"This is Epoch %d, training loss %.5f, validation loss %.5f\\\" \\\\\\r\\n % (epoch, train_avg_loss, test_avg_loss ))\\r\\n\\r\\n # Model improving, save the model down\\r\\n if test_avg_loss < self.best_validation_loss:\\r\\n self.best_validation_loss = train_avg_loss\\r\\n self.save()\\r\\n print(\\\"Saving the model down...\\\")\\r\\n\\r\\n if self.best_validation_loss < self.flags.stop_threshold:\\r\\n print(\\\"Training finished EARLIER at epoch %d, reaching loss of %.5f\\\" %\\\\\\r\\n (epoch, self.best_validation_loss))\\r\\n break\\r\\n\\r\\n # Learning rate decay upon plateau\\r\\n self.lr_scheduler.step(train_avg_loss)\\r\\n tk.record(1) # Record the total time of the training peroid\\r\",\n \"def train(args):\\n\\n log_header('Training network')\\n\\n train_retriever(args)\",\n \"def train(model, train_sampler, train_loader, test_sampler, test_loader,\\n use_cuda, epochs, loss_func, optimizer_name, lr,\\n batch_log_interval, hvd, smlb_out):\\n console = smlb_out.log.console\\n device = smlb_out.log.device\\n\\n # training history (validation only)\\n with console.subproc('Creating training history'):\\n loss_val_hist = torch.zeros(epochs, dtype=torch.float32)\\n acc_val_hist = torch.zeros(epochs, dtype=torch.float32)\\n\\n # send to device\\n if use_cuda:\\n with console.subproc('Sending model and history to device'):\\n model.cuda()\\n loss_val_hist = loss_val_hist.cuda()\\n acc_val_hist = acc_val_hist.cuda()\\n\\n # loss\\n with console.subproc('Creating loss function'):\\n loss_func = eval(f'nn.{loss_func}()')\\n console.message(f'Loss function: {loss_func}')\\n\\n # optimizer\\n with console.subproc('Creating optimizer'):\\n console.message(f'Learning rate specified: {lr}')\\n console.message(f'Reduction operation: {\\\"hvd.Average\\\"}')\\n console.message(f'Learning rate will be scaled by a factor of '\\n f'{hvd.size()} (hvd.size())')\\n optimizer = eval(f'torch.optim.{optimizer_name}(model.parameters(), '\\n f'lr={lr * hvd.size()})')\\n console.message(f'Optimizer: {optimizer}')\\n # Horovod: wrap optimizer with DistributedOptimizer\\n optimizer = hvd.DistributedOptimizer(optimizer, op=hvd.Average)\\n\\n # Horovod: broadcast model and optimizer\\n with console.subproc('Broadcasting model and optimizer'):\\n hvd.broadcast_parameters(model.state_dict(), root_rank=0)\\n hvd.broadcast_optimizer_state(optimizer, root_rank=0)\\n\\n # ----------------------\\n # Epoch loop starts here\\n # ----------------------\\n console.begin('**** EPOCH LOOP ****')\\n device.begin('**** EPOCH LOOP ****')\\n for epoch in range(epochs):\\n # only log on device within epoch loop\\n device.begin(f'Epoch: {epoch}')\\n\\n # -------------------\\n # Training batch loop\\n # -------------------\\n device.begin('Training batch loop')\\n # stamp train epoch in system monitor\\n smlb_out.system.stamp_event(f'epoch {epoch}: train')\\n # enter train mode\\n model.train()\\n # Horovod: set epoch to sampler for shuffling\\n train_sampler.set_epoch(epoch)\\n # batch loop\\n for batch_idx, (batch_x, batch_y) in enumerate(train_loader):\\n if use_cuda:\\n batch_x, batch_y = batch_x.cuda(), batch_y.cuda()\\n # forward, loss, acc\\n pred_y = model(batch_x)\\n loss = loss_func(pred_y, batch_y)\\n # backward\\n optimizer.zero_grad()\\n loss.backward()\\n optimizer.step()\\n if batch_idx % batch_log_interval == 0:\\n # accuracy on batch\\n with torch.no_grad():\\n acc = compute_acc(pred_y, batch_y)\\n # Horovod: use train_sampler to determine the number of\\n # samples in this worker's partition\\n device.message(\\n '[{:5d}/{:5d} ({:3.0f}%)] loss={:f}, acc={:f}, '\\n 'elapsed={:f} sec'.format(\\n batch_idx * len(batch_x), len(train_sampler),\\n 100 * batch_idx / len(train_loader), loss, acc,\\n device.elapsed_shallowest))\\n device.ended('Training batch loop')\\n\\n # ----------------------\\n # Validation on test set\\n # ----------------------\\n device.begin('Validating on test set')\\n # stamp validate epoch in system monitor\\n smlb_out.system.stamp_event(f'epoch {epoch}: validate')\\n # enter eval mode\\n model.eval()\\n # accumulate loss and acc\\n loss_val = torch.zeros((1,), dtype=torch.float32)\\n acc_val = torch.zeros((1,), dtype=torch.float32)\\n if use_cuda:\\n loss_val, acc_val = loss_val.cuda(), acc_val.cuda()\\n for batch_x, batch_y in test_loader:\\n if use_cuda:\\n batch_x, batch_y = batch_x.cuda(), batch_y.cuda()\\n # forward, loss, acc\\n with torch.no_grad():\\n pred_y = model(batch_x)\\n loss_val += loss_func(pred_y, batch_y)\\n acc_val += compute_acc(pred_y, batch_y) * len(pred_y)\\n if use_cuda:\\n loss_val, acc_val = loss_val.cpu(), acc_val.cpu()\\n loss_val /= len(test_sampler)\\n acc_val /= len(test_sampler)\\n # average metrics across ranks and save to history\\n with device.subproc('Averaging metrics across ranks (allreduce)'):\\n loss_val_hist[epoch] = metric_average(loss_val, 'avg_loss', hvd)\\n acc_val_hist[epoch] = metric_average(acc_val, 'avg_accuracy', hvd)\\n # log device-wise and average metrics\\n device.message('Metrics on rank: loss_val={:f}, acc_val={:f}'\\n .format(loss_val.item(), acc_val.item()))\\n device.message('Average metrics: loss_val={:f}, acc_val={:f}'\\n .format(loss_val_hist[epoch], acc_val_hist[epoch]))\\n device.ended('Validating on test set')\\n\\n # only show average on console\\n console.message(f'Epoch {epoch:2d}: '\\n f'loss_val={loss_val_hist[epoch]:f}, '\\n f'acc_val={acc_val_hist[epoch]:f}, '\\n f'elapsed={device.elapsed_shallowest:f} sec')\\n device.ended(f'Epoch: {epoch}')\\n device.ended('**** EPOCH LOOP ****')\\n console.ended('**** EPOCH LOOP ****')\\n\\n # send model and data back to CPU\\n if use_cuda:\\n with console.subproc('Sending model and history back to cpu'):\\n model.cpu()\\n loss_val_hist = loss_val_hist.cpu()\\n acc_val_hist = acc_val_hist.cpu()\\n\\n # return history\\n return {'loss_val': loss_val_hist.numpy().tolist(),\\n 'acc_val_hist': acc_val_hist.numpy().tolist()}\",\n \"def train(self, batch):\\n pass\",\n \"def __init__(self, options, path):\\n print('Prepare the network and data.')\\n self._options = options\\n self._path = path\\n self._epoch = 0\\n\\n self.device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n # Network.\\n network = SCNN()\\n #weight_init(network)\\n network = network.to(self.device)\\n # self._net = network.cuda()\\n self._net = network\\n #self._net = torch.nn.DataParallel(network)\\n\\n logspaced_LR = np.logspace(-1, -4, self._options['epochs'])\\n # Load the model from disk.\\n checkpoints_list = os.listdir(self._path['model'])\\n if len(checkpoints_list) != 0:\\n self._net.load_state_dict(torch.load(\\n os.path.join(self._path['model'], '%s%s%s' % ('net_params', str(len(checkpoints_list) - 1), '.pkl'))))\\n self._epoch = len(checkpoints_list)\\n self._options['base_lr'] = logspaced_LR[len(checkpoints_list)]\\n # self._net.load_state_dict(torch.load(self._path['model']))\\n print(self._net)\\n # Criterion.\\n self._criterion = torch.nn.CrossEntropyLoss().cuda()\\n # Solver.\\n self._solver = torch.optim.SGD(\\n self._net.parameters(), lr=self._options['base_lr'],\\n momentum=0.9, weight_decay=self._options['weight_decay'])\\n # self._solver = torch.optim.Adam(\\n # self._net.parameters(), lr=self._options['base_lr'],\\n # weight_decay=self._options['weight_decay'])\\n lambda1 = lambda epoch: logspaced_LR[epoch]\\n self._scheduler = torch.optim.lr_scheduler.LambdaLR(self._solver, lr_lambda=lambda1)\\n\\n self.train_transforms = torchvision.transforms.Compose([\\n torchvision.transforms.Resize(size=256), # Let smaller edge match\\n torchvision.transforms.RandomHorizontalFlip(),\\n torchvision.transforms.RandomCrop(size=224),\\n torchvision.transforms.ToTensor(),\\n torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),\\n std=(0.229, 0.224, 0.225))\\n ])\\n self.test_transforms = torchvision.transforms.Compose([\\n torchvision.transforms.Resize(size=256),\\n torchvision.transforms.CenterCrop(size=224),\\n torchvision.transforms.ToTensor(),\\n torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),\\n std=(0.229, 0.224, 0.225))\\n ])\\n self.train_data = ImageDataset(csv_file=os.path.join(path['kadis'], 'train.txt'),\\n img_dir=os.path.join(path['kadis'], 'dist_imgs'),\\n transform=self.train_transforms,\\n test=False)\\n self._train_loader = torch.utils.data.DataLoader(\\n self.train_data, batch_size=self._options['batch_size'],\\n shuffle=True, num_workers=1, pin_memory=True)\\n self.test_data = ImageDataset(csv_file=os.path.join(path['kadis'], 'test.txt'),\\n img_dir=os.path.join(path['kadis'], 'dist_imgs'),\\n transform=self.test_transforms,\\n test=True)\\n self._test_loader = torch.utils.data.DataLoader(\\n self.test_data, batch_size=self._options['batch_size'],\\n shuffle=False, num_workers=1, pin_memory=True)\",\n \"def train(self, dataset):\\n print_interval = 100\\n train_accuracy = 0\\n train_loss = 0\\n num_examples_trained = 0\\n # Put model into training mode\\n self.model.train()\\n # Step through dataset using pytorch data loader, and a tqdm progress bar\\n for num, batch in enumerate(tqdm(dataset.loader)):\\n xs, ys = batch\\n batch_size = len(xs)\\n num_examples_trained += batch_size\\n iloss, iaccuracy = self.model(xs, ys)\\n loss = iloss\\n # Zero out the gradient\\n self.optimizer.zero_grad()\\n # Perform backwards pass (backprop)\\n loss.backward()\\n # Step the optimizer\\n self.optimizer.step()\\n train_loss += iloss.cpu().data.numpy().item() * batch_size\\n train_accuracy += iaccuracy.cpu().data.numpy().item() * batch_size\\n # Print results info\\n if num % print_interval == 0:\\n print(\\\"training loss: \\\" + str(iloss.cpu().data.numpy().item()))\\n print(\\\"training acc: \\\" + str(iaccuracy.cpu().data.numpy().item()))\\n train_accuracy = train_accuracy / num_examples_trained\\n train_loss = train_loss / num_examples_trained\\n # Increment current epoch number\\n self.cur_epoch += 1\\n # Return accuracy and loss\\n return train_accuracy, train_loss\",\n \"def train(self, epochs=1, loader=None, reset_optimizer=False):\\n if reset_optimizer:\\n self.optimizer = self.optimizer_fn(self.tr_model.parameters()) \\n \\n # start training the worker using local dataset\\n self.tr_model.train() \\n running_loss, samples = 0.0, 0\\n \\n # check if a dataloader was provided for training\\n loader = self.tr_loader if not loader else loader\\n end_training = False\\n itr = 0\\n for ep in range(epochs):\\n # train next epoch\\n for i, x, y in loader: \\n itr += 1 \\n x, y = x.to(device), y.to(device)\\n \\n self.optimizer.zero_grad()\\n \\n loss = nn.CrossEntropyLoss()(self.tr_model(x), y)\\n \\n running_loss += loss.item()*y.shape[0]\\n samples += y.shape[0]\\n \\n loss.backward()\\n self.optimizer.step() \\n\\n # check for early stopping criteria\\n if (self.early_stop != -1) and (itr % 5 == 0):\\n print(\\\"Checking early stop criteria...\\\")\\n accuracy = self.evaluate()[\\\"accuracy\\\"]\\n if accuracy >= self.early_stop:\\n print(\\\"Stopping criteria reached for worker {}...\\\".format(self.id))\\n end_training = True\\n break\\n # check if early stop criteria was reached\\n if end_training:\\n break\\n train_stats = {\\\"loss\\\" : running_loss / samples}\\n \\n # return training statistics\\n return train_stats\",\n \"def train_nlp(model: ContinualModel, dataset: ContinualDataset,\\n args: Namespace) -> None:\\n model.net.to(model.device)\\n results, results_mask_classes = [], []\\n \\n model_stash = create_stash(model, args, dataset)\\n \\n if args.csv_log:\\n csv_logger = CsvLogger(dataset.SETTING, dataset.NAME, model.NAME)\\n if args.tensorboard:\\n tb_logger = TensorboardLogger(args, dataset.SETTING, model_stash)\\n model_stash['tensorboard_name'] = tb_logger.get_name()\\n \\n dataset_copy = get_dataset(args)\\n \\n for t in range(dataset.N_TASKS):\\n model.net.train()\\n _, _ = dataset_copy.get_data_loaders()\\n if model.NAME != 'icarl' and model.NAME != 'pnn':\\n # for forward transfer calculation\\n random_results_class, random_results_task = evaluate_nlp(model, dataset_copy)\\n \\n print(file=sys.stderr)\\n # start time\\n start_time = time.time()\\n for t in range(dataset.N_TASKS):\\n model.net.train()\\n train_loader, test_loader = dataset.get_data_loaders()\\n if hasattr(model, 'begin_task'):\\n model.begin_task(dataset)\\n if t:\\n accs = evaluate_nlp(model, dataset, last=True)\\n results[t - 1] = results[t - 1] + accs[0]\\n if dataset.SETTING == 'class-il':\\n results_mask_classes[t - 1] = results_mask_classes[t - 1] + accs[1]\\n for epoch in range(args.n_epochs):\\n for i, data in enumerate(train_loader):\\n if hasattr(dataset.train_loader.dataset, 'logits'):\\n # todo: to add logits\\n pass\\n else:\\n xs, ys, x_token_idxs, x_token_masks, y_token_idxs, y_token_masks, y_idxs = data\\n \\n x_token_idxs = x_token_idxs.to(model.device)\\n x_token_masks = x_token_masks.to(model.device)\\n y_token_idxs = y_token_idxs.to(model.device)\\n y_token_masks = y_token_masks.to(model.device)\\n y_idxs = y_idxs.to(model.device)\\n task_id = torch.tensor(t, dtype=torch.int64, requires_grad=False)\\n task_id = task_id.to(model.device)\\n \\n if model.require_task_id:\\n loss = model.observe(inputs=x_token_idxs, inputs_mask=x_token_masks, labels=y_idxs,\\n labels_name=y_token_idxs, labels_mask=y_token_masks, task_labels=task_id)\\n else:\\n loss = model.observe(inputs=x_token_idxs, inputs_mask=x_token_masks, labels=y_idxs,\\n labels_name=y_token_idxs, labels_mask=y_token_masks)\\n \\n progress_bar(i, len(train_loader), epoch, t, loss)\\n \\n if args.tensorboard:\\n tb_logger.log_loss(loss, args, epoch, t, i)\\n \\n model_stash['batch_idx'] = i + 1\\n model_stash['epoch_idx'] = epoch + 1\\n model_stash['batch_idx'] = 0\\n \\n model_stash['task_idx'] = t + 1\\n model_stash['epoch_idx'] = 0\\n \\n if hasattr(model, 'end_task'):\\n model.end_task(dataset)\\n \\n # reduce the running freq\\n # if (t+1) % args.eval_freq == 0:\\n accs = evaluate_nlp(model, dataset)\\n results.append(accs[0])\\n results_mask_classes.append(accs[1])\\n \\n mean_acc = np.mean(accs, axis=1)\\n print_mean_accuracy(mean_acc, t + 1, dataset.SETTING)\\n \\n # prob_model\\n if args.prob_all_tasks:\\n if args.prob_type != \\\"\\\":\\n for prob_l in range(12):\\n prob_l += 1\\n if args.prob_type == \\\"proto\\\":\\n p_accs = prob_proto_nlp(model, dataset, prob_l=prob_l)\\n else:\\n p_accs = prob_final_nlp(model, dataset, prob_l=prob_l)\\n p_mean_acc = np.mean(p_accs, axis=1)\\n print(\\\"task {} prob_l {}: mean_acc {}, masked_mean_acc {}\\\".format(t + 1,\\n prob_l,\\n round(p_mean_acc[0], 2),\\n round(p_mean_acc[1], 2)))\\n if args.csv_log:\\n csv_logger.log_task_prob(p_mean_acc, t)\\n \\n model_stash['mean_accs'].append(mean_acc)\\n if args.csv_log:\\n csv_logger.log(mean_acc)\\n if args.tensorboard:\\n tb_logger.log_accuracy(np.array(accs), mean_acc, args, t)\\n \\n running_time = time.time() - start_time\\n \\n # prob_model\\n if args.prob_type != \\\"\\\" and not args.prob_all_tasks:\\n for prob_l in range(12):\\n prob_l += 1\\n if args.prob_type == \\\"proto\\\":\\n accs = prob_proto_nlp(model, dataset, prob_l=prob_l)\\n else:\\n accs = prob_final_nlp(model, dataset, prob_l=prob_l)\\n mean_acc = np.mean(accs, axis=1)\\n print(\\\"prob_l {}: mean_acc {}, masked_mean_acc {}\\\".format(prob_l, mean_acc[0], mean_acc[1]))\\n if args.csv_log:\\n csv_logger.log_prob(mean_acc)\\n \\n if args.csv_log:\\n csv_logger.add_bwt(results, results_mask_classes)\\n csv_logger.add_running_time(running_time)\\n csv_logger.add_forgetting(results, results_mask_classes)\\n if model.NAME != 'icarl' and model.NAME != 'pnn':\\n csv_logger.add_fwt(results, random_results_class,\\n results_mask_classes, random_results_task)\\n \\n if args.tensorboard:\\n tb_logger.close()\\n if args.csv_log:\\n csv_logger.write(vars(args))\",\n \"def _train(args): \\n\\n #device = 'cuda' if torch.cuda.is_available() else 'cpu'\\n device = 'cpu'\\n logger.info(\\\"Device Type: {}\\\".format(device))\\n\\n logger.info(\\\"Loading SUN360 dataset\\\")\\n transform = transforms.Compose(\\n [transforms.Resize((224,224)),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\\n target_transform = transforms.Compose([transforms.Resize((224,224)),\\n transforms.ToTensor()]) \\n\\n trainset = SUN360Dataset(\\\"imagedata.json\\\",transform = transform, target_transform = target_transform)\\n train_loader = DataLoader(trainset, batch_size=args.batch_size,\\n shuffle=True, num_workers=args.workers)\\n \\\"\\\"\\\"\\n testset = torchvision.datasets.CIFAR10(root=args.data_dir, train=False,\\n download=False, transform=transform)\\n test_loader = DataLoader(testset, batch_size=args.batch_size,\\n shuffle=False, num_workers=args.workers)\\n \\\"\\\"\\\" \\n\\n logger.info(\\\"Model loaded\\\")\\n model = EfficientNet.from_name('efficientnet-b0',conv_type='Equi')\\n\\n if torch.cuda.device_count() > 1:\\n logger.info(\\\"Gpu count: {}\\\".format(torch.cuda.device_count()))\\n model = nn.DataParallel(model)\\n\\n model = model.to(device)\\n\\n criterion = CELoss().to(device)\\n optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\\n\\n for epoch in range(0, args.epochs):\\n running_loss = 0.0\\n for i, data in enumerate(train_loader):\\n # get the inputs\\n inputs, EM , CM = data\\n inputs, EM, CM = inputs.to(device), EM.to(device), CM.to(device)\\n\\n # zero the parameter gradients\\n optimizer.zero_grad()\\n\\n # forward + backward + optimize\\n outputs = model(inputs)\\n EMLoss, CMLoss = map_loss(outputs,EM,CM,criterion)\\n loss = EMLoss + CMLoss\\n loss.backward()\\n optimizer.step()\\n\\n # print statistics\\n running_loss += loss.item()\\n if i % 2000 == 1999: # print every 2000 mini-batches\\n print('[%d, %5d] loss: %.3f' %\\n (epoch + 1, i + 1, running_loss / 2000))\\n running_loss = 0.0\\n print('Finished Training')\\n return _save_model(model, args.model_dir)\",\n \"def train(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def train(self, train_dataset):\\n\\n # check fine_tuning option\\n model_path = os.path.join(self.check_point, 'model.pt')\\n if self.fine_tune and not os.path.exists(model_path):\\n raise Exception('Cannot find %s.' %model_path)\\n elif self.fine_tune and os.path.exists(model_path):\\n if self.verbose:\\n pass\\n self.model = torch.load(model_path)\\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\\n \\n # capture best model\\n best_val_psnr = -1\\n best_model_state = self.model.state_dict()\\n\\n # Train the model\\n for epoch in range(self.num_epochs):\\n self._epoch_step(train_dataset, epoch)\\n self.scheduler.step()\\n\\n\\n\\n # capture running PSNR on train and val dataset\\n train_psnr, train_ssim, _, _ = self._check_PSNR(train_dataset)\\n self.hist_train_psnr.append(train_psnr)\\n \\n\\n \\n # write the model to hard-disk for testing\\n if not os.path.exists(self.check_point):\\n os.makedirs(self.check_point)\\n model_path = os.path.join(self.check_point, 'model.pt')\\n torch.save(self.model, model_path)\",\n \"def train():\\n # YOUR TRAINING CODE GOES HERE\",\n \"def train(self, X_train, y_train):\\n self.model.fit(X_train, y_train)\",\n \"def train(\\n train_dataloader: torch.utils.data.dataloader,\\n model: nn.Module,\\n loss_function: nn.Module,\\n optimizer: torch.optim.Optimizer,\\n device: torch.device,\\n scheduler=None,\\n):\\n model.train()\\n\\n total_loss = 0\\n\\n for step, batch in tqdm(enumerate(train_dataloader)):\\n\\n if step % 50 == 0 and not step == 0:\\n print(\\\" Batch {:>5,} of {:>5,}.\\\".format(step, len(train_dataloader)))\\n\\n data = batch[\\\"data\\\"].to(device)\\n label = batch[\\\"label\\\"].to(device)\\n model.zero_grad()\\n\\n preds = model(data)\\n\\n loss = loss_function(preds, label)\\n\\n total_loss = total_loss + loss.item()\\n\\n loss.backward()\\n\\n optimizer.step()\\n # scheduler.step()\",\n \"def train():\\n\\t# 1、make dataloader\\n\\ttrain_loader, val_loader, num_query, num_class = make_data_loader(cfg)\\n\\t#print(\\\"num_query:{},num_class:{}\\\".format(num_query,num_class))\\n\\n\\t# 2、make model\\n\\tmodel = build_model(cfg, num_class)\\n\\n\\t# model.eval()\\n\\t# x = model(img_tensor)\\n\\t# print(x.shape)\\n\\t# 3、 make optimizer\\n\\toptimizer = make_optimizer(cfg, model)\\n\\n\\t# 4、 make lr_scheduler\\n\\tscheduler = make_lr_scheduler(cfg, optimizer)\\n\\n\\t# 5、 make loss_func\\n\\tif cfg.MODEL.PCB_NECK:\\n\\t\\t# make loss specificially for pcb \\n\\t\\tloss_func = get_softmax_triplet_loss_fn(cfg, num_class)\\n\\telse:\\n\\t\\tloss_func = make_loss(cfg, num_class)\\n\\n\\t# get paramters\\n\\tlog_period = cfg.OUTPUT.LOG_PERIOD \\n\\tckpt_period =cfg.OUTPUT.CHECKPOINT_PERIOD\\n\\teval_period = cfg.OUTPUT.EVAL_PERIOD\\n\\toutput_dir = cfg.OUTPUT.ROOT_DIR\\n\\tdevice = cfg.MODEL.DEVICE\\n\\tepochs = cfg.SOLVER.MAX_EPOCHS\\n\\tuse_gpu = device == \\\"cuda\\\"\\n\\tuse_neck = cfg.MODEL.NECK or cfg.MODEL.LEARN_REGION \\n\\t# how many batch for each log\\n\\tbatch_size = cfg.SOLVER.IMGS_PER_BATCH\\n\\tbatch_num = len(train_loader) \\n\\t\\n\\tlog_iters = batch_num // log_period\\n\\tpretrained = cfg.MODEL.PRETRAIN_PATH != ''\\n\\tparallel = cfg.MODEL.PARALLEL \\t\\n\\tgrad_clip = cfg.DARTS.GRAD_CLIP \\n\\n\\tfeat_norm = cfg.TEST.FEAT_NORM \\n\\tckpt_save_path = cfg.OUTPUT.ROOT_DIR + cfg.OUTPUT.CKPT_DIR\\n\\tif not os.path.exists(ckpt_save_path):\\n\\t\\tos.makedirs(ckpt_save_path)\\n\\n\\n\\t# create *_result.xlsx\\n\\t# save the result for analyze\\n\\tname = (cfg.OUTPUT.LOG_NAME).split(\\\".\\\")[0] + \\\".xlsx\\\"\\n\\tresult_path = cfg.OUTPUT.ROOT_DIR + name\\n\\n\\twb = xl.Workbook()\\n\\tsheet = wb.worksheets[0]\\n\\ttitles = ['size/M','speed/ms','final_planes', 'acc', 'mAP', 'r1', 'r5', 'r10', 'loss',\\n\\t\\t\\t 'acc', 'mAP', 'r1', 'r5', 'r10', 'loss','acc', 'mAP', 'r1', 'r5', 'r10', 'loss']\\n\\tsheet.append(titles)\\n\\tcheck_epochs = [40, 80, 120, 160, 200, 240, 280, 320, 360, epochs]\\n\\tvalues = []\\n\\n\\tlogger = logging.getLogger('MobileNetReID.train')\\n\\t\\n\\t# count parameter\\n\\tsize = count_parameters(model)\\n\\tlogger.info(\\\"the param number of the model is {:.2f} M\\\".format(size))\\n\\t\\n\\tvalues.append(format(size, '.2f'))\\n\\tvalues.append(model.final_planes)\\n\\n\\tlogger.info(\\\"Start training\\\")\\n\\t\\n\\t#count = 183, x, y = batch -> 11712 for train\\n\\tif pretrained:\\n\\t\\tstart_epoch = model.start_epoch\\n\\n\\tif parallel:\\n\\t\\tmodel = nn.DataParallel(model)\\n\\n\\tif use_gpu:\\n\\t\\t# model = nn.DataParallel(model)\\n\\t\\tmodel.to(device)\\n\\t\\n\\t# save the best model\\n\\tbest_mAP, best_r1 = 0., 0.\\n\\tis_best = False\\n\\t# batch : img, pid, camid, img_path\\n\\tavg_loss, avg_acc = RunningAverageMeter(), RunningAverageMeter()\\n\\tavg_time, global_avg_time = AverageMeter(), AverageMeter()\\n\\tglobal_avg_time.reset()\\n\\tfor epoch in range(epochs):\\n\\t\\tscheduler.step()\\n\\n\\t\\tif pretrained and epoch < start_epoch - 1:\\n\\t\\t\\tcontinue\\n\\t\\n\\t\\tmodel.train()\\n\\t\\t# sum_loss, sum_acc = 0., 0.\\n\\t\\tavg_loss.reset()\\n\\t\\tavg_acc.reset()\\n\\t\\tavg_time.reset()\\n\\t\\tfor i, batch in enumerate(train_loader):\\n\\n\\t\\t\\tt0 = time.time()\\n\\t\\t\\timgs,labels = batch\\n\\n\\t\\t\\tif use_gpu:\\n\\t\\t\\t\\timgs = imgs.to(device)\\n\\t\\t\\t\\tlabels = labels.to(device)\\n\\n\\t\\t\\tres = model(imgs)\\n\\t\\t\\t# score, feat = model(imgs)\\n\\t\\t\\t# loss = loss_func(score, feat, labels)\\n\\t\\t\\tloss, acc = compute_loss_acc(use_neck, res, labels, loss_func)\\n\\t\\t\\t\\n\\t\\t\\tloss.backward()\\n\\t\\t\\tif grad_clip != 0:\\n\\t\\t\\t\\tnn.utils.clip_grad_norm(model.parameters(), grad_clip)\\n\\n\\t\\t\\toptimizer.step()\\n\\n\\t\\t\\toptimizer.zero_grad()\\n\\n\\t\\t\\t# acc = (score.max(1)[1] == labels).float().mean()\\n\\n\\t\\t\\t# sum_loss += loss\\n\\t\\t\\t# sum_acc += acc \\n\\t\\t\\tt1 = time.time()\\n\\t\\t\\tavg_time.update((t1 - t0) / batch_size)\\n\\t\\t\\tavg_loss.update(loss)\\n\\t\\t\\tavg_acc.update(acc)\\n\\n\\t\\t\\t#log the info \\n\\t\\t\\tif (i+1) % log_iters == 0:\\n\\n\\t\\t\\t\\tlogger.info(\\\"epoch {}: {}/{} with loss is {:.5f} and acc is {:.3f}\\\".format(\\n\\t\\t\\t\\t\\t epoch+1, i+1, batch_num, avg_loss.avg, avg_acc.avg))\\n\\n\\t\\tlr = optimizer.state_dict()['param_groups'][0]['lr']\\n\\t\\tlogger.info(\\\"end epochs {}/{} with lr: {:.5f} and avg_time is {:.3f} ms\\\".format(epoch+1, epochs, lr, avg_time.avg * 1000))\\n\\t\\tglobal_avg_time.update(avg_time.avg)\\n\\t\\t# change the lr \\n\\n\\t\\t# eval the model \\n\\t\\tif (epoch+1) % eval_period == 0 or (epoch + 1) == epochs :\\n\\t\\t\\t\\n\\t\\t\\tmodel.eval()\\n\\t\\t\\tmetrics = R1_mAP(num_query, use_gpu = use_gpu, feat_norm = feat_norm)\\n\\n\\t\\t\\twith torch.no_grad():\\n\\n\\t\\t\\t\\tfor vi, batch in enumerate(val_loader):\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\timgs, labels, camids = batch\\n\\n\\t\\t\\t\\t\\tif use_gpu:\\n\\t\\t\\t\\t\\t\\timgs = imgs.to(device)\\n\\n\\t\\t\\t\\t\\tfeats = model(imgs)\\n\\t\\t\\t\\t\\tmetrics.update((feats,labels, camids))\\n\\n\\t\\t\\t\\t#compute cmc and mAP\\n\\t\\t\\t\\tcmc, mAP = metrics.compute()\\n\\t\\t\\t\\tlogger.info(\\\"validation results at epoch:{}\\\".format(epoch + 1))\\n\\t\\t\\t\\tlogger.info(\\\"mAP:{:.2%}\\\".format(mAP))\\n\\t\\t\\t\\tfor r in [1,5,10]:\\n\\t\\t\\t\\t\\tlogger.info(\\\"CMC curve, Rank-{:<3}:{:.2%}\\\".format(r,cmc[r-1]))\\t\\n\\n\\t\\t\\t\\t# determine whether cur model is the best \\n\\t\\t\\t\\tif mAP > best_mAP:\\n\\t\\t\\t\\t\\tis_best = True\\n\\t\\t\\t\\t\\tbest_mAP = mAP\\n\\t\\t\\t\\t\\tlogger.info(\\\"Get a new best mAP\\\")\\n\\t\\t\\t\\tif cmc[0] > best_r1:\\n\\t\\t\\t\\t\\tis_best = True\\n\\t\\t\\t\\t\\tbest_r1 = cmc[0]\\n\\t\\t\\t\\t\\tlogger.info(\\\"Get a new best r1\\\")\\n\\n\\t\\t\\t\\t# add the result to sheet\\n\\t\\t\\t\\tif (epoch + 1) in check_epochs:\\n\\t\\t\\t\\t\\tval = [avg_acc.avg, mAP, cmc[0], cmc[4], cmc[9]]\\n\\t\\t\\t\\t\\tchange = [format(v * 100, '.2f') for v in val]\\n\\t\\t\\t\\t\\tchange.append(format(avg_loss.avg, '.3f'))\\n\\t\\t\\t\\t\\tvalues.extend(change)\\n\\n\\n\\t\\t# we hope that eval_period == ckpt_period or eval_period == k* ckpt_period where k is int\\t\\t\\t\\n\\t\\t# whether to save the model\\n\\t\\tif (epoch+1) % ckpt_period == 0 or is_best:\\n\\n\\t\\t\\tif parallel:\\n\\t\\t\\t\\ttorch.save(model.module.state_dict(), ckpt_save_path + \\\"checkpoint_{}.pth\\\".format(epoch + 1 ))\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttorch.save(model.state_dict(), ckpt_save_path + \\\"checkpoint_{}.pth\\\".format(epoch + 1 ))\\n\\n\\t\\t\\tlogger.info(\\\"checkpoint {} saved !\\\".format(epoch + 1))\\n\\n\\t\\t\\tif is_best:\\n\\t\\t\\t\\tif parallel:\\n\\t\\t\\t\\t\\ttorch.save(model.module.state_dict(), ckpt_save_path + \\\"best_ckpt.pth\\\")\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\ttorch.save(model.state_dict(), ckpt_save_path + \\\"best_ckpt.pth\\\")\\n\\t\\t\\t\\tlogger.info(\\\"best checkpoint was saved\\\")\\n\\t\\t\\t\\tis_best = False\\n\\t\\n\\tvalues.insert(1, format(global_avg_time.avg * 1000, '.2f'))\\n\\tsheet.append(values)\\n\\twb.save(result_path)\\n\\n\\tlogger.info(\\\"training is end, time for per imgs is {} ms\\\".format(global_avg_time.avg *1000))\",\n \"def train_model(model, dataloaders, criterion, optimizer, args, start_epoch=1, num_epochs=25):\\n\\n # Initializes Session History in the history file\\n init_session_history(args)\\n since = time.time()\\n\\n train_acc_history = []\\n val_acc_history = []\\n train_loss_history = []\\n val_loss_history = []\\n train_f1_score = []\\n val_f1_score = []\\n plot_epoch = []\\n\\n best_model_wts = copy.deepcopy(model.state_dict())\\n best_acc = 0.0\\n\\n for epoch in range(start_epoch, num_epochs):\\n\\n # Each epoch has a training and validation phase\\n for phase in ['train', 'val']:\\n if phase == 'train':\\n model.train() # Set model to training mode\\n train_pred_classes = []\\n train_ground_truths = []\\n else:\\n model.eval() # Set model to evaluate mode\\n val_pred_classes = []\\n val_ground_truths = []\\n\\n running_loss = 0.0\\n running_corrects = 0\\n train_n_total = 1\\n\\n pbar = tqdm(dataloaders[phase])\\n # Iterate over data.\\n for sample in pbar:\\n inputs = sample[\\\"video\\\"]\\n labels = sample[\\\"action\\\"]\\n inputs = inputs.to(device)\\n labels = labels.to(device)\\n\\n # zero the parameter gradients\\n optimizer.zero_grad()\\n\\n # forward\\n # track history if only in train\\n with torch.set_grad_enabled(phase == 'train'):\\n\\n outputs = model(inputs)\\n loss = criterion(outputs, torch.max(labels, 1)[1])\\n\\n _, preds = torch.max(outputs, 1)\\n #print(preds)\\n #print(torch.max(labels, 1)[1])\\n\\n if phase == 'train':\\n train_pred_classes.extend(preds.detach().cpu().numpy())\\n train_ground_truths.extend(torch.max(labels, 1)[1].detach().cpu().numpy())\\n else:\\n val_pred_classes.extend(preds.detach().cpu().numpy())\\n val_ground_truths.extend(torch.max(labels, 1)[1].detach().cpu().numpy())\\n\\n # backward + optimize only if in training phase\\n if phase == 'train':\\n loss.backward()\\n optimizer.step()\\n\\n # statistics\\n running_loss += loss.item() * inputs.size(0)\\n running_corrects += torch.sum(preds == torch.max(labels, 1)[1])\\n\\n pbar.set_description('Phase: {} || Epoch: {} || Loss {:.5f} '.format(phase, epoch, running_loss / train_n_total))\\n train_n_total += 1\\n\\n epoch_loss = running_loss / len(dataloaders[phase].dataset)\\n epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\\n\\n # Calculate elapsed time\\n time_elapsed = time.time() - since\\n print(phase, ' training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\\n print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\\n\\n # For Checkpointing and Confusion Matrix\\n if phase == 'val':\\n val_acc_history.append(epoch_acc)\\n val_loss_history.append(epoch_loss)\\n val_pred_classes = np.asarray(val_pred_classes)\\n val_ground_truths = np.asarray(val_ground_truths)\\n val_accuracy, val_f1, val_precision, val_recall = get_acc_f1_precision_recall(\\n val_pred_classes, val_ground_truths\\n )\\n val_f1_score.append(val_f1)\\n val_confusion_matrix = np.array_str(confusion_matrix(val_ground_truths, val_pred_classes, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))\\n print('Epoch: {} || Val_Acc: {} || Val_Loss: {}'.format(\\n epoch, val_accuracy, epoch_loss\\n ))\\n print(f'val: \\\\n{val_confusion_matrix}')\\n\\n # Deep Copy Model if best accuracy\\n if epoch_acc > best_acc:\\n best_acc = epoch_acc\\n best_model_wts = copy.deepcopy(model.state_dict())\\n\\n # set current loss to val loss for write history\\n val_loss = epoch_loss\\n\\n if phase == 'train':\\n train_acc_history.append(epoch_acc)\\n train_loss_history.append(epoch_loss)\\n train_pred_classes = np.asarray(train_pred_classes)\\n train_ground_truths = np.asarray(train_ground_truths)\\n train_accuracy, train_f1, train_precision, train_recall = get_acc_f1_precision_recall(\\n train_pred_classes, train_ground_truths\\n )\\n train_f1_score.append(train_f1)\\n train_confusion_matrix = np.array_str(confusion_matrix(train_ground_truths, train_pred_classes, labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))\\n print('Epoch: {} || Train_Acc: {} || Train_Loss: {}'.format(\\n epoch, train_accuracy, epoch_loss\\n ))\\n print(f'train: \\\\n{train_confusion_matrix}')\\n plot_epoch.append(epoch)\\n\\n # set current loss to train loss for write history\\n train_loss = epoch_loss\\n\\n # Save Weights\\n model_name = save_weights(model, args, epoch, optimizer)\\n\\n # Write History after train and validation phase\\n write_history(\\n args.history_path,\\n model_name,\\n train_loss,\\n val_loss,\\n train_accuracy,\\n val_accuracy,\\n train_f1,\\n val_f1,\\n train_precision,\\n val_precision,\\n train_recall,\\n val_recall,\\n train_confusion_matrix,\\n val_confusion_matrix\\n )\\n\\n time_elapsed = time.time() - since\\n print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\\n print('Best val Acc: {:4f}'.format(best_acc))\\n\\n # load best model weights\\n model.load_state_dict(best_model_wts)\\n return model, train_loss_history, val_loss_history, train_acc_history, val_acc_history, train_f1_score, val_f1_score, plot_epoch\",\n \"def _train(self):\\n self.network.train() # note that here we are calling torch.nn.Module train class method\\n epochs_since_improvement = 0\\n best_params = None\\n self.calculate_validation_loss()\\n best_validation_loss = self.validation_average_loss\\n\\n while epochs_since_improvement < 10:\\n self.train_epoch()\\n self.calculate_validation_loss()\\n if self.validation_average_loss < best_validation_loss:\\n epochs_since_improvement = 0\\n best_validation_loss = self.validation_average_loss\\n best_params = self.network.state_dict()\\n else:\\n epochs_since_improvement += 1\\n LOGGER.info(\\\"Epochs since improvement in validation_loss: {} \\\\n\\\".format(epochs_since_improvement))\\n if self.maximum_epochs_allowed is not None and self.epochs_trained >= self.maximum_epochs_allowed:\\n break\\n LOGGER.info(\\\"Training complete after {} epochs \\\\n\\\".format(self.epochs_trained))\\n LOGGER.info(\\\"Best training loss achieved: {} \\\\n\\\".format(self.training_average_loss))\\n LOGGER.info(\\\"Best validation loss achieved: {}\\\".format(self.validation_average_loss))\\n self.learned_params = best_params\\n self.network.load_state_dict(best_params)\",\n \"def train():\\n import training\\n\\n # Ensure output directories exist\\n os.makedirs(os.path.dirname(cfg.scaler_path), exist_ok=True)\\n os.makedirs(cfg.model_path, exist_ok=True)\\n os.makedirs(cfg.log_path, exist_ok=True)\\n\\n # Load (standardized) input data and target values\\n tr_x, tr_y, _ = _load_data(cfg.training_set, is_training=True)\\n val_x, val_y, _ = _load_data(cfg.validation_set)\\n\\n # Try to create reproducible results\\n np.random.seed(cfg.initial_seed)\\n\\n # Save free parameters to disk\\n utils.log_parameters(cfg.training, os.path.join(cfg.model_path,\\n 'parameters.json'))\\n\\n training.train(tr_x, tr_y, val_x, val_y)\",\n \"def train(model, optimizer, loss_fn, dataloader, epoch):\\n\\n # Set the model into train mode\\n model.train()\\n\\n train_loss = 0\\n correct = 0\\n total = 0\\n datacount = len(dataloader)\\n\\n for batch_idx, (train_batch, labels_batch) in enumerate(dataloader):\\n\\n # move the data onto the device\\n train_batch, labels_batch = train_batch.to(device), labels_batch.to(device)\\n\\n # # convert to torch Variables\\n # train_batch, labels_batch = Variable(train_batch), Variable(labels_batch)\\n\\n # clear the previous grad \\n optimizer.zero_grad()\\n\\n # compute model outputs and loss\\n outputs = model(train_batch)\\n loss = loss_fn(outputs, labels_batch)\\n loss.backward()\\n\\n # after computing gradients based on current batch loss,\\n # apply them to parameters\\n optimizer.step()\\n\\n train_loss += loss.item()\\n _, predicted = outputs.max(1)\\n total += labels_batch.size(0)\\n correct += predicted.eq(labels_batch).sum().item()\\n # get learning rate\\n current_lr = get_lr(optimizer=optimizer)\\n\\n # write to tensorboard\\n writer.add_scalar('train/loss', train_loss/(batch_idx+1), (datacount * (epoch+1)) + (batch_idx+1))\\n writer.add_scalar('train/accuracy', 100.*correct/total, (datacount * (epoch+1)) + (batch_idx+1))\\n writer.add_scalar('Learning rate', current_lr)\\n\\n progress_bar(batch_idx, len(dataloader), 'Train Loss: %.3f | Train Acc: %.3f%% (%d/%d)'\\n % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))\",\n \"def forward_train(self, *args, **kwargs):\\n pass\",\n \"def training(self):\\n self.model.fit(self.train_x, self.train_y)\",\n \"def train(args, mesh_obj, model, opt=None, iters=10000, device='cuda', liveplot=False, run_label=None):\\n criterion = lambda x, z: torch.mean(torch.relu(x) - x * z + torch.log(1 + torch.exp(-torch.abs(x))))\\n\\n data_iter = tqdm(range(1, iters + 1))\\n if run_label is not None:\\n data_iter.set_description(run_label)\\n\\n mesh, corners = mesh_obj\\n c0, c1 = [torch.tensor(t, dtype=torch.float32) for t in corners]\\n render_args_hr[0] = [torch.tensor(t, dtype=torch.float32) for t in render_args_hr[0]]\\n render_args_lr[0] = [torch.tensor(t, dtype=torch.float32) for t in render_args_lr[0]]\\n render_args_hr[1] = [c0, c1]\\n render_args_lr[1] = [c0, c1]\\n\\n c1, c0 = c1.to(device), c0.to(device)\\n \\n step_list = []\\n loss_list = []\\n step_time_list = []\\n test_psnr_list = []\\n\\n model = model.to(device)\\n\\n # Main training Loop\\n postfix = {'loss': np.inf, 'psnr': 0., 'forward_steps': 0}\\n for i in data_iter:\\n start_time = time.time()\\n \\n inputs = torch.rand(args.train_batch_size, 3).to(device) * (c1 - c0) + c0\\n z_init = torch.zeros(1, model.interm_channels).to(device)\\n\\n target = torch.tensor(gt_fn(inputs.cpu().numpy(), mesh), dtype=torch.bool).to(device)\\n \\n model_outputs = model(inputs, z_init, skip_solver=False, verbose=False, include_grad=False)\\n\\n pred = torch.sigmoid(model_outputs['output'].squeeze())\\n loss = criterion(model_outputs['output'].squeeze(), target.float())\\n loss_list.append(loss.item())\\n\\n if opt:\\n opt.zero_grad()\\n loss.backward()\\n\\n opt.step()\\n \\n postfix['forward_steps'] = model_outputs['forward_steps']\\n postfix['loss'] = loss.item()\\n \\n step_time = time.time() - start_time\\n step_time_list.append(step_time)\\n\\n do_log = i % args.log_freq == 0\\n do_vis = i % args.vis_freq == 0\\n\\n if i % args.save_freq == 0:\\n torch.save(model.state_dict(), f'{args.save_dir:s}/model_step{i:d}.pth')\\n\\n if do_log:\\n summary_dict = {\\n 'train_loss': loss_list,\\n 'step_time': step_time_list\\n }\\n\\n with open('{:s}/summary.pkl'.format(args.log_dir), 'wb') as summary_f:\\n pickle.dump(summary_dict, summary_f)\\n \\n if do_vis:\\n with torch.no_grad():\\n depth_map, acc_map = render_rays_native_hier(model, *render_args_lr, device=device)\\n normal_map = make_normals(render_args_lr[0], depth_map.cpu()) * 0.5 + 0.5\\n\\n fig, axes = plt.subplots(1, 3)\\n fig.set_size_inches(6 * 3, 6)\\n\\n for ax in axes:\\n ax.clear()\\n ax.set_axis_off()\\n\\n axes[0].imshow(depth_map.squeeze().cpu().numpy())\\n axes[1].imshow(acc_map.squeeze().cpu().numpy())\\n axes[2].imshow(normal_map.squeeze().cpu().numpy())\\n\\n fig.set_tight_layout(True)\\n fig.savefig(args.vis_dir + '/vis_step{:d}.png'.format(i))\\n\\n data_iter.set_postfix(postfix)\\n\\n with torch.no_grad():\\n depth_map, acc_map = render_rays_native_hier(model, *render_args_hr, device=device)\\n normal_map = make_normals(render_args_hr[0], depth_map.cpu()) * 0.5 + 0.5\\n\\n skimage.io.imsave(args.vis_dir + '/final_rendered.png', normal_map.squeeze().cpu().numpy())\\n\\n return {\\n 'loss': loss_list,\\n 'step_time': step_time_list,\\n 'step': step_list,\\n 'test_psnr': test_psnr_list,\\n }\",\n \"def train_model(\\n checkpoint: int,\\n checkpoint_step: int,\\n data_loader: torch.utils.data.DataLoader,\\n device: torch.device,\\n epoch: int,\\n experiment: str,\\n max_norm: float,\\n model: lmp.model.BaseRNNModel,\\n optimizer: Union[\\n torch.optim.SGD,\\n torch.optim.Adam,\\n ],\\n vocab_size: int\\n):\\n # Set experiment output folder.\\n file_dir = f'{lmp.path.DATA_PATH}/{experiment}'\\n\\n if not os.path.exists(file_dir):\\n os.makedirs(file_dir)\\n\\n # Set experiment log folder.\\n writer = torch.utils.tensorboard.SummaryWriter(\\n f'{lmp.path.DATA_PATH}/log/{experiment}'\\n )\\n\\n # Define objective function.\\n criterion = torch.nn.CrossEntropyLoss()\\n\\n # Step = number of updates.\\n # Every update must increment `step`.\\n step = 0\\n\\n # Set model to train mode.\\n model.train()\\n\\n # Clean up gradient in model parameters.\\n model.zero_grad()\\n\\n # Initialize total loss.\\n total_loss = 0.0\\n\\n for cur_epoch in range(epoch):\\n\\n epoch_iterator = tqdm(\\n data_loader,\\n desc=f'epoch: {cur_epoch}, loss: {0:.6f}'\\n )\\n\\n for x, y in epoch_iterator:\\n # Increment step for each update.\\n step += 1\\n\\n # Continue training from previous checkpoint step.\\n if step < checkpoint:\\n continue\\n\\n # Put tensors on to specified device (CPU or GPU). Reshape `y` into\\n # shape (B x S) for cross-entropy.\\n # x.size = (B, S)\\n # y.size = (B x S)\\n x = x.to(device)\\n y = y.reshape(-1).to(device)\\n\\n # Forward pass.\\n # pred_y_logits.size = (B, S, V)\\n pred_y_logits = model(x)\\n\\n # Reshape `pred_y_logits` into shape (B x S, V) for cross-entropy.\\n pred_y_logits = pred_y_logits.reshape(-1, vocab_size)\\n\\n # Perform cross-entropy.\\n loss = criterion(pred_y_logits, y)\\n\\n # Calculate total loss.\\n total_loss += loss.item()\\n\\n # Log loss.\\n epoch_iterator.set_description(\\n f'epoch: {cur_epoch}, loss: {loss.item():.6f}'\\n )\\n\\n # Backward pass.\\n loss.backward()\\n\\n # Perform gradient clipping to avoid gradient explosion.\\n torch.nn.utils.clip_grad_norm_(\\n model.parameters(),\\n max_norm\\n )\\n\\n # Gradient descent.\\n optimizer.step()\\n\\n # `torch` required manually clean up gradient.\\n optimizer.zero_grad()\\n\\n # Save checkpoint for each `checkpoint_step`.\\n if step % checkpoint_step == 0:\\n torch.save(\\n model.state_dict(),\\n os.path.join(\\n file_dir,\\n f'model-{step}.pt'\\n )\\n )\\n torch.save(\\n optimizer.state_dict(),\\n os.path.join(\\n file_dir,\\n f'optimizer-{step}.pt'\\n )\\n )\\n # Log average loss.\\n writer.add_scalar(\\n f'{experiment}/loss',\\n total_loss / checkpoint_step,\\n step\\n )\\n total_loss = 0.0\\n\\n # Save last checkpoint.\\n torch.save(\\n model.state_dict(),\\n os.path.join(\\n file_dir,\\n f'model-{step}.pt'\\n )\\n )\\n torch.save(\\n optimizer.state_dict(),\\n os.path.join(\\n file_dir,\\n f'optimizer-{step}.pt'\\n )\\n )\",\n \"def train():\\n init_distributed_mode(args)\\n save_dir = TRAIN_CFG['save_dir']\\n if not os.path.exists(save_dir) and torch.distributed.get_rank() == 0:\\n os.mkdir(save_dir)\\n kwargs = {}\\n # If augmenting data, disable Pytorch's own augmentataion\\n # This has to be done manually as augmentation is embedded\\n # refer : https://github.com/pytorch/vision/issues/2263\\n base_path = DATASET_CFG['base_path']\\n train_set = DATASET_CFG['train']\\n valid_set = DATASET_CFG['valid']\\n dset_mean_std = DATASET_CFG['mean_std']\\n if dset_mean_std is not None:\\n dataset_mean = [i/255. for i in dset_mean_std[0]]\\n dataset_std = [i/255. for i in dset_mean_std[1]]\\n else:\\n dataset_mean, dataset_std = compute_mean_std(base_path, train_set)\\n kwargs['image_mean'] = dataset_mean\\n kwargs['image_std'] = dataset_std\\n kwargs['min_size'] = DATASET_CFG['min_size']\\n kwargs['max_size'] = DATASET_CFG['max_size']\\n kwargs['box_detections_per_img'] = 300 # increase max det to max val in our benchmark\\n\\n # Set benchmark related parameters\\n if benchmark == 'ScutHead':\\n combined_cfg = {**cfg, **sh_anchors}\\n elif benchmark == 'CrowdHuman':\\n combined_cfg = {**cfg, **ch_anchors}\\n elif benchmark == 'Combined':\\n combined_cfg = {**cfg, **combined_anchors}\\n else:\\n raise ValueError(\\\"New dataset has to be registered\\\")\\n\\n # Create Model\\n default_filter = False\\n model = customRCNN(cfg=combined_cfg,\\n use_deform=NET_CFG['use_deform'],\\n ohem=NET_CFG['ohem'],\\n context=NET_CFG['context'],\\n custom_sampling=NET_CFG['custom_sampling'],\\n default_filter=default_filter,\\n soft_nms=NET_CFG['soft_nms'],\\n upscale_rpn=NET_CFG['upscale_rpn'],\\n median_anchors=NET_CFG['median_anchors'],\\n **kwargs).cuda() \\n model_without_ddp = model\\n if args.distributed:\\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu],\\n find_unused_parameters=True)\\n model_without_ddp = model.module\\n\\n # Create Optimizer\\n params = [p for p in model.parameters() if p.requires_grad]\\n optimizer = torch.optim.SGD(params, lr=HYP_CFG['learning_rate'],\\n momentum=HYP_CFG['learning_rate'],\\n weight_decay=HYP_CFG['weight_decay'])\\n\\n scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,\\n milestones=TRAIN_CFG['milestones'],\\n gamma=HYP_CFG['gamma'])\\n # Restore from checkpoint\\n pt_model = TRAIN_CFG['pretrained_model']\\n if pt_model:\\n model_without_ddp = restore_network(model_without_ddp, pt_model,\\n only_backbone=TRAIN_CFG['only_backbone'])\\n \\n # Create training and vaid dataset\\n dataset_param = {'mean': dataset_mean, 'std':dataset_std,\\n 'shape':(kwargs['min_size'], kwargs['max_size'])}\\n batch_size = HYP_CFG['batch_size']\\n train_dataset = HeadDataset(train_set,\\n base_path,\\n dataset_param,\\n train=True)\\n val_dataset = HeadDataset(valid_set,\\n base_path,\\n dataset_param,\\n train=False)\\n train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)\\n train_batch_sampler = torch.utils.data.BatchSampler(train_sampler,\\n batch_size,\\n drop_last=True)\\n train_data_loader = torch.utils.data.DataLoader(train_dataset,\\n batch_sampler=train_batch_sampler,\\n num_workers=args.num_workers,\\n collate_fn=collate_fn)\\n\\n val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset)\\n val_batch_sampler = torch.utils.data.BatchSampler(val_sampler,\\n batch_size,\\n drop_last=True)\\n val_data_loader = torch.utils.data.DataLoader(val_dataset,\\n batch_sampler=val_batch_sampler,\\n num_workers=args.num_workers,\\n collate_fn=collate_fn)\\n # Fastforward the LR decayer\\n start_epoch = TRAIN_CFG['start_epoch']\\n max_epoch = TRAIN_CFG['max_epoch']\\n for _ in range(0, -1):\\n scheduler.step()\\n\\n # Start training\\n print(\\\"======= Training for \\\" + str(max_epoch) + \\\"===========\\\")\\n for epoch in range(start_epoch, int(max_epoch) + 1):\\n if epoch % TRAIN_CFG['eval_every'] == 0:\\n print(\\\"========= Evaluating Model ==========\\\")\\n result_dict = evaluate(model, val_data_loader, benchmark=benchmark)\\n if torch.distributed.get_rank() == 0:\\n logging.info('Eval score at {0} epoch is {1}'.format(str(epoch),\\n result_dict))\\n \\n train_one_epoch(model, optimizer, train_data_loader,\\n device, epoch, print_freq=1000)\\n scheduler.step()\\n if torch.distributed.get_rank() == 0:\\n print(\\\"Saving model\\\")\\n torch.save(model.state_dict(), osp.join(save_dir,\\n TRAIN_CFG['exp_name'] + '_epoch_' + str(epoch) + '.pth'))\",\n \"def setup(self):\\n print(\\\"setup\\\")\\n \\n self.modelToUse = 1\\n if self.train:\\n print(\\\"train\\\")\\n else:\\n print(\\\"no train\\\")\\n \\n device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n self.envSize = 17\\n \\n #init model\\n if self.train or not os.path.isfile(\\\"my-saved-model.pt\\\"):\\n self.logger.info(\\\"Setting up model from scratch.\\\")\\n if self.modelToUse == 0:\\n self.policy_net = Model_global_view(self.envSize, self.envSize, 6).to(device)\\n self.model = Model_global_view(self.envSize, self.envSize, 6).to(device)\\n elif self.modelToUse == 1:\\n self.policy_net = Model_local_view(self.envSize, self.envSize, 6).to(device)\\n self.model = Model_local_view(self.envSize, self.envSize, 6).to(device)\\n else:\\n self.policy_net = Model_combined_view(self.envSize, self.envSize, 6).to(device)\\n self.model = Model_combined_view(self.envSize, self.envSize, 6).to(device)\\n self.model.load_state_dict(self.policy_net.state_dict())\\n self.model.eval()\\n else:\\n self.logger.info(\\\"Loading model from saved state.\\\")\\n with open(\\\"my-saved-model.pt\\\", \\\"rb\\\") as file:\\n if self.modelToUse == 0:\\n self.model = Model_global_view(self.envSize, self.envSize, 6)\\n elif self.modelToUse == 1:\\n self.model = Model_local_view(self.envSize, self.envSize, 6)\\n else:\\n self.model = Model_combined_view(self.envSize, self.envSize, 6)\\n if torch.cuda.is_available():\\n self.model.load_state_dict(torch.load(file))\\n self.model.to(device)\\n else:\\n self.model.load_state_dict(torch.load(file, map_location=device))\",\n \"def train(self):\\r\\n \\r\\n args = self.args\\r\\n model = self.model\\r\\n dataset = self.dataset\\r\\n train_state = self.train_state\\r\\n optimizer = self.optimizer\\r\\n scheduler = self.scheduler\\r\\n train_bar = self.train_bar\\r\\n val_bar = self.val_bar\\r\\n epoch_bar = self.epoch_bar\\r\\n \\r\\n for epoch_index in range(args.num_epochs):\\r\\n train_state['epoch_index'] = epoch_index\\r\\n \\r\\n # Iterate over training dataset\\r\\n \\r\\n running_loss,running_acc = self.train_loop(epoch_index, args, \\r\\n model, dataset, \\r\\n optimizer, train_bar)\\r\\n \\r\\n train_state['train_loss'].append(running_loss)\\r\\n train_state['train_acc'].append(running_acc)\\r\\n \\r\\n running_loss,running_acc = self.val_loop(epoch_index, args, model, \\r\\n dataset, optimizer, val_bar)\\r\\n \\r\\n \\r\\n train_state['val_loss'].append(running_loss)\\r\\n train_state['val_acc'].append(running_acc)\\r\\n \\r\\n print(\\\"Epoch \\\"+str(epoch_index+1)+\\\": Running loss=\\\"+ \\\\\\r\\n str(running_loss)+\\\", Running Acc=\\\"+str(running_acc))\\r\\n \\r\\n train_state = update_train_state(args=args, model=model, \\r\\n train_state=train_state)\\r\\n \\r\\n scheduler.step(train_state['val_loss'][-1])\\r\\n \\r\\n if train_state['stop_early']:\\r\\n break\\r\\n \\r\\n train_bar.n = 0\\r\\n val_bar.n = 0\\r\\n epoch_bar.set_postfix(best_val=train_state['early_stopping_best_val'] )\\r\\n epoch_bar.update()\\r\\n \\r\\n state_dict = torch.load(train_state['model_filename'])\\r\\n model.load_state_dict(state_dict)\\r\\n return model\",\n \"def training(self) -> None:\\n self.compile_model()\\n self.train_epoch()\\n self.agent.save()\",\n \"def _train_model(self):\\n raise NotImplementedError()\",\n \"def self_training(args):\\n\\n print('load pre-trained model from [%s]' % args.load_model, file=sys.stderr)\\n params = torch.load(args.load_model, map_location=lambda storage, loc: storage)\\n vocab = params['vocab']\\n transition_system = params['transition_system']\\n saved_args = params['args']\\n saved_state = params['state_dict']\\n\\n # transfer arguments\\n saved_args.cuda = args.cuda\\n saved_args.save_to = args.save_to\\n saved_args.train_file = args.train_file\\n saved_args.unlabeled_file = args.unlabeled_file\\n saved_args.dev_file = args.dev_file\\n saved_args.load_decode_results = args.load_decode_results\\n args = saved_args\\n\\n update_args(args)\\n\\n model = Parser(saved_args, vocab, transition_system)\\n model.load_state_dict(saved_state)\\n\\n if args.cuda: model = model.cuda()\\n model.train()\\n optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\\n\\n print('load unlabeled data [%s]' % args.unlabeled_file, file=sys.stderr)\\n unlabeled_data = Dataset.from_bin_file(args.unlabeled_file)\\n\\n print('load decoding results of unlabeled data [%s]' % args.load_decode_results, file=sys.stderr)\\n decode_results = pickle.load(open(args.load_decode_results))\\n\\n labeled_data = Dataset.from_bin_file(args.train_file)\\n dev_set = Dataset.from_bin_file(args.dev_file)\\n\\n print('Num. examples in unlabeled data: %d' % len(unlabeled_data), file=sys.stderr)\\n assert len(unlabeled_data) == len(decode_results)\\n self_train_examples = []\\n for example, hyps in zip(unlabeled_data, decode_results):\\n if hyps:\\n hyp = hyps[0]\\n sampled_example = Example(idx='self_train-%s' % example.idx,\\n src_sent=example.src_sent,\\n tgt_code=hyp.code,\\n tgt_actions=hyp.action_infos,\\n tgt_ast=hyp.tree)\\n self_train_examples.append(sampled_example)\\n print('Num. self training examples: %d, Num. labeled examples: %d' % (len(self_train_examples), len(labeled_data)),\\n file=sys.stderr)\\n\\n train_set = Dataset(examples=labeled_data.examples + self_train_examples)\\n\\n print('begin training, %d training examples, %d dev examples' % (len(train_set), len(dev_set)), file=sys.stderr)\\n print('vocab: %s' % repr(vocab), file=sys.stderr)\\n\\n epoch = train_iter = 0\\n report_loss = report_examples = 0.\\n history_dev_scores = []\\n num_trial = patience = 0\\n while True:\\n epoch += 1\\n epoch_begin = time.time()\\n\\n for batch_examples in train_set.batch_iter(batch_size=args.batch_size, shuffle=True):\\n batch_examples = [e for e in batch_examples if len(e.tgt_actions) <= args.decode_max_time_step]\\n\\n train_iter += 1\\n optimizer.zero_grad()\\n\\n loss = -model.score(batch_examples)\\n # print(loss.data)\\n loss_val = torch.sum(loss).data[0]\\n report_loss += loss_val\\n report_examples += len(batch_examples)\\n loss = torch.mean(loss)\\n\\n loss.backward()\\n\\n # clip gradient\\n if args.clip_grad > 0.:\\n grad_norm = torch.nn.utils.clip_grad_norm(model.parameters(), args.clip_grad)\\n\\n optimizer.step()\\n\\n if train_iter % args.log_every == 0:\\n print('[Iter %d] encoder loss=%.5f' %\\n (train_iter,\\n report_loss / report_examples),\\n file=sys.stderr)\\n\\n report_loss = report_examples = 0.\\n\\n print('[Epoch %d] epoch elapsed %ds' % (epoch, time.time() - epoch_begin), file=sys.stderr)\\n # model_file = args.save_to + '.iter%d.bin' % train_iter\\n # print('save model to [%s]' % model_file, file=sys.stderr)\\n # model.save(model_file)\\n\\n # perform validation\\n print('[Epoch %d] begin validation' % epoch, file=sys.stderr)\\n eval_start = time.time()\\n eval_results = evaluation.evaluate(dev_set.examples, model, args, verbose=True)\\n dev_acc = eval_results['accuracy']\\n print('[Epoch %d] code generation accuracy=%.5f took %ds' % (epoch, dev_acc, time.time() - eval_start), file=sys.stderr)\\n is_better = history_dev_scores == [] or dev_acc > max(history_dev_scores)\\n history_dev_scores.append(dev_acc)\\n\\n if is_better:\\n patience = 0\\n model_file = args.save_to + '.bin'\\n print('save currently the best model ..', file=sys.stderr)\\n print('save model to [%s]' % model_file, file=sys.stderr)\\n model.save(model_file)\\n # also save the optimizers' state\\n torch.save(optimizer.state_dict(), args.save_to + '.optim.bin')\\n elif epoch == args.max_epoch:\\n print('reached max epoch, stop!', file=sys.stderr)\\n exit(0)\\n elif patience < args.patience:\\n patience += 1\\n print('hit patience %d' % patience, file=sys.stderr)\\n\\n if patience == args.patience:\\n num_trial += 1\\n print('hit #%d trial' % num_trial, file=sys.stderr)\\n if num_trial == args.max_num_trial:\\n print('early stop!', file=sys.stderr)\\n exit(0)\\n\\n # decay lr, and restore from previously best checkpoint\\n lr = optimizer.param_groups[0]['lr'] * args.lr_decay\\n print('load previously best model and decay learning rate to %f' % lr, file=sys.stderr)\\n\\n # load model\\n params = torch.load(args.save_to + '.bin', map_location=lambda storage, loc: storage)\\n model.load_state_dict(params['state_dict'])\\n if args.cuda: model = model.cuda()\\n\\n # load optimizers\\n if args.reset_optimizer:\\n print('reset optimizer', file=sys.stderr)\\n optimizer = torch.optim.Adam(model.inference_model.parameters(), lr=lr)\\n else:\\n print('restore parameters of the optimizers', file=sys.stderr)\\n optimizer.load_state_dict(torch.load(args.save_to + '.optim.bin'))\\n\\n # set new lr\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n # reset patience\\n patience = 0\",\n \"def train(self, training_steps=10):\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(model: torch.nn.Module,\\n trainingset_dataloader: torch.utils.data.DataLoader, trainingset_eval_dataloader: torch.utils.data.DataLoader,\\n validationset_eval_dataloader: torch.utils.data.DataLoader,\\n results_directory: str = \\\"results\\\", n_updates: int = int(1e5), show_progress: bool = True,\\n load_file: str = None, device: torch.device = torch.device('cuda:0'), rnd_seed: int = 0,\\n num_torch_threads: int = 3, learning_rate: float = 1e-4, l1_weight_decay: float = 0,\\n l2_weight_decay: float = 0, log_training_stats_at: int = int(1e2), evaluate_at: int = int(5e3)):\\n # Append current timestamp to results directory\\n results_directory = os.path.join(results_directory, datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S'))\\n os.makedirs(results_directory, exist_ok=True)\\n \\n # Read config file and set up results folder\\n logfile = os.path.join(results_directory, 'log.txt')\\n checkpointdir = os.path.join(results_directory, 'checkpoint')\\n os.makedirs(checkpointdir, exist_ok=True)\\n tensorboarddir = os.path.join(results_directory, 'tensorboard')\\n os.makedirs(tensorboarddir, exist_ok=True)\\n \\n # Prepare tensorboard writer\\n writer = SummaryWriter(log_dir=tensorboarddir)\\n \\n # Print all outputs to logfile and terminal\\n tee_print = TeePrint(logfile)\\n tprint = tee_print.tee_print\\n \\n # Set up PyTorch and numpy random seeds\\n try:\\n torch.set_num_threads(num_torch_threads)\\n torch.manual_seed(rnd_seed)\\n np.random.seed(rnd_seed)\\n \\n # Send model to device\\n model.to(device)\\n \\n # Define loss function\\n mean_cross_entropy = torch.nn.BCEWithLogitsLoss().to(device)\\n \\n # Get optimizer (eps needs to be at about 1e-4 to be numerically stable with 16 bit float)\\n optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=l2_weight_decay, eps=1e-4)\\n \\n # Create a checkpoint dictionary with objects we want to have saved and loaded if needed\\n state = dict(model=model, optimizer=optimizer, update=0, best_validation_loss=np.inf)\\n \\n # Setup the SaverLoader class to save/load our checkpoint dictionary to files or to RAM objects\\n saver_loader = SaverLoader(save_dict=state, device=device, save_dir=checkpointdir,\\n n_savefiles=1, # keep only the latest checkpoint\\n n_inmem=1 # save checkpoint only in RAM\\n )\\n \\n # Load previous checkpoint dictionary, if load_file is specified\\n if load_file is not None:\\n state.update(saver_loader.load_from_file(loadname=load_file, verbose=True))\\n tprint(f\\\"Loaded checkpoint from file {load_file}\\\")\\n update, best_validation_loss = state['update'], state['best_validation_loss']\\n \\n # Save checkpoint dictionary to RAM object\\n saver_loader.save_to_ram(savename=str(update))\\n \\n #\\n # Start training\\n #\\n try:\\n tprint(\\\"Training model...\\\")\\n update_progess_bar = tqdm(total=n_updates, disable=not show_progress, position=0,\\n desc=f\\\"loss={np.nan:6.4f}\\\")\\n while update < n_updates:\\n for data in trainingset_dataloader:\\n # Get samples as lists\\n labels, inputs, sequence_lengths, counts_per_sequence, sample_ids = data\\n \\n # Apply attention-based sequence reduction and create minibatch\\n with torch.no_grad():\\n labels, inputs, sequence_lengths, n_sequences = model.reduce_and_stack_minibatch(\\n labels, inputs, sequence_lengths, counts_per_sequence)\\n \\n # Reset gradients\\n optimizer.zero_grad()\\n \\n # Calculate predictions from reduced sequences\\n logit_outputs = model(inputs, n_sequences)\\n \\n # Calculate losses\\n pred_loss = mean_cross_entropy(logit_outputs, labels[..., -1])\\n l1reg_loss = (torch.mean(torch.stack([p.abs().float().mean() for p in model.parameters()])))\\n loss = pred_loss + l1reg_loss * l1_weight_decay\\n \\n # Perform update\\n loss.backward()\\n optimizer.step()\\n \\n update += 1\\n update_progess_bar.update()\\n update_progess_bar.set_description(desc=f\\\"loss={loss.item():6.4f}\\\", refresh=True)\\n \\n # Add to tensorboard\\n if update % log_training_stats_at == 0:\\n tb_group = 'training/'\\n writer.add_scalar(tag=tb_group+'pred_loss', scalar_value=pred_loss, global_step=update)\\n writer.add_scalar(tag=tb_group+'l1reg_loss', scalar_value=l1reg_loss, global_step=update)\\n writer.add_scalar(tag=tb_group+'loss', scalar_value=loss, global_step=update)\\n writer.add_histogram(tag=tb_group+'logit_outputs', values=logit_outputs, global_step=update)\\n \\n # Calculate scores and loss on training set and validation set\\n if update % evaluate_at == 0 or update == n_updates or update == 1:\\n print(\\\" Calculating training score...\\\")\\n roc_auc, bacc, f1, scoring_loss = evaluate(model=model, dataloader=trainingset_eval_dataloader)\\n print(f\\\" ...done!\\\")\\n tprint(f\\\"[training_inference] u: {update:07d}; roc_auc: {roc_auc:6.4f}; bacc: {bacc:6.4f}; \\\"\\n f\\\"f1: {f1:6.4f}; scoring_loss: {scoring_loss:6.4f}\\\")\\n \\n tb_group = 'training_inference/'\\n writer.add_scalar(tag=tb_group+'roc_auc', scalar_value=roc_auc, global_step=update)\\n writer.add_scalar(tag=tb_group+'bacc', scalar_value=bacc, global_step=update)\\n writer.add_scalar(tag=tb_group+'f1', scalar_value=f1, global_step=update)\\n writer.add_scalar(tag=tb_group+'scoring_loss', scalar_value=scoring_loss, global_step=update)\\n \\n print(\\\" Calculating validation score...\\\")\\n roc_auc, bacc, f1, scoring_loss = evaluate(model=model, dataloader=validationset_eval_dataloader)\\n print(f\\\" ...done!\\\")\\n tprint(f\\\"[validation] u: {update:07d}; roc_auc: {roc_auc:6.4f}; bacc: {bacc:6.4f}; \\\"\\n f\\\"f1: {f1:6.4f}; scoring_loss: {scoring_loss:6.4f}\\\")\\n \\n tb_group = 'validation/'\\n writer.add_scalar(tag=tb_group+'roc_auc', scalar_value=roc_auc, global_step=update)\\n writer.add_scalar(tag=tb_group+'bacc', scalar_value=bacc, global_step=update)\\n writer.add_scalar(tag=tb_group+'f1', scalar_value=f1, global_step=update)\\n writer.add_scalar(tag=tb_group+'scoring_loss', scalar_value=scoring_loss, global_step=update)\\n writer.add_histogram(tag=tb_group+'weights',\\n values=model.sequence_embedding_16bit.conv_aas.weight.cpu().detach(),\\n global_step=update)\\n writer.add_histogram(tag=tb_group+'biases',\\n values=model.sequence_embedding_16bit.conv_aas.bias.cpu().detach(),\\n global_step=update)\\n \\n # If we have a new best loss on the validation set, we save the model as new best model\\n if best_validation_loss > scoring_loss:\\n best_validation_loss = scoring_loss\\n tprint(f\\\" New best validation loss: {scoring_loss}\\\")\\n # Save current state as RAM object\\n state['update'] = update\\n state['best_validation_loss'] = scoring_loss\\n # Save checkpoint dictionary with currently best model to RAM\\n saver_loader.save_to_ram(savename=str(update))\\n # This would save to disk every time a new best model is found, which can be slow\\n # saver_loader.save_to_file(filename=f'best_so_far_u{update}.tar.gzip')\\n \\n if update >= n_updates:\\n break\\n update_progess_bar.close()\\n finally:\\n # In any case, save the current model and best model to a file\\n saver_loader.save_to_file(filename=f'lastsave_failed_u{update}.tar.gzip')\\n state.update(saver_loader.load_from_ram()) # load best model so far\\n saver_loader.save_to_file(filename=f'best_failed_u{update}.tar.gzip')\\n print('Finished Training!')\\n except Exception as e:\\n with open(logfile, 'a') as lf:\\n print(f\\\"Exception: {e}\\\", file=lf)\\n raise e\\n finally:\\n close_all() # Clean up\",\n \"def train(model, optimizer, epochs=1):\\n model = model.to(device=device) # move the model parameters to CPU/GPU\\n for e in range(epochs):\\n for t, (x, y) in enumerate(dataloader_train):\\n model.train() # put model to training mode\\n\\n x = x.to(device=device, dtype=dtype) # move to device, e.g. GPU\\n y = y.to(device=device, dtype=torch.long)\\n\\n scores = model(x)\\n \\n loss = F.multi_margin_loss(scores, y) \\n if t == 0:\\n losses.append(loss.item())\\n\\n\\n optimizer.zero_grad()\\n loss.backward()\\n\\n optimizer.step()\\n\\n if t % print_every == 0:\\n print('Iteration %d, loss = %.4f' % (t, loss.item()))\\n check_accuracy(dataloader_val, model)\\n print()\\n # losses.append(loss.item())\",\n \"def train_step(args, model: torch.nn.Module, batch: Tuple, meters: AverageMeterSet, epoch: int, batch_idx: int):\\n labeled_batch, unlabeled_batch = batch\\n labeled, targets = labeled_batch\\n unlabeled_k, _ = unlabeled_batch\\n\\n # One hot labels\\n targets = torch.zeros(args.batch_size, args.num_classes).scatter_(\\n 1, targets.view(-1, 1), 1\\n )\\n\\n unlabeled_k = [u_k.to(args.device) for u_k in unlabeled_k]\\n labeled = labeled.to(args.device)\\n targets = targets.to(args.device)\\n\\n # Disable batch-norm running_mean and running_var updates for pseduo-label forward passes\\n set_bn_running_updates(model, enable=False)\\n with torch.no_grad():\\n preds = [\\n torch.softmax(model(u_k.to(args.device)), dim=1) for u_k in unlabeled_k\\n ]\\n avg_preds = torch.stack(preds).mean(dim=0)\\n sharpened_preds = torch.pow(avg_preds, 1 / args.temperature)\\n unlabeled_targets = sharpened_preds / sharpened_preds.sum(dim=-1, keepdim=True)\\n unlabeled_targets = unlabeled_targets.detach()\\n\\n all_inputs = torch.cat([labeled] + unlabeled_k, dim=0)\\n all_targets = torch.cat(\\n [targets] + [unlabeled_targets for _ in range(len(unlabeled_k))], dim=0\\n )\\n\\n mixed_input, mixed_targets = mixup(all_inputs, all_targets, args.alpha)\\n\\n # Interleave labeled and unlabeled samples to avoid biased batch norm calculation\\n mixed_input = list(torch.split(mixed_input, args.batch_size))\\n mixed_input = interleave(mixed_input, args.batch_size)\\n\\n # Only update running batch-norm parameters for first batch of mixed batches\\n set_bn_running_updates(model, enable=True)\\n logits = [model(mixed_input[0])]\\n set_bn_running_updates(model, enable=False)\\n for input in mixed_input[1:]:\\n logits.append(model(input))\\n\\n # Put interleaved samples back - reverses interleaving applied before\\n logits = interleave(logits, args.batch_size)\\n logits_x = logits[0]\\n logits_u = torch.cat(logits[1:], dim=0)\\n\\n # Cross entropy loss for labeled samples\\n labeled_loss = -torch.sum(\\n F.log_softmax(logits_x, dim=1) * mixed_targets[: args.batch_size], dim=1\\n )\\n # L2-distance loss for unlabeled samples\\n unlabeled_loss = torch.mean(\\n (torch.softmax(logits_u, dim=1) - mixed_targets[args.batch_size :]) ** 2\\n )\\n\\n # Update unlabeled loss weight based on current step (linear rampup to max. value over first 16 epochs)\\n step = epoch * args.iters_per_epoch + (batch_idx + 1)\\n wu = (\\n args.wu * linear_rampup(step, 16 * args.iters_per_epoch)\\n if not args.resume\\n else args.wu\\n )\\n\\n # Total loss\\n loss = torch.mean(labeled_loss) + wu * unlabeled_loss\\n\\n meters.update(\\\"total_loss\\\", loss.item(), targets.size(0))\\n meters.update(\\\"labeled_loss\\\", torch.mean(labeled_loss).item(), targets.size(0))\\n meters.update(\\\"unlabeled_loss\\\", unlabeled_loss.item(), targets.size(0))\\n meters.update(\\\"wu\\\", wu, 1)\\n return loss\",\n \"def train_model(\\n face_model,\\n frame_model,\\n audio_model,\\n combination_model,\\n dataloader,\\n optimizer,\\n criterion,\\n num_epochs,\\n checkpoint_path,\\n save_freq,\\n eval_freq,\\n device=torch.device(\\\"cpu\\\"),\\n verbose=False,\\n):\\n assert isinstance(face_model, torch.nn.Module)\\n assert isinstance(frame_model, torch.nn.Module)\\n assert isinstance(audio_model, torch.nn.Module)\\n assert isinstance(combination_model, torch.nn.Module)\\n assert isinstance(num_epochs, int)\\n assert isinstance(checkpoint_path, str)\\n assert isinstance(save_freq, int)\\n assert isinstance(eval_freq, int)\\n assert isinstance(device, torch.device)\\n assert isinstance(verbose, bool)\\n\\n criterion_type = \\\"sinkhorn\\\" if isinstance(criterion, type(SamplesLoss())) else \\\"bce\\\"\\n\\n Path(checkpoint_path).mkdir(parents=True, exist_ok=True)\\n losses = list()\\n meter = AverageMeter()\\n\\n if verbose is False:\\n print(\\\"\\\".join(\\\"-\\\" * 80))\\n print(\\\"Epoch 0\\\")\\n print(\\\"\\\".join(\\\"-\\\" * 80))\\n progress_bar = tqdm(total=len(dataloader))\\n for epoch in range(num_epochs):\\n face_model.train()\\n frame_model.train()\\n audio_model.train()\\n combination_model.train()\\n\\n for batch_idx, batch in enumerate(dataloader):\\n face_model.train()\\n frame_model.train()\\n audio_model.train()\\n combination_model.train()\\n\\n frames, audio_stft, label = batch\\n frames = frames[:, :75]\\n frames = frames.float().to(device)\\n audio_stft = audio_stft.view(audio_stft.shape[0], -1).float().to(device)\\n label = label.to(device)\\n n_frames = frames.shape[0]\\n\\n try:\\n landmarks, face_embeddings = face_model(frames)\\n\\n extracted_lips = models.AugmentedLipNet._crop_lips_batch(\\n frames, landmarks\\n )\\n extracted_lips = (\\n extracted_lips.permute(0, -1, 1, 2, 3).float().to(device)\\n )\\n frame_embeddings = frame_model(extracted_lips)\\n\\n audio_embeddings = audio_model(audio_stft)\\n\\n concat_embeddings = torch.cat(\\n (face_embeddings, frame_embeddings, audio_embeddings[:, None, :]),\\n axis=1,\\n )\\n concat_embeddings = concat_embeddings[:, :, None, :].float().to(device)\\n prediction = combination_model(concat_embeddings)\\n\\n del frames\\n del audio_stft\\n del extracted_lips\\n del face_embeddings\\n del frame_embeddings\\n del audio_embeddings\\n del concat_embeddings\\n\\n torch.cuda.empty_cache()\\n\\n optimizer.zero_grad()\\n\\n loss = criterion(prediction, label)\\n loss.backward()\\n\\n optimizer.step()\\n losses.append(loss.item())\\n meter.update(loss.item(), n_frames)\\n\\n except (ValueError, TypeError):\\n print(\\\"No landmarks detected\\\")\\n pass\\n finally:\\n if verbose:\\n pass\\n else:\\n progress_bar.update(1)\\n torch.cuda.empty_cache()\\n\\n if (epoch + 1) % save_freq == 0:\\n # Save the model.\\n print(\\\"Saving model weights.\\\")\\n description = \\\"Epoch: {}, Loss: {}\\\".format(epoch, meter.avg)\\n filename = \\\"{}/checkpoint_{}_{}.pth\\\".format(\\n checkpoint_path, epoch, criterion_type\\n )\\n save_checkpoints(\\n face_model,\\n frame_model,\\n audio_model,\\n combination_model,\\n description,\\n filename,\\n )\\n \\\"\\\"\\\" Need to write the evaluation script first.\\n if (epoch + 1) % eval_freq == 0:\\n # Evaluate the model.\\n print(\\\"Evaluating the model\\\")\\n description = \\n \\\"\\\"\\\"\\n\\n if verbose is False:\\n print(\\\"\\\".join(\\\"-\\\" * 80))\\n print(\\\"Epoch {}\\\".format(epoch + 1))\\n print(\\\"\\\".join(\\\"-\\\" * 80))\\n progress_bar.refresh()\\n progress_bar.reset()\\n\\n losses = np.array(losses)\\n np.save(\\\"./losses\\\", losses)\\n progress_bar.close()\",\n \"def main(args):\\n # Use CUDA\\n use_cuda = args.use_cuda and torch.cuda.is_available()\\n device = torch.device(\\\"cuda\\\" if use_cuda else \\\"cpu\\\")\\n\\n # Fix random seed\\n torch.manual_seed(args.seed)\\n\\n # Generate token-to-index and index-to-token mapping\\n tok2id, id2tok = data_loader.build_or_load_vocab(\\n args.train, overwrite=False)\\n\\n print(\\\"*\\\" * 5)\\n print(args)\\n\\n # Create DataLoader() objects\\n params = {\\n \\\"batch_size\\\": args.batch_size,\\n \\\"collate_fn\\\": data_loader.collate_fn,\\n \\\"shuffle\\\": args.shuffle,\\n \\\"num_workers\\\": args.num_workers,\\n }\\n # train_dataset = data_loader.SNLIDataSet(args.train, tok2id)\\n # train_loader = torch.utils.data.DataLoader(train_dataset, **params)\\n val_dataset = data_loader.SNLIDataSet(args.val, tok2id)\\n val_loader = torch.utils.data.DataLoader(val_dataset, **params)\\n\\n # Initialize model\\n if args.model == \\\"rnn\\\": # RNN model\\n model = RNN(\\n vocab_size=const.MAX_VOCAB_SIZE, # Vocabulary size\\n emb_dim=const.EMB_DIM, # Embedding dimensions\\n hidden_dim=args.hidden_dim, # Hidden dimensions\\n dropout_prob=args.dropout_prob, # Dropout probability\\n padding_idx=const.PAD_IDX, # Padding token index\\n num_classes=const.NUM_CLASSES, # Number of class labels\\n id2tok=id2tok, # Vocabulary\\n ).to(device)\\n # Load model weights from disk\\n model.load_state_dict(torch.load(const.MODELS + \\\"rnn.pt\\\"))\\n model.eval()\\n elif args.model == \\\"cnn\\\": # CNN model\\n model = CNN(\\n vocab_size=const.MAX_VOCAB_SIZE, # Vocabulary size\\n emb_dim=const.EMB_DIM, # Embedding dimensions\\n hidden_dim=args.hidden_dim, # Hidden dimensions\\n kernel_size=args.kernel_size, # Kernel size\\n dropout_prob=args.dropout_prob, # Dropout probability\\n padding_idx=const.PAD_IDX, # Padding token index\\n num_classes=const.NUM_CLASSES, # Number of class labels\\n id2tok=id2tok, # Vocabulary\\n ).to(device)\\n # Load model weights from disk\\n model.load_state_dict(torch.load(const.MODELS + \\\"cnn.pt\\\"))\\n model.eval()\\n else:\\n print(\\\"Invalid model specification, exiting\\\")\\n exit()\\n\\n # Criterion\\n criterion = torch.nn.CrossEntropyLoss()\\n # Model parameters\\n params = [p for p in model.parameters() if p.requires_grad]\\n\\n # Inspect correct/incorrect predictions\\n if args.inspect:\\n right, wrong = eval_model(val_loader, model, device, criterion,\\n inspect=True)\\n print(\\\"\\\\nValidation premises with correct predictions:\\\\n\\\")\\n for i, item in enumerate(right):\\n text = \\\" \\\".join([id2tok[idx] for idx in item if idx > 0])\\n print(\\\"#{}\\\\n {}\\\".format(i + 1, text))\\n print(\\\"\\\\nValidation premises with incorrect predictions:\\\\n\\\")\\n for i, item in enumerate(wrong):\\n text = \\\" \\\".join([id2tok[idx] for idx in item if idx > 0])\\n print(\\\"#{}\\\\n {}\\\".format(i + 1, text))\\n return\\n\\n # Validation\\n val_acc, _ = eval_model(val_loader, model, device, criterion)\\n print(\\\"\\\\n Validation accuracy: {}\\\".format(val_acc))\\n\\n print(\\\"*\\\" * 5 + \\\"\\\\n\\\")\",\n \"def train(params):\\n p, x = load_simulated_data()\\n\\n # calculate means\\n p_mean = p.mean(axis=(0, 1))\\n p_std = p.std(axis=(0, 1))\\n\\n x_mean = x.mean(axis=(0, 1))\\n x_std = x.std(axis=(0, 1))\\n\\n # TODO - does this make sense?\\n # delta = x[:,2::2] - x[:,:-2:2]\\n # the number to look ahead\\n delta = x[:, 1:] - x[:, :-1]\\n delta_mean = delta.mean(axis=(0, 1))\\n delta_std = delta.std(axis=(0, 1))\\n\\n # send to torch tensors\\n p_mean, p_std = torch.Tensor(p_mean).to(device), torch.Tensor(p_std).to(device)\\n x_mean, x_std = torch.Tensor(x_mean).to(device), torch.Tensor(x_std).to(device)\\n delta_mean, delta_std = (\\n torch.Tensor(delta_mean).to(device),\\n torch.Tensor(delta_std).to(device),\\n )\\n\\n # parameters\\n buffer_size = int(params[\\\"buffer size\\\"])\\n activation = params[\\\"activation\\\"]\\n\\n # train val split\\n training_split = 0.5\\n n = len(p)\\n k = int(n * training_split)\\n train_p, val_p = p[:k], p[k:]\\n train_x, val_x = x[:k], x[k:]\\n\\n n_ahead = 1\\n train_dataset = LookaheadDataset(states=train_x, actions=train_p, n_ahead=n_ahead)\\n val_dataset = LookaheadDataset(states=val_x, actions=val_p, n_ahead=n_ahead)\\n\\n action_size = len(train_dataset[0][0][0])\\n state_size = len(train_dataset[0][1])\\n output_size = len(train_dataset[0][2][0])\\n\\n model_path = params.get(\\\"model path\\\", None)\\n dropout = params[\\\"dropout\\\"]\\n hidden_layers = int(params[\\\"hidden layers\\\"])\\n hidden_size = int(params[\\\"hidden size\\\"])\\n\\n model = Network(\\n action_size=action_size,\\n state_size=state_size,\\n output_size=output_size,\\n hidden_layers=hidden_layers,\\n hidden_size=hidden_size,\\n dropout=dropout,\\n activation=activation,\\n action_mean=p_mean,\\n action_std=p_std,\\n state_mean=x_mean,\\n state_std=x_std,\\n output_mean=delta_mean,\\n output_std=delta_std,\\n )\\n\\n model.to(device)\\n if params.get(\\\"load\\\", False):\\n model.load_state_dict(torch.load(model_path))\\n\\n learning_rate = params[\\\"learning rate\\\"]\\n batch_size = int(params[\\\"batch size\\\"])\\n\\n optimizer = optim.Adam(model.parameters(), lr=learning_rate)\\n\\n train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\\n val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)\\n\\n train_losses = []\\n val_losses = []\\n\\n best_loss = np.inf\\n print_info = params.get(\\\"print\\\", False)\\n\\n epochs = int(params[\\\"epochs\\\"])\\n max_batches = np.inf\\n if print_info:\\n loop = tqdm(total=min(len(train_dataloader), max_batches) * epochs)\\n\\n def step(state, deltas):\\n s = state + deltas\\n return s\\n\\n for epoch in range(epochs):\\n model.train()\\n # new_n_ahead = min((epoch + 1) * 5, 100)\\n new_n_ahead = 10\\n if new_n_ahead != n_ahead:\\n n_ahead = new_n_ahead\\n if print_info:\\n print(n_ahead)\\n train_dataset = LookaheadDataset(\\n states=train_x, actions=train_p, n_ahead=n_ahead\\n )\\n val_dataset = LookaheadDataset(states=val_x, actions=val_p, n_ahead=n_ahead)\\n train_dataloader = DataLoader(\\n train_dataset, batch_size=batch_size, shuffle=True\\n )\\n val_dataloader = DataLoader(\\n val_dataset, batch_size=batch_size, shuffle=True\\n )\\n for b, (a, s, d) in enumerate(train_dataloader):\\n s = s.float().to(device)\\n a = a.float().to(device)\\n d = d.float().to(device)\\n\\n d_est = torch.zeros(d.shape).to(device)\\n\\n for i in range(n_ahead):\\n d_hat = model(a[:, i], s)\\n if i == 0:\\n # d_est[:,i] = d_est[:,i] + d_hat\\n d_est[:, i] = d_hat\\n else:\\n d_est[:, i] = d_est[:, i - 1] + d_hat\\n s = s + d_hat\\n\\n # normalize d\\n d = (d - delta_mean) / delta_std\\n d_est = (d_est - delta_mean) / delta_std\\n\\n loss = loss_function(d, d_est)\\n if print_info:\\n if not val_losses:\\n loop.set_description(\\\"loss: {:.3f}\\\".format(loss.item()))\\n else:\\n loop.set_description(\\n \\\"loss: {:.4f}, val loss: {:.4f}\\\".format(\\n loss.item(), val_losses[-1]\\n )\\n )\\n train_losses.append(loss.item())\\n optimizer.zero_grad()\\n loss.backward()\\n optimizer.step()\\n\\n if print_info:\\n loop.update(1)\\n if b > max_batches:\\n break\\n with torch.no_grad():\\n model.eval()\\n epoch_losses = []\\n for b, (a, s, d) in enumerate(val_dataloader):\\n s = s.float().to(device)\\n a = a.float().to(device)\\n d = d.float().to(device)\\n\\n d_est = torch.zeros(d.shape).to(device)\\n\\n for i in range(n_ahead):\\n d_hat = model(a[:, i], s)\\n if i == 0:\\n # d_est[:,i] = d_est[:,i] + d_hat\\n d_est[:, i] = d_hat\\n else:\\n d_est[:, i] = d_est[:, i - 1] + d_hat\\n s = s + d_hat\\n\\n # normalize d\\n d = (d - delta_mean) / delta_std\\n d_est = (d_est - delta_mean) / delta_std\\n\\n loss = loss_function(d, d_est)\\n\\n epoch_losses.append(loss.item())\\n if b > max_batches:\\n break\\n val_losses.append(np.mean(epoch_losses))\\n\\n if np.mean(epoch_losses) < best_loss:\\n best_loss = np.mean(epoch_losses)\\n if model_path:\\n torch.save(model.state_dict(), model_path)\\n if print_info:\\n print(\\\"Best Val Loss: {:.4}\\\".format(best_loss))\\n n_ahead = 100\\n val_dataset = LookaheadDataset(states=val_x, actions=val_p, n_ahead=n_ahead)\\n val_dataloader = DataLoader(val_dataset, batch_size=100, shuffle=True)\\n\\n # calculate HZ\\n start = time()\\n with torch.no_grad():\\n model.eval()\\n for b, (a, s, d) in enumerate(val_dataloader):\\n s = s.float().to(device)\\n a = a.float().to(device)\\n d = d.float().to(device)\\n\\n d_est = torch.zeros(d.shape).to(device)\\n\\n for i in range(n_ahead):\\n d_hat = model(a[:, i], s)\\n if i == 0:\\n # d_est[:,i] = d_est[:,i] + d_hat\\n d_est[:, i] = d_hat\\n else:\\n d_est[:, i] = d_est[:, i - 1] + d_hat\\n s = s + d_hat\\n elapsed = time() - start\\n speed = elapsed / len(val_dataloader)\\n return val_losses[-1].item(), speed\",\n \"def train(forward_fn, optimizer, scaler, batch, device, opt):\\n # Zero gradients\\n optimizer.zero_grad()\\n\\n # Data\\n x = batch.to(device)\\n nt, n = x.shape[0], x.shape[1]\\n\\n # Forward (inference)\\n x_, y, z, _, q_y_0_params, q_z_params, p_z_params, res = forward_fn(x, nt, dt=1 / opt.n_euler_steps)\\n\\n # Loss\\n # NLL\\n nll = utils.neg_logprob(x_, x, scale=opt.obs_scale).sum()\\n # y_0 KL\\n q_y_0 = utils.make_normal_from_raw_params(q_y_0_params)\\n kl_y_0 = distrib.kl_divergence(q_y_0, distrib.Normal(0, 1)).sum()\\n # z KL\\n q_z, p_z = utils.make_normal_from_raw_params(q_z_params), utils.make_normal_from_raw_params(p_z_params)\\n kl_z = distrib.kl_divergence(q_z, p_z).sum()\\n # ELBO\\n loss = nll + opt.beta_y * kl_y_0 + opt.beta_z * kl_z\\n # L2 regularization of residuals\\n if opt.l2_res > 0:\\n l2_res = torch.norm(res, p=2, dim=2).sum()\\n loss += opt.l2_res * l2_res\\n # Batch average\\n loss /= n\\n\\n # Backward and weight update\\n if opt.torch_amp:\\n with torch_amp.autocast(enabled=False):\\n scaler.scale(loss).backward()\\n scaler.step(optimizer)\\n scaler.update()\\n else:\\n if opt.apex_amp:\\n with apex_amp.scale_loss(loss, optimizer) as scaled_loss:\\n scaled_loss.backward()\\n else:\\n loss.backward()\\n optimizer.step()\\n\\n # Logs\\n with torch.no_grad():\\n loss = loss.item()\\n nll = nll.sum().item() / n\\n kl_y_0 = kl_y_0.item() / n\\n kl_z = kl_z.item() / n\\n\\n return loss, nll, kl_y_0, kl_z\",\n \"def train(\\n # fmt: off\\n lang: (\\\"Model language\\\", \\\"positional\\\", None, str),\\n output_path: (\\\"Output directory to store model in\\\", \\\"positional\\\", None, Path),\\n train_path: (\\\"Location of JSON-formatted training data\\\", \\\"positional\\\", None, Path),\\n dev_path: (\\\"Location of JSON-formatted development data\\\", \\\"positional\\\", None, Path),\\n raw_text: (\\\"Path to jsonl file with unlabelled text documents.\\\", \\\"option\\\", \\\"rt\\\", Path) = None,\\n base_model: (\\\"Name of model to update (optional)\\\", \\\"option\\\", \\\"b\\\", str) = None,\\n pipeline: (\\\"Comma-separated names of pipeline components\\\", \\\"option\\\", \\\"p\\\", str) = \\\"tagger,parser,ner\\\",\\n vectors: (\\\"Model to load vectors from\\\", \\\"option\\\", \\\"v\\\", str) = None,\\n replace_components: (\\\"Replace components from base model\\\", \\\"flag\\\", \\\"R\\\", bool) = False,\\n n_iter: (\\\"Number of iterations\\\", \\\"option\\\", \\\"n\\\", int) = 30,\\n n_early_stopping: (\\\"Maximum number of training epochs without dev accuracy improvement\\\", \\\"option\\\", \\\"ne\\\", int) = None,\\n n_examples: (\\\"Number of examples\\\", \\\"option\\\", \\\"ns\\\", int) = 0,\\n use_gpu: (\\\"Use GPU\\\", \\\"option\\\", \\\"g\\\", int) = -1,\\n version: (\\\"Model version\\\", \\\"option\\\", \\\"V\\\", str) = \\\"0.0.0\\\",\\n meta_path: (\\\"Optional path to meta.json to use as base.\\\", \\\"option\\\", \\\"m\\\", Path) = None,\\n init_tok2vec: (\\\"Path to pretrained weights for the token-to-vector parts of the models. See 'spacy pretrain'. Experimental.\\\", \\\"option\\\", \\\"t2v\\\", Path) = None,\\n parser_multitasks: (\\\"Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'\\\", \\\"option\\\", \\\"pt\\\", str) = \\\"\\\",\\n entity_multitasks: (\\\"Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'\\\", \\\"option\\\", \\\"et\\\", str) = \\\"\\\",\\n noise_level: (\\\"Amount of corruption for data augmentation\\\", \\\"option\\\", \\\"nl\\\", float) = 0.0,\\n orth_variant_level: (\\\"Amount of orthography variation for data augmentation\\\", \\\"option\\\", \\\"ovl\\\", float) = 0.0,\\n eval_beam_widths: (\\\"Beam widths to evaluate, e.g. 4,8\\\", \\\"option\\\", \\\"bw\\\", str) = \\\"\\\",\\n gold_preproc: (\\\"Use gold preprocessing\\\", \\\"flag\\\", \\\"G\\\", bool) = False,\\n learn_tokens: (\\\"Make parser learn gold-standard tokenization\\\", \\\"flag\\\", \\\"T\\\", bool) = False,\\n textcat_multilabel: (\\\"Textcat classes aren't mutually exclusive (multilabel)\\\", \\\"flag\\\", \\\"TML\\\", bool) = False,\\n textcat_arch: (\\\"Textcat model architecture\\\", \\\"option\\\", \\\"ta\\\", str) = \\\"bow\\\",\\n textcat_positive_label: (\\\"Textcat positive label for binary classes with two labels\\\", \\\"option\\\", \\\"tpl\\\", str) = None,\\n tag_map_path: (\\\"Location of JSON-formatted tag map\\\", \\\"option\\\", \\\"tm\\\", Path) = None,\\n verbose: (\\\"Display more information for debug\\\", \\\"flag\\\", \\\"VV\\\", bool) = False,\\n debug: (\\\"Run data diagnostics before training\\\", \\\"flag\\\", \\\"D\\\", bool) = False,\\n # fmt: on\\n):\\n util.fix_random_seed()\\n util.set_env_log(verbose)\\n\\n # Make sure all files and paths exists if they are needed\\n train_path = util.ensure_path(train_path)\\n dev_path = util.ensure_path(dev_path)\\n meta_path = util.ensure_path(meta_path)\\n output_path = util.ensure_path(output_path)\\n if raw_text is not None:\\n raw_text = list(srsly.read_jsonl(raw_text))\\n if not train_path or not train_path.exists():\\n msg.fail(\\\"Training data not found\\\", train_path, exits=1)\\n if not dev_path or not dev_path.exists():\\n msg.fail(\\\"Development data not found\\\", dev_path, exits=1)\\n if meta_path is not None and not meta_path.exists():\\n msg.fail(\\\"Can't find model meta.json\\\", meta_path, exits=1)\\n meta = srsly.read_json(meta_path) if meta_path else {}\\n if output_path.exists() and [p for p in output_path.iterdir() if p.is_dir()]:\\n msg.warn(\\n \\\"Output directory is not empty\\\",\\n \\\"This can lead to unintended side effects when saving the model. \\\"\\n \\\"Please use an empty directory or a different path instead. If \\\"\\n \\\"the specified output path doesn't exist, the directory will be \\\"\\n \\\"created for you.\\\",\\n )\\n if not output_path.exists():\\n output_path.mkdir()\\n msg.good(f\\\"Created output directory: {output_path}\\\")\\n\\n tag_map = {}\\n if tag_map_path is not None:\\n tag_map = srsly.read_json(tag_map_path)\\n # Take dropout and batch size as generators of values -- dropout\\n # starts high and decays sharply, to force the optimizer to explore.\\n # Batch size starts at 1 and grows, so that we make updates quickly\\n # at the beginning of training.\\n dropout_rates = util.decaying(\\n util.env_opt(\\\"dropout_from\\\", 0.2),\\n util.env_opt(\\\"dropout_to\\\", 0.2),\\n util.env_opt(\\\"dropout_decay\\\", 0.0),\\n )\\n batch_sizes = util.compounding(\\n util.env_opt(\\\"batch_from\\\", 100.0),\\n util.env_opt(\\\"batch_to\\\", 1000.0),\\n util.env_opt(\\\"batch_compound\\\", 1.001),\\n )\\n\\n if not eval_beam_widths:\\n eval_beam_widths = [1]\\n else:\\n eval_beam_widths = [int(bw) for bw in eval_beam_widths.split(\\\",\\\")]\\n if 1 not in eval_beam_widths:\\n eval_beam_widths.append(1)\\n eval_beam_widths.sort()\\n has_beam_widths = eval_beam_widths != [1]\\n\\n default_dir = Path(__file__).parent.parent / \\\"ml\\\" / \\\"models\\\" / \\\"defaults\\\"\\n\\n # Set up the base model and pipeline. If a base model is specified, load\\n # the model and make sure the pipeline matches the pipeline setting. If\\n # training starts from a blank model, intitalize the language class.\\n pipeline = [p.strip() for p in pipeline.split(\\\",\\\")]\\n msg.text(f\\\"Training pipeline: {pipeline}\\\")\\n disabled_pipes = None\\n pipes_added = False\\n if use_gpu >= 0:\\n activated_gpu = None\\n try:\\n activated_gpu = set_gpu(use_gpu)\\n except Exception as e:\\n msg.warn(f\\\"Exception: {e}\\\")\\n if activated_gpu is not None:\\n msg.text(f\\\"Using GPU: {use_gpu}\\\")\\n else:\\n msg.warn(f\\\"Unable to activate GPU: {use_gpu}\\\")\\n msg.text(\\\"Using CPU only\\\")\\n use_gpu = -1\\n if base_model:\\n msg.text(f\\\"Starting with base model '{base_model}'\\\")\\n nlp = util.load_model(base_model)\\n if nlp.lang != lang:\\n msg.fail(\\n f\\\"Model language ('{nlp.lang}') doesn't match language \\\"\\n f\\\"specified as `lang` argument ('{lang}') \\\",\\n exits=1,\\n )\\n if vectors:\\n msg.text(f\\\"Loading vectors from model '{vectors}'\\\")\\n _load_vectors(nlp, vectors)\\n\\n nlp.select_pipes(disable=[p for p in nlp.pipe_names if p not in pipeline])\\n for pipe in pipeline:\\n # first, create the model.\\n # Bit of a hack after the refactor to get the vectors into a default config\\n # use train-from-config instead :-)\\n if pipe == \\\"parser\\\":\\n config_loc = default_dir / \\\"parser_defaults.cfg\\\"\\n elif pipe == \\\"tagger\\\":\\n config_loc = default_dir / \\\"tagger_defaults.cfg\\\"\\n elif pipe == \\\"ner\\\":\\n config_loc = default_dir / \\\"ner_defaults.cfg\\\"\\n elif pipe == \\\"textcat\\\":\\n config_loc = default_dir / \\\"textcat_defaults.cfg\\\"\\n elif pipe == \\\"senter\\\":\\n config_loc = default_dir / \\\"senter_defaults.cfg\\\"\\n else:\\n raise ValueError(f\\\"Component {pipe} currently not supported.\\\")\\n pipe_cfg = util.load_config(config_loc, create_objects=False)\\n if vectors:\\n pretrained_config = {\\n \\\"@architectures\\\": \\\"spacy.VocabVectors.v1\\\",\\n \\\"name\\\": vectors,\\n }\\n pipe_cfg[\\\"model\\\"][\\\"tok2vec\\\"][\\\"pretrained_vectors\\\"] = pretrained_config\\n\\n if pipe == \\\"parser\\\":\\n pipe_cfg[\\\"learn_tokens\\\"] = learn_tokens\\n elif pipe == \\\"textcat\\\":\\n pipe_cfg[\\\"exclusive_classes\\\"] = not textcat_multilabel\\n pipe_cfg[\\\"architecture\\\"] = textcat_arch\\n pipe_cfg[\\\"positive_label\\\"] = textcat_positive_label\\n\\n if pipe not in nlp.pipe_names:\\n msg.text(f\\\"Adding component to base model '{pipe}'\\\")\\n nlp.add_pipe(nlp.create_pipe(pipe, config=pipe_cfg))\\n pipes_added = True\\n elif replace_components:\\n msg.text(f\\\"Replacing component from base model '{pipe}'\\\")\\n nlp.replace_pipe(pipe, nlp.create_pipe(pipe, config=pipe_cfg))\\n pipes_added = True\\n else:\\n if pipe == \\\"textcat\\\":\\n textcat_cfg = nlp.get_pipe(\\\"textcat\\\").cfg\\n base_cfg = {\\n \\\"exclusive_classes\\\": textcat_cfg[\\\"exclusive_classes\\\"],\\n \\\"architecture\\\": textcat_cfg[\\\"architecture\\\"],\\n \\\"positive_label\\\": textcat_cfg[\\\"positive_label\\\"],\\n }\\n if base_cfg != pipe_cfg:\\n msg.fail(\\n f\\\"The base textcat model configuration does\\\"\\n f\\\"not match the provided training options. \\\"\\n f\\\"Existing cfg: {base_cfg}, provided cfg: {pipe_cfg}\\\",\\n exits=1,\\n )\\n msg.text(f\\\"Extending component from base model '{pipe}'\\\")\\n disabled_pipes = nlp.select_pipes(\\n disable=[p for p in nlp.pipe_names if p not in pipeline]\\n )\\n else:\\n msg.text(f\\\"Starting with blank model '{lang}'\\\")\\n lang_cls = util.get_lang_class(lang)\\n nlp = lang_cls()\\n\\n if vectors:\\n msg.text(f\\\"Loading vectors from model '{vectors}'\\\")\\n _load_vectors(nlp, vectors)\\n\\n for pipe in pipeline:\\n # first, create the model.\\n # Bit of a hack after the refactor to get the vectors into a default config\\n # use train-from-config instead :-)\\n if pipe == \\\"parser\\\":\\n config_loc = default_dir / \\\"parser_defaults.cfg\\\"\\n elif pipe == \\\"tagger\\\":\\n config_loc = default_dir / \\\"tagger_defaults.cfg\\\"\\n elif pipe == \\\"morphologizer\\\":\\n config_loc = default_dir / \\\"morphologizer_defaults.cfg\\\"\\n elif pipe == \\\"ner\\\":\\n config_loc = default_dir / \\\"ner_defaults.cfg\\\"\\n elif pipe == \\\"textcat\\\":\\n config_loc = default_dir / \\\"textcat_defaults.cfg\\\"\\n elif pipe == \\\"senter\\\":\\n config_loc = default_dir / \\\"senter_defaults.cfg\\\"\\n else:\\n raise ValueError(f\\\"Component {pipe} currently not supported.\\\")\\n pipe_cfg = util.load_config(config_loc, create_objects=False)\\n if vectors:\\n pretrained_config = {\\n \\\"@architectures\\\": \\\"spacy.VocabVectors.v1\\\",\\n \\\"name\\\": vectors,\\n }\\n pipe_cfg[\\\"model\\\"][\\\"tok2vec\\\"][\\\"pretrained_vectors\\\"] = pretrained_config\\n\\n if pipe == \\\"parser\\\":\\n pipe_cfg[\\\"learn_tokens\\\"] = learn_tokens\\n elif pipe == \\\"textcat\\\":\\n pipe_cfg[\\\"exclusive_classes\\\"] = not textcat_multilabel\\n pipe_cfg[\\\"architecture\\\"] = textcat_arch\\n pipe_cfg[\\\"positive_label\\\"] = textcat_positive_label\\n\\n pipe = nlp.create_pipe(pipe, config=pipe_cfg)\\n nlp.add_pipe(pipe)\\n\\n # Update tag map with provided mapping\\n nlp.vocab.morphology.tag_map.update(tag_map)\\n\\n # Multitask objectives\\n multitask_options = [(\\\"parser\\\", parser_multitasks), (\\\"ner\\\", entity_multitasks)]\\n for pipe_name, multitasks in multitask_options:\\n if multitasks:\\n if pipe_name not in pipeline:\\n msg.fail(\\n f\\\"Can't use multitask objective without '{pipe_name}' in \\\"\\n f\\\"the pipeline\\\"\\n )\\n pipe = nlp.get_pipe(pipe_name)\\n for objective in multitasks.split(\\\",\\\"):\\n pipe.add_multitask_objective(objective)\\n\\n # Prepare training corpus\\n msg.text(f\\\"Counting training words (limit={n_examples})\\\")\\n corpus = GoldCorpus(train_path, dev_path, limit=n_examples)\\n n_train_words = corpus.count_train()\\n\\n if base_model and not pipes_added:\\n # Start with an existing model, use default optimizer\\n optimizer = create_default_optimizer()\\n else:\\n # Start with a blank model, call begin_training\\n cfg = {\\\"device\\\": use_gpu}\\n optimizer = nlp.begin_training(lambda: corpus.train_examples, **cfg)\\n nlp._optimizer = None\\n\\n # Load in pretrained weights (TODO: this may be broken in the config rewrite)\\n if init_tok2vec is not None:\\n components = _load_pretrained_tok2vec(nlp, init_tok2vec)\\n msg.text(f\\\"Loaded pretrained tok2vec for: {components}\\\")\\n\\n # Verify textcat config\\n if \\\"textcat\\\" in pipeline:\\n textcat_labels = nlp.get_pipe(\\\"textcat\\\").cfg.get(\\\"labels\\\", [])\\n if textcat_positive_label and textcat_positive_label not in textcat_labels:\\n msg.fail(\\n f\\\"The textcat_positive_label (tpl) '{textcat_positive_label}' \\\"\\n f\\\"does not match any label in the training data.\\\",\\n exits=1,\\n )\\n if textcat_positive_label and len(textcat_labels) != 2:\\n msg.fail(\\n \\\"A textcat_positive_label (tpl) '{textcat_positive_label}' was \\\"\\n \\\"provided for training data that does not appear to be a \\\"\\n \\\"binary classification problem with two labels.\\\",\\n exits=1,\\n )\\n train_data = corpus.train_data(\\n nlp,\\n noise_level=noise_level,\\n gold_preproc=gold_preproc,\\n max_length=0,\\n ignore_misaligned=True,\\n )\\n train_labels = set()\\n if textcat_multilabel:\\n multilabel_found = False\\n for ex in train_data:\\n train_labels.update(ex.gold.cats.keys())\\n if list(ex.gold.cats.values()).count(1.0) != 1:\\n multilabel_found = True\\n if not multilabel_found and not base_model:\\n msg.warn(\\n \\\"The textcat training instances look like they have \\\"\\n \\\"mutually-exclusive classes. Remove the flag \\\"\\n \\\"'--textcat-multilabel' to train a classifier with \\\"\\n \\\"mutually-exclusive classes.\\\"\\n )\\n if not textcat_multilabel:\\n for ex in train_data:\\n train_labels.update(ex.gold.cats.keys())\\n if list(ex.gold.cats.values()).count(1.0) != 1 and not base_model:\\n msg.warn(\\n \\\"Some textcat training instances do not have exactly \\\"\\n \\\"one positive label. Modifying training options to \\\"\\n \\\"include the flag '--textcat-multilabel' for classes \\\"\\n \\\"that are not mutually exclusive.\\\"\\n )\\n nlp.get_pipe(\\\"textcat\\\").cfg[\\\"exclusive_classes\\\"] = False\\n textcat_multilabel = True\\n break\\n if base_model and set(textcat_labels) != train_labels:\\n msg.fail(\\n f\\\"Cannot extend textcat model using data with different \\\"\\n f\\\"labels. Base model labels: {textcat_labels}, training data \\\"\\n f\\\"labels: {list(train_labels)}\\\",\\n exits=1,\\n )\\n if textcat_multilabel:\\n msg.text(\\n f\\\"Textcat evaluation score: ROC AUC score macro-averaged across \\\"\\n f\\\"the labels '{', '.join(textcat_labels)}'\\\"\\n )\\n elif textcat_positive_label and len(textcat_labels) == 2:\\n msg.text(\\n f\\\"Textcat evaluation score: F1-score for the \\\"\\n f\\\"label '{textcat_positive_label}'\\\"\\n )\\n elif len(textcat_labels) > 1:\\n if len(textcat_labels) == 2:\\n msg.warn(\\n \\\"If the textcat component is a binary classifier with \\\"\\n \\\"exclusive classes, provide '--textcat_positive_label' for \\\"\\n \\\"an evaluation on the positive class.\\\"\\n )\\n msg.text(\\n f\\\"Textcat evaluation score: F1-score macro-averaged across \\\"\\n f\\\"the labels '{', '.join(textcat_labels)}'\\\"\\n )\\n else:\\n msg.fail(\\n \\\"Unsupported textcat configuration. Use `spacy debug-data` \\\"\\n \\\"for more information.\\\"\\n )\\n\\n # fmt: off\\n row_head, output_stats = _configure_training_output(pipeline, use_gpu, has_beam_widths)\\n row_widths = [len(w) for w in row_head]\\n row_settings = {\\\"widths\\\": row_widths, \\\"aligns\\\": tuple([\\\"r\\\" for i in row_head]), \\\"spacing\\\": 2}\\n # fmt: on\\n print(\\\"\\\")\\n msg.row(row_head, **row_settings)\\n msg.row([\\\"-\\\" * width for width in row_settings[\\\"widths\\\"]], **row_settings)\\n try:\\n iter_since_best = 0\\n best_score = 0.0\\n for i in range(n_iter):\\n train_data = corpus.train_dataset(\\n nlp,\\n noise_level=noise_level,\\n orth_variant_level=orth_variant_level,\\n gold_preproc=gold_preproc,\\n max_length=0,\\n ignore_misaligned=True,\\n )\\n if raw_text:\\n random.shuffle(raw_text)\\n raw_batches = util.minibatch(\\n (nlp.make_doc(rt[\\\"text\\\"]) for rt in raw_text), size=8\\n )\\n words_seen = 0\\n with tqdm.tqdm(total=n_train_words, leave=False) as pbar:\\n losses = {}\\n for batch in util.minibatch_by_words(train_data, size=batch_sizes):\\n if not batch:\\n continue\\n try:\\n nlp.update(\\n batch,\\n sgd=optimizer,\\n drop=next(dropout_rates),\\n losses=losses,\\n )\\n except ValueError as e:\\n err = \\\"Error during training\\\"\\n if init_tok2vec:\\n err += \\\" Did you provide the same parameters during 'train' as during 'pretrain'?\\\"\\n msg.fail(err, f\\\"Original error message: {e}\\\", exits=1)\\n if raw_text:\\n # If raw text is available, perform 'rehearsal' updates,\\n # which use unlabelled data to reduce overfitting.\\n raw_batch = list(next(raw_batches))\\n nlp.rehearse(raw_batch, sgd=optimizer, losses=losses)\\n docs = [ex.doc for ex in batch]\\n if not int(os.environ.get(\\\"LOG_FRIENDLY\\\", 0)):\\n pbar.update(sum(len(doc) for doc in docs))\\n words_seen += sum(len(doc) for doc in docs)\\n with nlp.use_params(optimizer.averages):\\n util.set_env_log(False)\\n epoch_model_path = output_path / f\\\"model{i}\\\"\\n nlp.to_disk(epoch_model_path)\\n nlp_loaded = util.load_model_from_path(epoch_model_path)\\n for beam_width in eval_beam_widths:\\n for name, component in nlp_loaded.pipeline:\\n if hasattr(component, \\\"cfg\\\"):\\n component.cfg[\\\"beam_width\\\"] = beam_width\\n dev_dataset = list(\\n corpus.dev_dataset(\\n nlp_loaded,\\n gold_preproc=gold_preproc,\\n ignore_misaligned=True,\\n )\\n )\\n nwords = sum(len(ex.doc) for ex in dev_dataset)\\n start_time = timer()\\n scorer = nlp_loaded.evaluate(dev_dataset, verbose=verbose)\\n end_time = timer()\\n if use_gpu < 0:\\n gpu_wps = None\\n cpu_wps = nwords / (end_time - start_time)\\n else:\\n gpu_wps = nwords / (end_time - start_time)\\n with use_ops(\\\"numpy\\\"):\\n nlp_loaded = util.load_model_from_path(epoch_model_path)\\n for name, component in nlp_loaded.pipeline:\\n if hasattr(component, \\\"cfg\\\"):\\n component.cfg[\\\"beam_width\\\"] = beam_width\\n dev_dataset = list(\\n corpus.dev_dataset(\\n nlp_loaded,\\n gold_preproc=gold_preproc,\\n ignore_misaligned=True,\\n )\\n )\\n start_time = timer()\\n scorer = nlp_loaded.evaluate(dev_dataset, verbose=verbose)\\n end_time = timer()\\n cpu_wps = nwords / (end_time - start_time)\\n acc_loc = output_path / f\\\"model{i}\\\" / \\\"accuracy.json\\\"\\n srsly.write_json(acc_loc, scorer.scores)\\n\\n # Update model meta.json\\n meta[\\\"lang\\\"] = nlp.lang\\n meta[\\\"pipeline\\\"] = nlp.pipe_names\\n meta[\\\"spacy_version\\\"] = f\\\">={about.__version__}\\\"\\n if beam_width == 1:\\n meta[\\\"speed\\\"] = {\\n \\\"nwords\\\": nwords,\\n \\\"cpu\\\": cpu_wps,\\n \\\"gpu\\\": gpu_wps,\\n }\\n meta.setdefault(\\\"accuracy\\\", {})\\n for component in nlp.pipe_names:\\n for metric in _get_metrics(component):\\n meta[\\\"accuracy\\\"][metric] = scorer.scores[metric]\\n else:\\n meta.setdefault(\\\"beam_accuracy\\\", {})\\n meta.setdefault(\\\"beam_speed\\\", {})\\n for component in nlp.pipe_names:\\n for metric in _get_metrics(component):\\n meta[\\\"beam_accuracy\\\"][metric] = scorer.scores[metric]\\n meta[\\\"beam_speed\\\"][beam_width] = {\\n \\\"nwords\\\": nwords,\\n \\\"cpu\\\": cpu_wps,\\n \\\"gpu\\\": gpu_wps,\\n }\\n meta[\\\"vectors\\\"] = {\\n \\\"width\\\": nlp.vocab.vectors_length,\\n \\\"vectors\\\": len(nlp.vocab.vectors),\\n \\\"keys\\\": nlp.vocab.vectors.n_keys,\\n \\\"name\\\": nlp.vocab.vectors.name,\\n }\\n meta.setdefault(\\\"name\\\", f\\\"model{i}\\\")\\n meta.setdefault(\\\"version\\\", version)\\n meta[\\\"labels\\\"] = nlp.meta[\\\"labels\\\"]\\n meta_loc = output_path / f\\\"model{i}\\\" / \\\"meta.json\\\"\\n srsly.write_json(meta_loc, meta)\\n util.set_env_log(verbose)\\n\\n progress = _get_progress(\\n i,\\n losses,\\n scorer.scores,\\n output_stats,\\n beam_width=beam_width if has_beam_widths else None,\\n cpu_wps=cpu_wps,\\n gpu_wps=gpu_wps,\\n )\\n if i == 0 and \\\"textcat\\\" in pipeline:\\n textcats_per_cat = scorer.scores.get(\\\"textcats_per_cat\\\", {})\\n for cat, cat_score in textcats_per_cat.items():\\n if cat_score.get(\\\"roc_auc_score\\\", 0) < 0:\\n msg.warn(\\n f\\\"Textcat ROC AUC score is undefined due to \\\"\\n f\\\"only one value in label '{cat}'.\\\"\\n )\\n msg.row(progress, **row_settings)\\n # Early stopping\\n if n_early_stopping is not None:\\n current_score = _score_for_model(meta)\\n if current_score < best_score:\\n iter_since_best += 1\\n else:\\n iter_since_best = 0\\n best_score = current_score\\n if iter_since_best >= n_early_stopping:\\n msg.text(\\n f\\\"Early stopping, best iteration is: {i - iter_since_best}\\\"\\n )\\n msg.text(\\n f\\\"Best score = {best_score}; Final iteration score = {current_score}\\\"\\n )\\n break\\n except Exception as e:\\n msg.warn(f\\\"Aborting and saving final best model. Encountered exception: {e}\\\")\\n finally:\\n best_pipes = nlp.pipe_names\\n if disabled_pipes:\\n disabled_pipes.restore()\\n with nlp.use_params(optimizer.averages):\\n final_model_path = output_path / \\\"model-final\\\"\\n nlp.to_disk(final_model_path)\\n meta_loc = output_path / \\\"model-final\\\" / \\\"meta.json\\\"\\n final_meta = srsly.read_json(meta_loc)\\n final_meta.setdefault(\\\"accuracy\\\", {})\\n final_meta[\\\"accuracy\\\"].update(meta.get(\\\"accuracy\\\", {}))\\n final_meta.setdefault(\\\"speed\\\", {})\\n final_meta[\\\"speed\\\"].setdefault(\\\"cpu\\\", None)\\n final_meta[\\\"speed\\\"].setdefault(\\\"gpu\\\", None)\\n meta.setdefault(\\\"speed\\\", {})\\n meta[\\\"speed\\\"].setdefault(\\\"cpu\\\", None)\\n meta[\\\"speed\\\"].setdefault(\\\"gpu\\\", None)\\n # combine cpu and gpu speeds with the base model speeds\\n if final_meta[\\\"speed\\\"][\\\"cpu\\\"] and meta[\\\"speed\\\"][\\\"cpu\\\"]:\\n speed = _get_total_speed(\\n [final_meta[\\\"speed\\\"][\\\"cpu\\\"], meta[\\\"speed\\\"][\\\"cpu\\\"]]\\n )\\n final_meta[\\\"speed\\\"][\\\"cpu\\\"] = speed\\n if final_meta[\\\"speed\\\"][\\\"gpu\\\"] and meta[\\\"speed\\\"][\\\"gpu\\\"]:\\n speed = _get_total_speed(\\n [final_meta[\\\"speed\\\"][\\\"gpu\\\"], meta[\\\"speed\\\"][\\\"gpu\\\"]]\\n )\\n final_meta[\\\"speed\\\"][\\\"gpu\\\"] = speed\\n # if there were no speeds to update, overwrite with meta\\n if (\\n final_meta[\\\"speed\\\"][\\\"cpu\\\"] is None\\n and final_meta[\\\"speed\\\"][\\\"gpu\\\"] is None\\n ):\\n final_meta[\\\"speed\\\"].update(meta[\\\"speed\\\"])\\n # note: beam speeds are not combined with the base model\\n if has_beam_widths:\\n final_meta.setdefault(\\\"beam_accuracy\\\", {})\\n final_meta[\\\"beam_accuracy\\\"].update(meta.get(\\\"beam_accuracy\\\", {}))\\n final_meta.setdefault(\\\"beam_speed\\\", {})\\n final_meta[\\\"beam_speed\\\"].update(meta.get(\\\"beam_speed\\\", {}))\\n srsly.write_json(meta_loc, final_meta)\\n msg.good(\\\"Saved model to output directory\\\", final_model_path)\\n with msg.loading(\\\"Creating best model...\\\"):\\n best_model_path = _collate_best_model(final_meta, output_path, best_pipes)\\n msg.good(\\\"Created best model\\\", best_model_path)\",\n \"def train(model, ids, data, scaler):\\n if model.model_type == 'torch':\\n train_ids, val_ids, _, _ = train_test_split(ids, ids, test_size=0.1, random_state=1)\\n train_loader = double_loader(data, train_ids, batch_size=model.batch_size)\\n val_loader = double_loader(data, val_ids, batch_size=len(val_ids))\\n \\n regressor = copy.deepcopy(model.model) \\n optimiser = model.optimiser(regressor.parameters(), lr=model.lr)\\n loss_function = torch.nn.MSELoss()\\n name = model.name.replace(' ','_')\\n early_stopping = EarlyStopping(name,regressor)\\n \\n for epoch in range(model.num_epochs):\\n #train\\n for (sol,solv,targets) in train_loader:\\n if model.data_type == 'sentences':\\n sol, solv = sol.to(device), solv.to(device)\\n targets = targets.view(-1,1)\\n targets = scaler.transform(targets)\\n optimiser.zero_grad()\\n outputs = regressor(sol,solv).to(device)\\n cuda_targets = targets.to(device)\\n loss = loss_function(outputs, cuda_targets)\\n loss.backward()\\n optimiser.step()\\n #evaluate\\n for (sol,solv,targets) in val_loader:\\n if model.data_type == 'sentences':\\n sol, solv = sol.to(device), solv.to(device)\\n targets = targets.view(-1,1)\\n targets = scaler.transform(targets)\\n outputs = regressor(sol,solv).to(device)\\n cuda_targets = targets.to(device)\\n loss = loss_function(outputs, cuda_targets)\\n val_loss = loss.item()\\n #early stopping\\n early_stopping.store(val_loss, regressor)\\n if early_stopping.stop:\\n #print(\\\"Stopping at epoch \\\"+str(epoch))\\n break\\n regressor.load_state_dict(torch.load('checkpoints/'+name+'.pt'))\\n else:\\n regressor = sklearn.base.clone(model.model)\\n targets = scaler.transform(data[1][ids])\\n regressor.fit(data[0][ids], targets)\\n return regressor\",\n \"def train(args, model, device, train_loader, optimizer, epoch, writer):\\n model.train()\\n running_loss = 0\\n for batch_idx, (data, target) in enumerate(train_loader):\\n data, target = data.to(device), target.to(device)\\n optimizer.zero_grad()\\n output = model(data)\\n loss = F.nll_loss(output, target)\\n running_loss += loss\\n loss.backward()\\n optimizer.step()\\n if batch_idx % args.log_interval == 0:\\n writer.add_scalar('training loss', loss / args.log_interval, epoch * len(train_loader) + batch_idx)\\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\\\tLoss: {:.6f}'.format(\\n epoch, batch_idx * len(data), len(train_loader.dataset),\\n 100. * batch_idx / len(train_loader), loss.item()))\\n running_loss = 0\",\n \"def train_model(args: argparse.Namespace, hp: HParams, extension_architecture: str, timestamp: str,\\n logger: logging.Logger) -> None:\\n # 1. Check if we have to train a single tier or a complete model (with several tiers)\\n if args.tier is not None:\\n # 1.1 Argument tier was defined. Only that tier will be trained.\\n logging.info(f\\\"Training single tier of the model: Tier {args.tier}\\\")\\n\\n # 2. Setup tensorboard logging\\n # 2.1 Create tensorboard logs directory (tensorboard requires a different folder for each\\n # run of the model, in this case every run to train a tier) so we add the extension of the\\n # network's architecture of this run and the timestamp to identify it completely\\n tensorboard_dir = f\\\"{hp.logging.dir_log_tensorboard}{extension_architecture}_\\\" \\\\\\n f\\\"{timestamp}_tier{args.tier}\\\"\\n Path(tensorboard_dir).mkdir(parents=True, exist_ok=True)\\n # 2.2 Create tensorboard writer\\n tensorboardwriter = TensorboardWriter(hp, tensorboard_dir)\\n\\n # 3. Start training of the tier\\n train_tier(args, hp, args.tier, extension_architecture, timestamp, tensorboardwriter,\\n logger)\\n\\n tensorboardwriter.close()\\n\\n else:\\n # 1.2 Argument tier was not defined. Train all tiers of the model.\\n logging.info(\\\"Training all tiers of the model\\\")\\n\\n for tier in range(1, hp.network.n_tiers + 1):\\n # 2. Setup tensorboard logging (one for every tier)\\n # 2.1 Create tensorboard logs directory (tensorboard requires a different folder for\\n # each run of the model, in this case every run to train a tier) so we add the extension\\n # of the network's architecture of this run and the timestamp to identify it completely\\n tensorboard_dir = hp.logging.dir_log_tensorboard + extension_architecture \\\\\\n + f\\\"_{timestamp}_tier{tier}\\\"\\n Path(tensorboard_dir).mkdir(parents=True, exist_ok=True)\\n # 2.2 Create tensorboard writer\\n tensorboardwriter = TensorboardWriter(hp, tensorboard_dir)\\n\\n # 3. Start training of the tier\\n train_tier(args, hp, tier, extension_architecture, timestamp, tensorboardwriter, logger)\\n\\n tensorboardwriter.close()\\n del tensorboardwriter\",\n \"def train_model(model, train_dataloader, epoch, optimizer):\\r\\n model.train() # Set model to train mode\\r\\n total_epoch_loss = 0\\r\\n steps = 0\\r\\n\\r\\n for idx, (input_ids, input_mask, segment_ids, label_ids) in enumerate(train_dataloader):\\r\\n input_ids, input_mask, segment_ids, label_ids = input_ids.to(device), input_mask.to(device), segment_ids.to(device), label_ids.to(device)\\r\\n loss = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)\\r\\n loss.backward()\\r\\n total_epoch_loss += loss.item()\\r\\n\\r\\n torch.nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=1)\\r\\n optimizer.step()\\r\\n model.zero_grad()\\r\\n\\r\\n steps += 1\\r\\n\\r\\n if steps % 100 == 0:\\r\\n print(f'Epoch: {epoch + 1}, Iteration: {(idx + 1)}/{len(train_dataloader)}, Training Loss: {total_epoch_loss/steps:.4f}')\\r\\n\\r\\n return total_epoch_loss/len(train_dataloader)\",\n \"def train(self, X_train, y_train):\\n\\n self.model_pipeline.fit(X_train, y_train)\",\n \"def train(config: Config, device: torch.device, resume: Optional[ResumeInfo]) -> None:\\n # Load datasets\\n print(colored(\\\"loading training datasets:\\\", attrs=[\\\"bold\\\"]))\\n dataset_factory = DatasetFactory()\\n datasets, preprocessors = dataset_factory.create(config)\\n print(f\\\"train: {len(datasets.train)}\\\")\\n print(f\\\"val: {len(datasets.val)}\\\")\\n print(f\\\"test: {len(datasets.test)}\\\")\\n\\n # Create model runner\\n print(colored(\\\"model:\\\", attrs=[\\\"bold\\\"]))\\n runner_factory = RunnerFactory()\\n runner = runner_factory.create(config, device, preprocessors, datasets, resume)\\n print(f\\\"{runner.model=}\\\")\\n print(f\\\"{runner.criterion=}\\\")\\n print(f\\\"{runner.optimiser=}\\\")\\n\\n print(colored(\\\"training:\\\", attrs=[\\\"bold\\\"]))\\n runner.train()\",\n \"def train_model(dataset):\\n\\n # clear the session so that we can train more than one model\\n K.clear_session()\\n\\n # initialize the model\\n model = initalizer.init_nn()\\n\\n # fit the model\\n model.fit(dataset, epochs=40)\\n\\n return model\",\n \"def train(self, x, t):\\n self.model.fit(x, t)\",\n \"def train(self, x, t):\\n self.model.fit(x, t)\",\n \"def train(self, x, t):\\n self.model.fit(x, t)\",\n \"def train(self, x, t):\\n self.model.fit(x, t)\",\n \"def train(self, x, t):\\n self.model.fit(x, t)\",\n \"def train(self, **kwargs):\\n self.solver.train(**kwargs)\",\n \"def train(self):\\n model = TreeTrainer.train(self)\\n model['latent'] = self._latent\\n model['suffstats'] = {\\n 'vert_ss': self._vert_ss,\\n 'edge_ss': self._edge_ss,\\n 'feat_ss': self._feat_ss,\\n }\\n return model\",\n \"def train(epoch):\\n model.train()\\n acc_loss = 0\\n acc_nll = 0\\n acc_kl_theta_loss = 0\\n acc_kl_eta_loss = 0\\n acc_kl_alpha_loss = 0\\n acc_pred_loss = 0 # classification loss\\n cnt = 0\\n\\n indices = torch.randperm(args.num_docs_train)\\n indices = torch.split(indices, args.batch_size)\\n \\n for idx, ind in enumerate(indices):\\n\\n optimizer.zero_grad()\\n model.zero_grad() \\n \\n data_batch, times_batch, sources_batch, labels_batch = data.get_batch(\\n train_tokens, train_counts, ind, train_sources, train_labels, \\n args.vocab_size, args.emb_size, temporal=True, times=train_times) \\n\\n sums = data_batch.sum(1).unsqueeze(1)\\n\\n if args.bow_norm:\\n normalized_data_batch = data_batch / sums\\n else:\\n normalized_data_batch = data_batch\\n\\n # print(\\\"forward passing ...\\\")\\n\\n loss, nll, kl_alpha, kl_eta, kl_theta, pred_loss = model(data_batch, normalized_data_batch, \\n times_batch, sources_batch, labels_batch, train_rnn_inp, args.num_docs_train)\\n\\n # set_trace()\\n\\n # print(\\\"forward done.\\\")\\n # print(\\\"backward passing ...\\\")\\n\\n # set_trace()\\n\\n loss.backward()\\n\\n # print(\\\"backward done.\\\")\\n\\n if args.clip > 0:\\n torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)\\n optimizer.step()\\n\\n # set_trace()\\n\\n acc_loss += torch.sum(loss).item()\\n acc_nll += torch.sum(nll).item()\\n acc_kl_theta_loss += torch.sum(kl_theta).item()\\n acc_kl_eta_loss += torch.sum(kl_eta).item()\\n acc_kl_alpha_loss += torch.sum(kl_alpha).item()\\n\\n acc_pred_loss += torch.sum(pred_loss).item()\\n\\n cnt += 1\\n\\n cur_loss = round(acc_loss / cnt, 2) \\n cur_nll = round(acc_nll / cnt, 2) \\n cur_kl_theta = round(acc_kl_theta_loss / cnt, 2) \\n cur_kl_eta = round(acc_kl_eta_loss / cnt, 2) \\n cur_kl_alpha = round(acc_kl_alpha_loss / cnt, 2)\\n\\n cur_pred_loss = round(acc_pred_loss / cnt, 2) \\n\\n if idx % args.log_interval == 0 and idx > 0:\\n\\n lr = optimizer.param_groups[0]['lr']\\n print('Epoch: {} .. batch: {}/{} .. LR: {} .. KL_theta: {} .. KL_eta: {} .. KL_alpha: {} .. Rec_loss: {} .. Pred_loss: {} .. NELBO: {}'.format(\\n epoch, idx, len(indices), lr, cur_kl_theta, cur_kl_eta, cur_kl_alpha, cur_nll, cur_pred_loss, cur_loss))\\n \\n\\n lr = optimizer.param_groups[0]['lr']\\n print('*'*100)\\n print('Epoch----->{} .. LR: {} .. KL_theta: {} .. KL_eta: {} .. KL_alpha: {} .. Rec_loss: {} .. Pred_loss: {} .. NELBO: {}'.format(\\n epoch, lr, cur_kl_theta, cur_kl_eta, cur_kl_alpha, cur_nll, cur_pred_loss, cur_loss))\\n print('*'*100)\",\n \"def training(model, trainloader, in_args_epochs, in_args_gpu, \\n in_args_learning_rate):\\n if in_args_gpu == True:\\n model.to('cuda')\\n epochs = in_args_epochs\\n criterion = nn.NLLLoss()\\n optimizer = optim.Adam(model.classifier.parameters(), \\n lr=in_args_learning_rate)\\n print_every = 100\\n steps = 0\\n for e in range(epochs):\\n running_loss = 0\\n for ii, (inputs, labels) in enumerate(trainloader):\\n steps += 1\\n if in_args_gpu == True:\\n inputs, labels = inputs.to('cuda'), labels.to('cuda')\\n optimizer.zero_grad()\\n # Forward and backward passes\\n outputs = model.forward(inputs)\\n loss = criterion(outputs, labels)\\n loss.backward()\\n optimizer.step()\\n running_loss += loss.item()\\n if steps % print_every == 0:\\n print(\\\"Epoch: {}/{}... \\\".format(e+1, epochs),\\n \\\"Training Loss: {:.4f}\\\".format(running_loss/print_every))\\n running_loss = 0\",\n \"def train_model(\\n fname: Path,\\n save_name: Path,\\n batch_size: int = 32,\\n warmup_steps: int = 100,\\n steps: int = 1000,\\n num_heads: int = 2,\\n model_dim: int = 128,\\n key_dim: int = 128,\\n value_dim: int = 128,\\n dropout: float = 0.1,\\n num_mask: int = 9,\\n):\\n seqs = read_fasta(fname)\\n\\n X = seqs_to_integer(seqs)\\n\\n X = torch.from_numpy(X).type(torch.LongTensor)\\n\\n X_train, X_test = random_split(X)\\n\\n train_params = {\\n \\\"batch_size\\\": batch_size,\\n \\\"lr\\\": 0.0005,\\n \\\"weight_decay\\\": 0.0,\\n \\\"warmup_steps\\\": warmup_steps,\\n \\\"steps\\\": steps,\\n }\\n model_params[\\\"n_head\\\"] = num_heads\\n model_params[\\\"d_model\\\"] = model_dim\\n model_params[\\\"d_k\\\"] = key_dim\\n model_params[\\\"d_v\\\"] = value_dim\\n model_params[\\\"dropout\\\"] = dropout\\n model_params[\\\"num_mask\\\"] = num_mask\\n\\n model = BERT(**model_params)\\n\\n optimizer = Adam(\\n model.parameters(),\\n lr=train_params[\\\"lr\\\"],\\n weight_decay=train_params[\\\"weight_decay\\\"],\\n )\\n\\n scheduler = WarmupAnnealLR(optimizer, warmup_steps=train_params[\\\"warmup_steps\\\"])\\n\\n train(\\n model,\\n X_train,\\n X_test,\\n save_name,\\n batch_size=train_params[\\\"batch_size\\\"],\\n optimizer=optimizer,\\n scheduler=scheduler,\\n steps=train_params[\\\"steps\\\"],\\n pbar_increment=10,\\n )\",\n \"def train(self):\\r\\n self.speaker2index_and_index2speaker()\\r\\n \\\"\\\"\\\"Initialize history matrix\\\"\\\"\\\"\\r\\n self.history = np.random.normal(loc=0, scale=0.1, size=(len(self.s2i), config.train.class_history))\\r\\n \\\"\\\"\\\"\\\"\\\"\\\"\\r\\n \\\"\\\"\\\"\\\"\\\"\\\"\\r\\n iterations = 0\\r\\n \\\"\\\"\\\"Get train/test\\\"\\\"\\\"\\r\\n if WORDSPLIT:\\r\\n train, test = self.get_train_test_wordsplit()\\r\\n elif UTTERANCE_SPLIT:\\r\\n train, test, val = self.get_train_test_utterance_split()\\r\\n wordlist = joblib.load('wordlist.pkl')\\r\\n dictionary = joblib.load('dict.pkl')\\r\\n phones = joblib.load('phones.pkl')\\r\\n metadata_help = {'wordlist': wordlist, 'dictionary': dictionary, 'phones': phones}\\r\\n p2c = utils.phone2class(phones)\\r\\n c2p = utils.class2phone(phones)\\r\\n \\\"\\\"\\\"CTC loss\\\"\\\"\\\"\\r\\n # self.ctc_loss = nn.CTCLoss(blank=p2c[config.data.PAD_token], reduction='mean')\\r\\n self.ctc_loss = nn.CTCLoss(blank=p2c[config.data.PAD_token], reduction='none')\\r\\n for epoch in range(config.train.num_epochs):\\r\\n \\\"\\\"\\\"Make dataloader\\\"\\\"\\\"\\r\\n train_data = Dataset({'files': train, 'mode': 'train', 'metadata_help': metadata_help})\\r\\n train_gen = data.DataLoader(train_data, batch_size=config.train.batch_size,\\r\\n shuffle=True, collate_fn=train_data.collate, drop_last=True)\\r\\n val_data = Dataset({'files': val, 'mode': 'train', 'metadata_help': metadata_help})\\r\\n val_gen = data.DataLoader(val_data, batch_size=config.train.batch_size,\\r\\n shuffle=True, collate_fn=val_data.collate, drop_last=True)\\r\\n\\r\\n for batch_number, features in enumerate(train_gen):\\r\\n spectrograms = features['spectrograms']\\r\\n phones = features['phones']\\r\\n input_lengths = features['input_lengths']\\r\\n target_lengths = features['target_lengths']\\r\\n metadata = features[\\\"metadata\\\"]\\r\\n batch_speakers = [x['speaker'] for x in metadata]\\r\\n self.G = self.G.train()\\r\\n\\r\\n #ipdb.set_trace()\\r\\n \\\"\\\"\\\"Make input_lengths and target_lengths torch ints\\\"\\\"\\\"\\r\\n input_lengths = input_lengths.to(torch.int32)\\r\\n target_lengths = target_lengths.to(torch.int32)\\r\\n phones = phones.to(torch.int32)\\r\\n\\r\\n outputs = self.G(spectrograms)\\r\\n\\r\\n outputs = outputs.permute(1, 0, 2) # swap batch and sequence length dimension for CTC loss\\r\\n\\r\\n loss = self.ctc_loss(log_probs=outputs, targets=phones,\\r\\n input_lengths=input_lengths, target_lengths=target_lengths)\\r\\n\\r\\n \\\"\\\"\\\"Update the loss history\\\"\\\"\\\"\\r\\n self.update_history(loss, batch_speakers)\\r\\n if epoch >= config.train.regular_epochs:\\r\\n loss_weights = self.get_loss_weights(batch_speakers, type=types[0])\\r\\n else:\\r\\n loss_weights = self.get_loss_weights(batch_speakers, type=types[1])\\r\\n loss = loss * loss_weights\\r\\n\\r\\n # Backward and optimize.\\r\\n self.reset_grad()\\r\\n # loss.backward()\\r\\n loss.sum().backward()\\r\\n self.g_optimizer.step()\\r\\n\\r\\n if iterations % self.log_step == 0:\\r\\n print(str(iterations) + ', loss: ' + str(loss.sum().item()))\\r\\n if self.use_tensorboard:\\r\\n self.logger.scalar_summary('loss', loss.sum().item(), iterations)\\r\\n\\r\\n if iterations % self.model_save_step == 0:\\r\\n \\\"\\\"\\\"Calculate validation loss\\\"\\\"\\\"\\r\\n val_loss = self.val_loss(val=val_gen, iterations=iterations)\\r\\n print(str(iterations) + ', val_loss: ' + str(val_loss))\\r\\n if self.use_tensorboard:\\r\\n self.logger.scalar_summary('val_loss', val_loss, iterations)\\r\\n \\\"\\\"\\\"Save model checkpoints.\\\"\\\"\\\"\\r\\n if iterations % self.model_save_step == 0:\\r\\n G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(iterations))\\r\\n torch.save({'model': self.G.state_dict(),\\r\\n 'optimizer': self.g_optimizer.state_dict()}, G_path)\\r\\n print('Saved model checkpoints into {}...'.format(self.model_save_dir))\\r\\n\\r\\n iterations += 1\",\n \"def _train(self, dataset):\\n net = buildNetwork(\\n dataset.params_length,\\n self._default_hidden_layers,\\n 1 # a binary classifier only requires one output layer\\n )\\n ds = SupervisedDataSet(dataset)\\n trainer = BackpropTrainer(net, ds)\\n trainer.trainUntilConvergence()\\n net.activate(params.as_serialized)\",\n \"def forward_train(self, preds_T: torch.Tensor) -> torch.Tensor:\\n raise NotImplementedError\",\n \"def train_nlu(data, config, model_dir):\\n # Read the config_spacy.json as JSON \\n with open(config) as f: config = json.load(f)\\n\\n training_data = load_data(data)\\n trainer = Trainer(RasaNLUModelConfig(config))\\n trainer.train(training_data)\\n\\n model_directory = trainer.persist(model_dir, fixed_model_name= \\\"weathernlu\\\")\",\n \"def train(self):\\n self.ae_train(self.net0, self.ae0_optimizer, self.train0_loader, self.val_loader, name='Net0')\\n self.ae_train(self.net1, self.ae1_optimizer, self.train1_loader, self.val_loader, name='Net1')\\n self.ae_train(self.net2, self.ae2_optimizer, self.train2_loader, self.val_loader, name='Net2')\\n\\n self.classifier_train(self.net0, self.optimizer0, self.train0_loader, self.val_loader, name='Net0')\\n self.classifier_train(self.net1, self.optimizer1, self.train1_loader, self.val_loader, name='Net1')\\n self.classifier_train(self.net2, self.optimizer2, self.train2_loader, self.val_loader, name='Net2')\",\n \"def train(self):\\n # For debugging\\n torch.autograd.set_detect_anomaly(True)\\n # Move variables to device if haven't done so\\n self.user_features = self.move_to_cuda(self.user_features, self.device)\\n self.item_features = self.move_to_cuda(self.item_features, self.device)\\n self.labels = self.move_to_cuda(self.labels, self.device)\\n self.model = self.model.to(self.device)\\n # Pretrain\\n if self.pretrain_bm > 0:\\n self.pretrain_bm_net(self.pretrain_bm)\\n if self.pretrain_nc > 0:\\n self.pretrain_nc_net(self.pretrain_nc)\\n # optimizers\\n optims = MultipleOptimizer(torch.optim.Adam(self.model.bm_net.parameters(), lr=self.lr),\\n torch.optim.Adam(self.model.nc_net.parameters(), lr=self.lr, weight_decay=self.weight_decay))\\n # optims = torch.optim.Adam(self.model.parameters(), lr = self.lr)\\n\\n # criterion = nn.CrossEntropyLoss()\\n criterion = F.nll_loss\\n best_test_auc = 0.\\n best_val_auc = 0.\\n best_res = None\\n cnt_wait = 0\\n patience = 50\\n # Training...\\n for epoch in range(self.n_epochs):\\n self.model.train()\\n self.model.zero_grad()\\n input_adj = self.adj.clone()\\n input_adj = input_adj.to(self.device)\\n nc_logits, modified_adj, bm_loss, nc_loss = self.model(input_adj, self.user_features, self.item_features, self.n_epochs, epoch)\\n loss = nc_loss + bm_loss * self.alpha\\n optims.zero_grad()\\n loss.backward()\\n # for name, params in self.model.named_parameters():\\n # \\tif params.requires_grad:\\n # \\t\\tprint(f'{name}: requires grad')\\n # \\t\\tprint(torch.sum(params.grad))\\n optims.step()\\n # Computation Graph\\n # Validation\\n self.model.eval()\\n with torch.no_grad():\\n # input_adj = self.adj.clone()\\n # input_adj = input_adj.to(self.device)\\n # nc_logits_eval_original, _ = self.model.nc_net(input_adj, self.user_features, self.item_features)\\n # input_adj = self.adj.clone()\\n # input_adj = input_adj.to(self.device)\\n nc_logits_eval_modified, _, _, _ = self.model(input_adj, self.user_features, self.item_features, self.n_epochs, epoch)\\n training_res = self.eval_node_cls(nc_logits[self.train_nid].detach(), self.labels[self.train_nid], self.n_classes)\\n # res = self.eval_node_cls(nc_logits_eval_original[self.val_nid], self.labels[self.val_nid], self.n_classes)\\n res_modified = self.eval_node_cls(nc_logits_eval_modified[self.val_nid], self.labels[self.val_nid], self.n_classes)\\n if res_modified['auc'] > best_val_auc:\\n cnt_wait = 0\\n best_val_auc = res_modified['auc']\\n # res_test = self.eval_node_cls(nc_logits_eval_original[self.test_nid], self.labels[self.test_nid], self.n_classes)\\n res_test_modified = self.eval_node_cls(nc_logits_eval_modified[self.test_nid], self.labels[self.test_nid], self.n_classes)\\n if res_test_modified['auc'] > best_test_auc:\\n best_test_auc = res_test_modified['auc']\\n best_res = res_test_modified\\n self.logger.info('Eland Training, Epoch [{}/{}]: loss {:.4f}, train_auc: {:.4f}, val_auc {:.4f}, test_auc {:.4f}, test_ap {:.4f}'\\n .format(epoch+1, self.n_epochs, loss.item(), training_res['auc'], res_modified['auc'], res_test_modified['auc'], res_test_modified['ap']))\\n else:\\n cnt_wait += 1\\n self.logger.info('Eland Training, Epoch [{}/{}]: loss {:.4f}, train_auc: {:.4f}, val_auc {:.4f}'\\n .format(epoch+1, self.n_epochs, loss.item(), training_res['auc'], res_modified['auc']))\\n\\n if cnt_wait >= patience:\\n self.logger.info('Early stop!')\\n break\\n self.logger.info('Best Test Results: auc {:.4f}, ap {:.4f}'.format(best_res['auc'], best_res['ap']))\\n\\n return best_res['auc'], best_res['ap']\",\n \"def train(model: ContinualModel, dataset: ContinualDataset,\\n args: Namespace) -> None:\\n model.net.to(model.device)\\n results, results_mask_classes = [], []\\n \\n model_stash = create_stash(model, args, dataset)\\n \\n if args.csv_log:\\n csv_logger = CsvLogger(dataset.SETTING, dataset.NAME, model.NAME)\\n if args.tensorboard:\\n tb_logger = TensorboardLogger(args, dataset.SETTING, model_stash)\\n model_stash['tensorboard_name'] = tb_logger.get_name()\\n \\n dataset_copy = get_dataset(args)\\n for t in range(dataset.N_TASKS):\\n model.net.train()\\n _, _ = dataset_copy.get_data_loaders()\\n if model.NAME != 'icarl' and model.NAME != 'pnn':\\n random_results_class, random_results_task = evaluate(model, dataset_copy)\\n \\n print(file=sys.stderr)\\n for t in range(dataset.N_TASKS):\\n model.net.train()\\n train_loader, test_loader = dataset.get_data_loaders()\\n if hasattr(model, 'begin_task'):\\n model.begin_task(dataset)\\n if t:\\n accs = evaluate(model, dataset, last=True)\\n results[t - 1] = results[t - 1] + accs[0]\\n if dataset.SETTING == 'class-il':\\n results_mask_classes[t - 1] = results_mask_classes[t - 1] + accs[1]\\n for epoch in range(args.n_epochs):\\n for i, data in enumerate(train_loader):\\n if hasattr(dataset.train_loader.dataset, 'logits'):\\n inputs, labels, not_aug_inputs, logits = data\\n inputs = inputs.to(model.device)\\n labels = labels.to(model.device)\\n not_aug_inputs = not_aug_inputs.to(model.device)\\n logits = logits.to(model.device)\\n loss = model.observe(inputs, labels, not_aug_inputs, logits)\\n else:\\n inputs, labels, not_aug_inputs = data\\n inputs, labels = inputs.to(model.device), labels.to(\\n model.device)\\n not_aug_inputs = not_aug_inputs.to(model.device)\\n loss = model.observe(inputs, labels, not_aug_inputs)\\n \\n progress_bar(i, len(train_loader), epoch, t, loss)\\n \\n if args.tensorboard:\\n tb_logger.log_loss(loss, args, epoch, t, i)\\n \\n model_stash['batch_idx'] = i + 1\\n model_stash['epoch_idx'] = epoch + 1\\n model_stash['batch_idx'] = 0\\n model_stash['task_idx'] = t + 1\\n model_stash['epoch_idx'] = 0\\n \\n if hasattr(model, 'end_task'):\\n model.end_task(dataset)\\n \\n accs = evaluate(model, dataset)\\n results.append(accs[0])\\n results_mask_classes.append(accs[1])\\n \\n mean_acc = np.mean(accs, axis=1)\\n print_mean_accuracy(mean_acc, t + 1, dataset.SETTING)\\n \\n model_stash['mean_accs'].append(mean_acc)\\n if args.csv_log:\\n csv_logger.log(mean_acc)\\n if args.tensorboard:\\n tb_logger.log_accuracy(np.array(accs), mean_acc, args, t)\\n \\n if args.csv_log:\\n csv_logger.add_bwt(results, results_mask_classes)\\n csv_logger.add_forgetting(results, results_mask_classes)\\n if model.NAME != 'icarl' and model.NAME != 'pnn':\\n csv_logger.add_fwt(results, random_results_class,\\n results_mask_classes, random_results_task)\\n \\n if args.tensorboard:\\n tb_logger.close()\\n if args.csv_log:\\n csv_logger.write(vars(args))\",\n \"def train(self, X, y):\\n self.model.fit(X, y)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78621304","0.72798127","0.72797334","0.7267533","0.72607505","0.7249732","0.7220984","0.72000355","0.7183494","0.71330434","0.7132294","0.71074426","0.7061743","0.7004282","0.6996066","0.6973272","0.6952807","0.6951445","0.690291","0.68992347","0.6887959","0.68817943","0.6869317","0.68609846","0.68609846","0.6856915","0.68542147","0.68481493","0.68439543","0.6830481","0.6821091","0.68205404","0.681378","0.68098146","0.68058157","0.6782756","0.6774745","0.6774112","0.6765507","0.6763332","0.67523354","0.6751885","0.6723248","0.6718327","0.6713946","0.67018306","0.66987073","0.6690822","0.6688732","0.66887045","0.66854787","0.66848075","0.6678592","0.6663687","0.6653325","0.66527927","0.66483647","0.66482836","0.6645683","0.66431177","0.66415024","0.66413367","0.6628236","0.6625907","0.6625907","0.6625907","0.6625907","0.6625907","0.6616216","0.6614328","0.66131496","0.66078216","0.66077054","0.66043234","0.6594071","0.6592858","0.65914154","0.6588721","0.6584505","0.6580883","0.6579658","0.65770173","0.6576125","0.6573287","0.6573287","0.6573287","0.6573287","0.6573287","0.6563008","0.65595937","0.6558258","0.6549769","0.65466887","0.6543813","0.65418524","0.6538252","0.65363765","0.653007","0.65276486","0.6527299","0.65191513"],"string":"[\n \"0.78621304\",\n \"0.72798127\",\n \"0.72797334\",\n \"0.7267533\",\n \"0.72607505\",\n \"0.7249732\",\n \"0.7220984\",\n \"0.72000355\",\n \"0.7183494\",\n \"0.71330434\",\n \"0.7132294\",\n \"0.71074426\",\n \"0.7061743\",\n \"0.7004282\",\n \"0.6996066\",\n \"0.6973272\",\n \"0.6952807\",\n \"0.6951445\",\n \"0.690291\",\n \"0.68992347\",\n \"0.6887959\",\n \"0.68817943\",\n \"0.6869317\",\n \"0.68609846\",\n \"0.68609846\",\n \"0.6856915\",\n \"0.68542147\",\n \"0.68481493\",\n \"0.68439543\",\n \"0.6830481\",\n \"0.6821091\",\n \"0.68205404\",\n \"0.681378\",\n \"0.68098146\",\n \"0.68058157\",\n \"0.6782756\",\n \"0.6774745\",\n \"0.6774112\",\n \"0.6765507\",\n \"0.6763332\",\n \"0.67523354\",\n \"0.6751885\",\n \"0.6723248\",\n \"0.6718327\",\n \"0.6713946\",\n \"0.67018306\",\n \"0.66987073\",\n \"0.6690822\",\n \"0.6688732\",\n \"0.66887045\",\n \"0.66854787\",\n \"0.66848075\",\n \"0.6678592\",\n \"0.6663687\",\n \"0.6653325\",\n \"0.66527927\",\n \"0.66483647\",\n \"0.66482836\",\n \"0.6645683\",\n \"0.66431177\",\n \"0.66415024\",\n \"0.66413367\",\n \"0.6628236\",\n \"0.6625907\",\n \"0.6625907\",\n \"0.6625907\",\n \"0.6625907\",\n \"0.6625907\",\n \"0.6616216\",\n \"0.6614328\",\n \"0.66131496\",\n \"0.66078216\",\n \"0.66077054\",\n \"0.66043234\",\n \"0.6594071\",\n \"0.6592858\",\n \"0.65914154\",\n \"0.6588721\",\n \"0.6584505\",\n \"0.6580883\",\n \"0.6579658\",\n \"0.65770173\",\n \"0.6576125\",\n \"0.6573287\",\n \"0.6573287\",\n \"0.6573287\",\n \"0.6573287\",\n \"0.6573287\",\n \"0.6563008\",\n \"0.65595937\",\n \"0.6558258\",\n \"0.6549769\",\n \"0.65466887\",\n \"0.6543813\",\n \"0.65418524\",\n \"0.6538252\",\n \"0.65363765\",\n \"0.653007\",\n \"0.65276486\",\n \"0.6527299\",\n \"0.65191513\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":74598,"cells":{"query":{"kind":"string","value":"Returns True if word is in the wordList and is entirely composed of letters in the hand. Otherwise, returns False. Does not mutate hand or wordList."},"document":{"kind":"string","value":"def isValidWord(word, hand, wordList):\r\n # TO DO ... <-- Remove this comment when you code this function\r\n \r\n newword = {}\r\n for x in word:\r\n newword[x] = newword.get(x,0) + 1\r\n \r\n for l in newword:\r\n if newword.get(l,0) > hand.get(l, 0):\r\n return False\r\n \r\n is_valid = False\r\n for n in wordList:\r\n if word == n:\r\n return True\r\n \r\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def isValidWord(word, hand, wordList):\r\n \r\n \r\n if type(word) != str:\r\n return False\r\n \r\n if word not in wordList:\r\n return False\r\n \r\n for letter in word:\r\n if letter not in hand:\r\n return False\r\n \r\n updatedHand = hand.copy()\r\n # copy hand as to not mutate the hand passed in\r\n \r\n for letter in word:\r\n if letter in updatedHand:\r\n updatedHand[letter] -= 1\r\n \r\n for letter in word:\r\n if updatedHand[letter] < 0:\r\n return False\r\n \r\n return True","def isValidWord(word, hand, wordList):\n wordTest = word.lower()\n handcopy = hand.copy()\n for i in wordTest:\n if i not in 'abcdefghijklmnopqrstuvwxyz':\n return False\n if wordTest not in wordList:\n return False\n else:\n for i in word:\n if i not in handcopy:\n return False\n else:\n if handcopy[i]==0:\n return False\n if handcopy.get(i,0) !=0:\n handcopy[i]-=1\n if handcopy.get(i,0)<0:\n return False\n return True","def is_valid_word(word, hand, word_list):\r\n # TO DO ...\r\n word_freq_dict = get_frequency_dict(word)\r\n for letter in word_freq_dict:\r\n if letter not in hand or hand[letter] - word_freq_dict[letter] < 0:\r\n return False\r\n return word in word_list","def isValidWord(word: str, hand: Dict[str, int], wordList: List[str]) -> bool:\n cp_hand = hand.copy()\n\n if word not in wordList:\n return False\n\n for char in word:\n if cp_hand.get(char, 0) < 1:\n return False\n else:\n cp_hand[char] = cp_hand.get(char,0) - 1\n\n return True\n # one line:\n # return word in wordList and all(word.count(c) <= hand.get(c, 0) \n # for c in word) # Kiwitrader","def isValidWord(word, hand, wordList):\n myHand = hand.copy() \n for letter in word:\n if letter not in hand or myHand[letter] <= 0:\n return False\n else:\n myHand[letter] -= 1 \n if word not in wordList:\n return False\n else:\n return True","def isValidWord(word, hand, wordList):\n tempHand = hand.copy()\n if len(word) > 0 and word in wordList:\n for letter in word:\n if letter not in tempHand or tempHand[letter] <= 0:\n return False\n else:\n tempHand[letter] = tempHand.get(letter, 0) - 1\n return True\n return False","def isValidWord(word, hand, wordList):\n if word not in wordList:\n print('word not in wordList')\n return False\n else:\n for letter in word:\n if letter not in hand:\n print('letter not in hand')\n return False\n else:\n newHand = hand.copy()\n for letter in word:\n if letter not in newHand: # this if statement is here to prevent keyError\n print('letter not in newHand')\n return False\n else:\n newHand[letter] = newHand[letter] - 1\n if newHand[letter] < 0:\n print('no letter left')\n return False\n return True","def isValidWord(word, hand, wordList):\n output = hand.copy()\n word_check = False\n if word in wordList:\n word_check = True\n \n letter_check = set(list(word)) <= set(output.keys())\n \n for letter in word:\n if letter in output.keys():\n output[letter] -= 1\n \n value_check = all(i >= 0 for i in output.values())\n \n if word_check == True and letter_check == True and value_check == True:\n return True\n else:\n return False","def is_valid_word(word, hand, word_list):\n failure=True\n word=word.lower()\n if word not in word_list:\n failure=False\n for i in word:\n w=hand.get(i,0)\n if w==0:\n failure=False\n break\n return failure","def isValidWord(word, hand, wordList):\n status = []\n status2 = []\n\n # check if the input is valid\n if not word in wordList:\n return False\n \n for letter in word:\n \n ## commented out bc global var not available for the individual exercise\n # if not letter in SCRABBLE_LETTER_VALUES.keys():\n # status.append(False)\n \n if not letter in hand.keys():\n status.append(False)\n \n if False in status:\n return False\n \n # check if there are enough values in hand for the guess\n for key, value in getFrequencyDict(word).items():\n \n if not key in hand or value > hand[key]:\n status2.append(False)\n\n if False in status2:\n return False\n \n return True","def isValidWord(word, hand, wordList):\n # TO DO ... <-- Remove this comment when you code this function\n #for line in fil:\n # print line\n if word not in w:\n w.append(word)\n #print w\n else:\n #print \"already have a word inside\"\n return False\n for z in word:\n print z\n if z in hand:\n #if p != word:\n #print hand[c]\n if hand[z] > 0:\n hand[z] -= 1\n \n else:\n return False\n else:\n return False\n if word in wordList:\n print \"here is true\"\n return True\n else:\n return False","def is_valid_word(word_test, hand_word, word_list):\n count = 0\n word_test = list(word_test)\n for i_1 in word_test:\n if i_1 in hand_word:\n count += count\n if count == len(word_test) and word_test[i_1] == word_list[i_1]:\n return True\n return False","def isvalid_word1(word, hand, word_list1):\n # TO DO ... <-- Remove this comment when you code this function\n for i in word:\n if i not in hand:\n return False\n i = i\n return word in word_list1","def is_valid_word(word, hand, word_list):\n h = dict(hand)\n if word.lower() in word_list:\n for letter in word:\n if h.get(letter, 0) == 0:\n return False\n else:\n h[letter] -= 1\n else:\n return False\n return True","def isValidWords(word, hand, wordList):\n #tothand = 0\n #w = []\n # TO DO ... <-- Remove this comment when you code this function\n #for line in fil:\n # print line\n #if word not in w:\n # w.append(word)\n #print word\n #for h in hand:\n # global tothand\n # tothand += hand[h]\n #print tothand\n # if tothand == 0:\n if word in wordList:\n #print wordList\n print \"word found\"\n return True\n else:\n print \"Not a word\"\n return False\n \n #else:\n # return True","def isWordPartOf(self,word,wordlist):\n\t\tfor w in wordlist:\n\t\t\tif w in self._part_of_badword: \n\t\t\t\treturn True \t \n\t\t\t\tif w.startswith(word) or w.endswith(word):\n\t\t\t\t\tself._part_of_badword[w] = True \n\t\t\t\t\treturn True\n\t\treturn False","def is_validword(word, hand, word_list1):\n # TO DO ... <-- Remove this comment when you code this function\n word_list = []\n cnt_1 = 0\n for i in word:\n word_list += i.split(\",\")\n for i in word_list:\n if i in hand.keys():\n cnt_1 += 1\n if cnt_1 == len(word) and word in word_list1:\n score = get_word_score(word, n_num)\n update_hand(hand, word)\n else:\n print(\"Invalid Word\")","def isWord(wordList, word):\n word = word.lower()\n word = word.strip(\" !@#$%^&*()-_+={}[]|\\\\:;'<>?,./\\\"\")\n return word in wordList","def isWord(wordList, word):\n word = word.lower()\n word = word.strip(\" !@#$%^&*()-_+={}[]|\\\\:;'<>?,./\\\"\")\n return word in wordList","def isWord(wordList, word):\n word = word.lower()\n word = word.strip(\" !@#$%^&*()-_+={}[]|\\\\:;'<>?,./\\\"\")\n return word in wordList","def is_word_complete(word, guesses_list):\n\n check_list = [letter for letter in word if letter not in guesses_list]\n return check_list == []","def isValidWord(word, hand, wordList):\n # TO DO ... <-- Remove this comment when you code this function\n if word not in wordList:\n return False\n dic={}\n for k in hand:\n dic[k]=hand[k] \n for w in word:\n a=dic.get(w,0)\n if a > 0:\n dic[w]=a-1\n else:\n return False\n return True","def _validate_word(self, word):\n return type(word) == type('a') and set(self._letters) == set(list(word))","def check(self, word: str) -> bool:\n for s in (word, word.lower(), word.capitalize()):\n if s in self.words or s in self.ignored_words:\n return True\n return False","def is_word(wordlist, word):\n word = word.lower()\n word = word.strip(\" !@#$%^&*()-_+={}[]|\\:;'<>?,./\\\"\")\n return word in wordlist","def win(word_list):\n\n if '_' not in word_list:\n\n return True\n else:\n return False","def contains(self, word: Iterable[Terminal]) -> bool:\n return self._get_final_state(word) is not None","def isAlphabeticalWord(word, wordList):\n \n if (len(word) > 0):\n curr = word[0]\n for letter in word:\n if (curr > letter):\n return False\n else:\n curr = letter\n if wordList is None:\n return True\n return word in wordList","def check_word(words, word):\r\n if word in words:\r\n return True\r\n else:\r\n return False","def has_word(self, word):\n try:\n iterator = iter(str(word))\n for symbol in iterator:\n if symbol not in self:\n raise Exception(symbol + \" is not in alphabet\")\n except Exception as error:\n print('Alphabet Error:', error)\n return False\n else:\n return True","def check_word(self, word):\n first_letter, rest = word[0], word[1:]\n\n for possible_start in self._find_letter(first_letter):\n if self._check_word(possible_start, rest):\n return True\n\n return False","def onlyuse(word, letters):\r\n truth = True\r\n for letter in word:\r\n truth = letter in letters and truth\r\n return truth","def is_word_guessed(secret_word, letters_guessed):\n\n for letter in secret_word:\n if letter in letters_guessed:\n pass\n else:\n return False\n return True","def has_word(self, word)->bool:\n if len(word) == 1:\n chars = word + GNode.CHAR_EOW\n else:\n chars = word[0] + GNode.CHAR_REV + word[1:] + GNode.CHAR_EOW\n cursor = self.root\n for c in chars.lower():\n if c not in cursor.children:\n return False\n else:\n cursor = cursor.children[c]\n return True","def viableWord(self, word, filledInSpaces):\r\n \r\n # Check if it fits blanks\r\n for (index, letter) in filledInSpaces:\r\n if letter != word[index]:\r\n return False\r\n\r\n # Check if it fits unused\r\n for letter in word:\r\n if letter in self.wrongLetters:\r\n return False\r\n\r\n return True","def search(self, word):\n length = len(word)\n if length == 1:\n for letter in string.ascii_lowercase:\n key = \"{}/{}\".format(1, letter)\n if key in self.origin and letter != word:\n return True\n return False\n\n key = \"{}/{}\".format(len(word), word[0])\n ls = self.origin.get(key, [])\n if len(ls) == 0:\n return False\n\n for origin in ls:\n if self.only_modify_one_char(word, origin):\n return True\n return False","def word_check(word):\n word1 = word[1:]\n if word1 not in word_dict: return False\n if not homophones (word, word1): return False\n \n \n word2 = word[0] + word[2:]\n if word2 not in word_dict: return False\n if not homophones(word, word2): return False\n\n return True","def is_a_word(self, word):\n word = word.lower()\n if word in self.data:\n return True\n else:\n # for char in word:\n # if char.isnumeric():\n # return True\n word = list(word)\n numbers = len([x for x in word if x.isnumeric()])\n # # letters = len([x for x in word if x.isalpha()])\n if numbers >= 2 or numbers/len(word) > 0.4:\n return True\n return False","def test_is_valid_word(word_list):\n failure=False\n\n # test 1\n word = \"hello\"\n handOrig = get_frequency_dict(word)\n handCopy = handOrig.copy()\n\n if not is_valid_word(word, handCopy, word_list):\n print(\"FAILURE: test_is_valid_word()\")\n print(\"\\tExpected True, but got False for word: '\" + word + \"' and hand:\", handOrig)\n\n failure = True\n\n # Test a second time to see if word_list or hand has been modified\n if not is_valid_word(word, handCopy, word_list):\n print(\"FAILURE: test_is_valid_word()\")\n\n if handCopy != handOrig:\n print(\"\\tTesting word\", word, \"for a second time - be sure you're not modifying hand.\")\n print(\"\\tAt this point, hand ought to be\", handOrig, \"but it is\", handCopy)\n\n else:\n print(\"\\tTesting word\", word, \"for a second time - have you modified word_list?\")\n wordInWL = word in word_list\n print(\"The word\", word, \"should be in word_list - is it?\", wordInWL)\n\n print(\"\\tExpected True, but got False for word: '\" + word + \"' and hand:\", handCopy)\n\n failure = True\n\n\n # test 2\n hand = {'r': 1, 'a': 3, 'p': 2, 'e': 1, 't': 1, 'u':1}\n word = \"Rapture\"\n\n if is_valid_word(word, hand, word_list):\n print(\"FAILURE: test_is_valid_word()\")\n print(\"\\tExpected False, but got True for word: '\" + word + \"' and hand:\", hand)\n\n failure = True \n\n # test 3\n hand = {'n': 1, 'h': 1, 'o': 1, 'y': 1, 'd':1, 'w':1, 'e': 2}\n word = \"honey\"\n\n if not is_valid_word(word, hand, word_list):\n print(\"FAILURE: test_is_valid_word()\")\n print(\"\\tExpected True, but got False for word: '\"+ word +\"' and hand:\", hand)\n\n failure = True \n\n # test 4\n hand = {'r': 1, 'a': 3, 'p': 2, 't': 1, 'u':2}\n word = \"honey\"\n\n if is_valid_word(word, hand, word_list):\n print(\"FAILURE: test_is_valid_word()\")\n print(\"\\tExpected False, but got True for word: '\" + word + \"' and hand:\", hand)\n \n failure = True\n\n # test 5\n hand = {'e':1, 'v':2, 'n':1, 'i':1, 'l':2}\n word = \"EVIL\"\n \n if not is_valid_word(word, hand, word_list):\n print(\"FAILURE: test_is_valid_word()\")\n print(\"\\tExpected True, but got False for word: '\" + word + \"' and hand:\", hand)\n \n failure = True\n \n # test 6\n word = \"Even\"\n\n if is_valid_word(word, hand, word_list):\n print(\"FAILURE: test_is_valid_word()\")\n print(\"\\tExpected False, but got True for word: '\" + word + \"' and hand:\", hand)\n print(\"\\t(If this is the only failure, make sure is_valid_word() isn't mutating its inputs)\") \n \n failure = True \n\n if not failure:\n print(\"SUCCESS: test_is_valid_word()\")","def check_win(secret_word, old_letters_guessed):\n for i in secret_word:\n if i not in old_letters_guessed:\n return False\n # all letters from secret_word are in old_letters_guessed - the player won\n return True","def has_word(self, word):\n return word in self.word_set","def check_word(word):\r\n if word in word_master:\r\n valid = True\r\n else:\r\n valid = False\r\n return valid","def check_word(self, word):\n word = word.lower().strip()\n return not word or word in self.dictionary","def search(self, word: str) -> bool:\n curr_chars = self.chars\n for c in list(word):\n if c not in curr_chars:\n return False\n curr_chars = curr_chars[c]\n return self.end_of_word in curr_chars","def is_word_complete(word, guesses):\n # go through each letter of the word\n # if the letter is not in guesses then False\n # if you don't get any false, then you can consider True\n\n for letter in word:\n if letter not in guesses:\n return False\n return True","def is_word_guessed(secret_word, letters_guessed):\n for char in secret_word:\n flag = False\n for letter in letters_guessed:\n if char == letter:\n flag = True\n if flag == False:\n break\n\n return flag","def uses_only(word, available):\n for letter in word:\n if letter not in available:\n return False\n return True","def contains(self, word: str) -> bool:\n if not isinstance(word, str):\n raise TypeError('word argument must be str')\n\n current = self._base\n for character in word + '$':\n try:\n current = current[character]\n except KeyError:\n return False\n else:\n return True","def has_letter(word):\r\n for char in word:\r\n if char.isalpha():\r\n return True\r\n return False","def check_win(secret_word, old_letters_guessed):\n for letters_guessed in secret_word:\n if letters_guessed in old_letters_guessed:\n continue\n else:\n return False\n return True","def include_word(word, chardict):\n if (all(char in chardict.keys() for char in word)) & (len(word)<=25):\n # Some word2vec entries are all capitals and generally are acronyms.\n # This is unlikely to be learnable\n if not word.isupper():\n return True\n\n return False","def avoids (word, frbdn_letters):\n for letter in frbdn_letters:\n if letter in word: \n return False\n return True","def replacement_allowed(self, word):\n not_list = ['was', 'were', 'is', 'are', 'have', 'has', 'had']\n for not_word in not_list:\n if word == not_word:\n return False\n return True","def check_word(self, word):\n\n return self.graph.is_in(word)","def __contains__(self, word):\n if word in self.vocab:\n return True\n else:\n char_ngrams = compute_ngrams(word, self.min_n, self.max_n)\n return any(ng in self.ngrams for ng in char_ngrams)","def check_win(secret_word, old_letters_guessed):\n j=len(secret_word)\n for i in secret_word:\n j -= 1\n if i in old_letters_guessed:\n if j == 0:\n return True\n else: \n continue\n else:\n return False","def check_word(self, word):\n\n if not self.words:\n return None\n word = ''.join(word)\n return next((True for w in self.words if w == word), False)","def avoids(word, forbidden):\n for letter in word:\n if letter in forbidden:\n return False\n return True","def is_word(self, word):\r\n\r\n return self.data(word) is not None","def can_make_word(word, block_collection=blocks):\n if not word:\n return False\n\n blocks_remaining = block_collection[:]\n for char in word.upper():\n for block in blocks_remaining:\n if char in block:\n blocks_remaining.remove(block)\n break\n else:\n return False\n return True","def isWordSet(self):\n return len(self.getWord()) != 0","def search(self, word: str) -> bool:\n # Checking if the word is present in the list.\n return word in self.mylist","def match(self, words):\n return words == self.words(len(words))","def words_in_dictionary(word_list):\n for word in word_list:\n word = word.lower()\n raw_word = word.replace(\"'\", '').replace('.', '')\n if word not in DICTIONARY_LOWER and raw_word not in DICTIONARY_LOWER:\n return False\n return True","def uses_all(w, letters):\n\treturn set(letters).issubset(set(w))","def has_special_chars(word):\n for ch in constants.SPECIAL_CHARS:\n if ch in word:\n return True\n if not any(c.isalpha() for c in word):\n return True\n return False","def contains(self, word: Iterable[Terminal]) -> bool:\n # Remove epsilons\n word = [to_terminal(x) for x in word if x != Epsilon()]\n if not word:\n return self.generate_epsilon()\n cyk_table = CYKTable(self, word)\n return cyk_table.generate_word()","def check_win(secret_word, old_letters_guessed):\n returned_list = list(secret_word)\n for i in range(len(secret_word)):\n if secret_word[i] in old_letters_guessed:\n returned_list[i] = secret_word[i] + ' '\n else:\n returned_list[i] = '_ '\n returned_str = ''.join(returned_list)\n\n string_no_spaces = returned_str.replace(\" \", \"\")\n if string_no_spaces == secret_word:\n return True\n else:\n return False","def uses_only(w, letters):\n\treturn set(w).issubset(set(letters))","def letter_locations_good(word, letters):\n goods = []\n for letter, indices in letters.items():\n # Is the letter in the word at all?\n goods.append(letter in word)\n\n # If the letter's position is known, is it in the right place?\n if indices[\"yes\"] is not None:\n goods.append(word[indices[\"yes\"]] == letter)\n\n # If there are excluded spots for the letter, is it NOT there in this word?\n if indices[\"no\"]:\n for index in indices[\"no\"]:\n goods.append(word[index] != letter)\n return all(goods)","def _check_pauli_word(pauli_word):\n return all(pauli in PauliRot._ALLOWED_CHARACTERS for pauli in set(pauli_word))","def search(self, word):\n for wc in self.get_wildcards(word):\n # Don't forget word not in self.all_words\n if wc in self.wc_dict and (self.wc_dict[wc] > 1 or word not in self.all_words) :\n return True\n return False","def isPresent(self, word):\n\t\treturn word in self.link_words","def accepts(self, word: Iterable[str]) -> bool:\n if self._enfa is None:\n self._enfa = self.to_epsilon_nfa()\n return self._enfa.accepts(word)","def checkWord(word):\r\n check = word in cachedWordList\r\n if check:\r\n print(word + \" spelt correctly\")\r\n else:\r\n print(word + \" not found in dictionary\")\r\n return check","def valid(phrase):\n words = []\n series_of_words = phrase.split(' ')\n words.append(series_of_words.pop())\n for word in series_of_words:\n if word in words:\n return False\n words.append(word)\n return True","def been_played(self, word):\n words = self.played_out or ''\n words = words.split(' ')\n\n return True if ((words.count(word) > 0) or \n (words.count( singularize(word) ) > 0) or \n (words.count( pluralize(word) ) > 0)) else False","def check_word(self, line, sp_ch='*'):\n valid = []\n le = cross.get_length()\n for n in range(le):\n t1 = \"\".join(line)\n t2 = cross.get_word(n)\n if len(t1) == len(t2):\n z = zip(t1, t2)\n for x in z:\n if (x[0] != x[1]) and (x[0] != sp_ch):\n break\n else:\n word_as_list = list(t2)\n valid.append(word_as_list)\n return valid","def Has(cls, word_list):\n entity = WordList.get_by_id(word_list)\n if entity:\n return True\n return False","def word_in_list(word_list):\n word_set = set(word_list)\n inp_word = \"\"\n while inp_word != \"/q\":\n if inp_word == \"/q\":\n break\n inp_word = input(\"What word do you want to check? ('/q' to stop) > \")\n if inp_word in word_set:\n print(f\"Word '{inp_word}' is in the list!\")\n else:\n print(f\"Cannot find word '{inp_word}' in the list.\")","def _internal_contains(self, letters):\n len_letters = len(letters)\n for word in self.words:\n len_word = len(word)\n word_with_letters_removed = WordUtils.remove_letters_from_word(word, letters)\n if len(word_with_letters_removed) == (len_word - len_letters):\n yield word","def search(self, word: str) -> bool:\n cur = self.root\n for letter in word:\n if letter not in cur:\n return False\n cur = cur[letter]\n if \"isWord\" not in cur:\n return False\n return True","def exist(self, board: List[List[str]], word: str) -> bool:\n for row in range(0, len(board)):\n for col in range(0, len(board[0])):\n board_copy = deepcopy(board)\n if self.existRecu(board_copy, word, row, col):\n return True\n return False","def search(self, word):\n pointer = self.tries\n for i in range(len(word)):\n ascii = ord(word[i]) - ord('a')\n if pointer[ascii] == None:\n return False\n pointer = pointer[ascii]\n if word in pointer[26:]:\n return True\n else:\n return False","def search(self, word):\n if len(word) not in self.length_set:\n return False\n for i in self.mutate(word):\n if i in self.s:\n return True\n return False","def can_make_word(word, letters):\n grouped_chars = group_input(word)\n for char in letters:\n\n if is_empty(grouped_chars):\n return True\n\n if char in grouped_chars and grouped_chars[char] > 0:\n grouped_chars[char] -= 1\n\n return is_empty(grouped_chars)","def contains (self,phrase,chars):\r\n\r\n for x in chars:\r\n\r\n if x in phrase:\r\n return True\r\n return False","def verify(self, word):\n if len(word) < 2:\n return (True, word)\n\n if word.lower() in self.replacement_words.keys():\n return (True, self.replacement_words[word.lower()])\n\n if word.lower() in self.word_list:\n return (True, word)\n\n if word.lower() in self.ignored_words:\n return (True, word)\n\n return (False, word)","def check_inventory(self, check_word):\n is_there = False\n for thing in self.bag_of_holding:\n if check_word == thing.name:\n is_there = True\n\n return is_there","def check_word(word):\n\n return bool(re.match(r'^[a-z]+$', word))","def triable(state_of_play, word, guesses):\n if len(state_of_play) != len(word):\n return False\n for i in range(len(word)):\n if state_of_play[i]!=\" \" and state_of_play[i]!=word[i]:\n return False\n if state_of_play[i]==\" \" and word[i] in guesses:\n return False\n return True","def _is_real_word(self, token):\n return not (token in self._non_words)","def checkWord(self, word):\n toFill = int(tapeLimit - len(word) / 2)\n tape = [self.blank] * toFill + list(word) + [self.blank] * toFill\n\n state = self.initialState\n pos = toFill\n\n while state != self.acceptState and state != self.rejectState:\n transition = self.transitions[(state, tape[pos])]\n state = transition[0]\n tape[pos] = transition[1]\n\n if transition[2] == '>':\n pos += 1\n elif transition[2] == '<':\n pos -= 1\n\n return state == self.acceptState","def in_bisect(word_list, word):\n\ti = bisect_left(word_list, word)\n\tif i != len(word_list) and word_list[i] == word:\n\t\treturn True\n\telse:\n\t\treturn False","def _does_words_matches(original_word: str, encoded_word: str) -> bool:\n return(\n len(original_word) == len(encoded_word) and\n original_word[0] == encoded_word[0] and\n original_word[-1] == encoded_word[-1] and\n sorted(original_word[1:-1]) == sorted(encoded_word[1:-1])\n )","def check_present_and_add(self, word):\n\n current_node = self.root_node\n is_new_word = False\n\n # iterate through trie adding missing notes\n for char in word:\n if char not in current_node:\n is_new_word = True\n current_node[char] = {}\n current_node = current_node[char]\n \n # mark end of word so that words that are prefixes of present words are not\n # returned - i.e. each word must have an explicit \"End of Word\" marker\n if \"End of Word\" not in current_node:\n is_new_word = True\n current_node[\"End on Word\"] = {}\n\n return is_new_word","def is_cap_word(self, word):\n try:\n return word[0].isupper()\n except:\n return False","def isUnique(self, word):\n abbr = self.gen_abbr(word)\n\n if abbr not in self.dict:\n return True\n elif len(self.dict[abbr]) == 1 and word in self.dict[abbr]:\n return True\n else:\n return False","def search(self, word):\n length = len(word)\n if length not in self.dic:\n return False\n else:\n candidateList = self.dic[length]\n for candidate in candidateList:\n for i in xrange(length):\n if candidate[i] != word[i]:\n if candidate[i+1:] == word[i+1:]:\n return True\n else:\n break\n return False","def _check_add_entities(self, word: str, type_: str) -> bool:\n\n if self.check_known and self.check_unknown:\n return True\n elif self.check_known and word in self.known_words[type_]:\n return True\n elif self.check_unknown and word not in self.known_words[type_]:\n return True\n return False"],"string":"[\n \"def isValidWord(word, hand, wordList):\\r\\n \\r\\n \\r\\n if type(word) != str:\\r\\n return False\\r\\n \\r\\n if word not in wordList:\\r\\n return False\\r\\n \\r\\n for letter in word:\\r\\n if letter not in hand:\\r\\n return False\\r\\n \\r\\n updatedHand = hand.copy()\\r\\n # copy hand as to not mutate the hand passed in\\r\\n \\r\\n for letter in word:\\r\\n if letter in updatedHand:\\r\\n updatedHand[letter] -= 1\\r\\n \\r\\n for letter in word:\\r\\n if updatedHand[letter] < 0:\\r\\n return False\\r\\n \\r\\n return True\",\n \"def isValidWord(word, hand, wordList):\\n wordTest = word.lower()\\n handcopy = hand.copy()\\n for i in wordTest:\\n if i not in 'abcdefghijklmnopqrstuvwxyz':\\n return False\\n if wordTest not in wordList:\\n return False\\n else:\\n for i in word:\\n if i not in handcopy:\\n return False\\n else:\\n if handcopy[i]==0:\\n return False\\n if handcopy.get(i,0) !=0:\\n handcopy[i]-=1\\n if handcopy.get(i,0)<0:\\n return False\\n return True\",\n \"def is_valid_word(word, hand, word_list):\\r\\n # TO DO ...\\r\\n word_freq_dict = get_frequency_dict(word)\\r\\n for letter in word_freq_dict:\\r\\n if letter not in hand or hand[letter] - word_freq_dict[letter] < 0:\\r\\n return False\\r\\n return word in word_list\",\n \"def isValidWord(word: str, hand: Dict[str, int], wordList: List[str]) -> bool:\\n cp_hand = hand.copy()\\n\\n if word not in wordList:\\n return False\\n\\n for char in word:\\n if cp_hand.get(char, 0) < 1:\\n return False\\n else:\\n cp_hand[char] = cp_hand.get(char,0) - 1\\n\\n return True\\n # one line:\\n # return word in wordList and all(word.count(c) <= hand.get(c, 0) \\n # for c in word) # Kiwitrader\",\n \"def isValidWord(word, hand, wordList):\\n myHand = hand.copy() \\n for letter in word:\\n if letter not in hand or myHand[letter] <= 0:\\n return False\\n else:\\n myHand[letter] -= 1 \\n if word not in wordList:\\n return False\\n else:\\n return True\",\n \"def isValidWord(word, hand, wordList):\\n tempHand = hand.copy()\\n if len(word) > 0 and word in wordList:\\n for letter in word:\\n if letter not in tempHand or tempHand[letter] <= 0:\\n return False\\n else:\\n tempHand[letter] = tempHand.get(letter, 0) - 1\\n return True\\n return False\",\n \"def isValidWord(word, hand, wordList):\\n if word not in wordList:\\n print('word not in wordList')\\n return False\\n else:\\n for letter in word:\\n if letter not in hand:\\n print('letter not in hand')\\n return False\\n else:\\n newHand = hand.copy()\\n for letter in word:\\n if letter not in newHand: # this if statement is here to prevent keyError\\n print('letter not in newHand')\\n return False\\n else:\\n newHand[letter] = newHand[letter] - 1\\n if newHand[letter] < 0:\\n print('no letter left')\\n return False\\n return True\",\n \"def isValidWord(word, hand, wordList):\\n output = hand.copy()\\n word_check = False\\n if word in wordList:\\n word_check = True\\n \\n letter_check = set(list(word)) <= set(output.keys())\\n \\n for letter in word:\\n if letter in output.keys():\\n output[letter] -= 1\\n \\n value_check = all(i >= 0 for i in output.values())\\n \\n if word_check == True and letter_check == True and value_check == True:\\n return True\\n else:\\n return False\",\n \"def is_valid_word(word, hand, word_list):\\n failure=True\\n word=word.lower()\\n if word not in word_list:\\n failure=False\\n for i in word:\\n w=hand.get(i,0)\\n if w==0:\\n failure=False\\n break\\n return failure\",\n \"def isValidWord(word, hand, wordList):\\n status = []\\n status2 = []\\n\\n # check if the input is valid\\n if not word in wordList:\\n return False\\n \\n for letter in word:\\n \\n ## commented out bc global var not available for the individual exercise\\n # if not letter in SCRABBLE_LETTER_VALUES.keys():\\n # status.append(False)\\n \\n if not letter in hand.keys():\\n status.append(False)\\n \\n if False in status:\\n return False\\n \\n # check if there are enough values in hand for the guess\\n for key, value in getFrequencyDict(word).items():\\n \\n if not key in hand or value > hand[key]:\\n status2.append(False)\\n\\n if False in status2:\\n return False\\n \\n return True\",\n \"def isValidWord(word, hand, wordList):\\n # TO DO ... <-- Remove this comment when you code this function\\n #for line in fil:\\n # print line\\n if word not in w:\\n w.append(word)\\n #print w\\n else:\\n #print \\\"already have a word inside\\\"\\n return False\\n for z in word:\\n print z\\n if z in hand:\\n #if p != word:\\n #print hand[c]\\n if hand[z] > 0:\\n hand[z] -= 1\\n \\n else:\\n return False\\n else:\\n return False\\n if word in wordList:\\n print \\\"here is true\\\"\\n return True\\n else:\\n return False\",\n \"def is_valid_word(word_test, hand_word, word_list):\\n count = 0\\n word_test = list(word_test)\\n for i_1 in word_test:\\n if i_1 in hand_word:\\n count += count\\n if count == len(word_test) and word_test[i_1] == word_list[i_1]:\\n return True\\n return False\",\n \"def isvalid_word1(word, hand, word_list1):\\n # TO DO ... <-- Remove this comment when you code this function\\n for i in word:\\n if i not in hand:\\n return False\\n i = i\\n return word in word_list1\",\n \"def is_valid_word(word, hand, word_list):\\n h = dict(hand)\\n if word.lower() in word_list:\\n for letter in word:\\n if h.get(letter, 0) == 0:\\n return False\\n else:\\n h[letter] -= 1\\n else:\\n return False\\n return True\",\n \"def isValidWords(word, hand, wordList):\\n #tothand = 0\\n #w = []\\n # TO DO ... <-- Remove this comment when you code this function\\n #for line in fil:\\n # print line\\n #if word not in w:\\n # w.append(word)\\n #print word\\n #for h in hand:\\n # global tothand\\n # tothand += hand[h]\\n #print tothand\\n # if tothand == 0:\\n if word in wordList:\\n #print wordList\\n print \\\"word found\\\"\\n return True\\n else:\\n print \\\"Not a word\\\"\\n return False\\n \\n #else:\\n # return True\",\n \"def isWordPartOf(self,word,wordlist):\\n\\t\\tfor w in wordlist:\\n\\t\\t\\tif w in self._part_of_badword: \\n\\t\\t\\t\\treturn True \\t \\n\\t\\t\\t\\tif w.startswith(word) or w.endswith(word):\\n\\t\\t\\t\\t\\tself._part_of_badword[w] = True \\n\\t\\t\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"def is_validword(word, hand, word_list1):\\n # TO DO ... <-- Remove this comment when you code this function\\n word_list = []\\n cnt_1 = 0\\n for i in word:\\n word_list += i.split(\\\",\\\")\\n for i in word_list:\\n if i in hand.keys():\\n cnt_1 += 1\\n if cnt_1 == len(word) and word in word_list1:\\n score = get_word_score(word, n_num)\\n update_hand(hand, word)\\n else:\\n print(\\\"Invalid Word\\\")\",\n \"def isWord(wordList, word):\\n word = word.lower()\\n word = word.strip(\\\" !@#$%^&*()-_+={}[]|\\\\\\\\:;'<>?,./\\\\\\\"\\\")\\n return word in wordList\",\n \"def isWord(wordList, word):\\n word = word.lower()\\n word = word.strip(\\\" !@#$%^&*()-_+={}[]|\\\\\\\\:;'<>?,./\\\\\\\"\\\")\\n return word in wordList\",\n \"def isWord(wordList, word):\\n word = word.lower()\\n word = word.strip(\\\" !@#$%^&*()-_+={}[]|\\\\\\\\:;'<>?,./\\\\\\\"\\\")\\n return word in wordList\",\n \"def is_word_complete(word, guesses_list):\\n\\n check_list = [letter for letter in word if letter not in guesses_list]\\n return check_list == []\",\n \"def isValidWord(word, hand, wordList):\\n # TO DO ... <-- Remove this comment when you code this function\\n if word not in wordList:\\n return False\\n dic={}\\n for k in hand:\\n dic[k]=hand[k] \\n for w in word:\\n a=dic.get(w,0)\\n if a > 0:\\n dic[w]=a-1\\n else:\\n return False\\n return True\",\n \"def _validate_word(self, word):\\n return type(word) == type('a') and set(self._letters) == set(list(word))\",\n \"def check(self, word: str) -> bool:\\n for s in (word, word.lower(), word.capitalize()):\\n if s in self.words or s in self.ignored_words:\\n return True\\n return False\",\n \"def is_word(wordlist, word):\\n word = word.lower()\\n word = word.strip(\\\" !@#$%^&*()-_+={}[]|\\\\:;'<>?,./\\\\\\\"\\\")\\n return word in wordlist\",\n \"def win(word_list):\\n\\n if '_' not in word_list:\\n\\n return True\\n else:\\n return False\",\n \"def contains(self, word: Iterable[Terminal]) -> bool:\\n return self._get_final_state(word) is not None\",\n \"def isAlphabeticalWord(word, wordList):\\n \\n if (len(word) > 0):\\n curr = word[0]\\n for letter in word:\\n if (curr > letter):\\n return False\\n else:\\n curr = letter\\n if wordList is None:\\n return True\\n return word in wordList\",\n \"def check_word(words, word):\\r\\n if word in words:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def has_word(self, word):\\n try:\\n iterator = iter(str(word))\\n for symbol in iterator:\\n if symbol not in self:\\n raise Exception(symbol + \\\" is not in alphabet\\\")\\n except Exception as error:\\n print('Alphabet Error:', error)\\n return False\\n else:\\n return True\",\n \"def check_word(self, word):\\n first_letter, rest = word[0], word[1:]\\n\\n for possible_start in self._find_letter(first_letter):\\n if self._check_word(possible_start, rest):\\n return True\\n\\n return False\",\n \"def onlyuse(word, letters):\\r\\n truth = True\\r\\n for letter in word:\\r\\n truth = letter in letters and truth\\r\\n return truth\",\n \"def is_word_guessed(secret_word, letters_guessed):\\n\\n for letter in secret_word:\\n if letter in letters_guessed:\\n pass\\n else:\\n return False\\n return True\",\n \"def has_word(self, word)->bool:\\n if len(word) == 1:\\n chars = word + GNode.CHAR_EOW\\n else:\\n chars = word[0] + GNode.CHAR_REV + word[1:] + GNode.CHAR_EOW\\n cursor = self.root\\n for c in chars.lower():\\n if c not in cursor.children:\\n return False\\n else:\\n cursor = cursor.children[c]\\n return True\",\n \"def viableWord(self, word, filledInSpaces):\\r\\n \\r\\n # Check if it fits blanks\\r\\n for (index, letter) in filledInSpaces:\\r\\n if letter != word[index]:\\r\\n return False\\r\\n\\r\\n # Check if it fits unused\\r\\n for letter in word:\\r\\n if letter in self.wrongLetters:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def search(self, word):\\n length = len(word)\\n if length == 1:\\n for letter in string.ascii_lowercase:\\n key = \\\"{}/{}\\\".format(1, letter)\\n if key in self.origin and letter != word:\\n return True\\n return False\\n\\n key = \\\"{}/{}\\\".format(len(word), word[0])\\n ls = self.origin.get(key, [])\\n if len(ls) == 0:\\n return False\\n\\n for origin in ls:\\n if self.only_modify_one_char(word, origin):\\n return True\\n return False\",\n \"def word_check(word):\\n word1 = word[1:]\\n if word1 not in word_dict: return False\\n if not homophones (word, word1): return False\\n \\n \\n word2 = word[0] + word[2:]\\n if word2 not in word_dict: return False\\n if not homophones(word, word2): return False\\n\\n return True\",\n \"def is_a_word(self, word):\\n word = word.lower()\\n if word in self.data:\\n return True\\n else:\\n # for char in word:\\n # if char.isnumeric():\\n # return True\\n word = list(word)\\n numbers = len([x for x in word if x.isnumeric()])\\n # # letters = len([x for x in word if x.isalpha()])\\n if numbers >= 2 or numbers/len(word) > 0.4:\\n return True\\n return False\",\n \"def test_is_valid_word(word_list):\\n failure=False\\n\\n # test 1\\n word = \\\"hello\\\"\\n handOrig = get_frequency_dict(word)\\n handCopy = handOrig.copy()\\n\\n if not is_valid_word(word, handCopy, word_list):\\n print(\\\"FAILURE: test_is_valid_word()\\\")\\n print(\\\"\\\\tExpected True, but got False for word: '\\\" + word + \\\"' and hand:\\\", handOrig)\\n\\n failure = True\\n\\n # Test a second time to see if word_list or hand has been modified\\n if not is_valid_word(word, handCopy, word_list):\\n print(\\\"FAILURE: test_is_valid_word()\\\")\\n\\n if handCopy != handOrig:\\n print(\\\"\\\\tTesting word\\\", word, \\\"for a second time - be sure you're not modifying hand.\\\")\\n print(\\\"\\\\tAt this point, hand ought to be\\\", handOrig, \\\"but it is\\\", handCopy)\\n\\n else:\\n print(\\\"\\\\tTesting word\\\", word, \\\"for a second time - have you modified word_list?\\\")\\n wordInWL = word in word_list\\n print(\\\"The word\\\", word, \\\"should be in word_list - is it?\\\", wordInWL)\\n\\n print(\\\"\\\\tExpected True, but got False for word: '\\\" + word + \\\"' and hand:\\\", handCopy)\\n\\n failure = True\\n\\n\\n # test 2\\n hand = {'r': 1, 'a': 3, 'p': 2, 'e': 1, 't': 1, 'u':1}\\n word = \\\"Rapture\\\"\\n\\n if is_valid_word(word, hand, word_list):\\n print(\\\"FAILURE: test_is_valid_word()\\\")\\n print(\\\"\\\\tExpected False, but got True for word: '\\\" + word + \\\"' and hand:\\\", hand)\\n\\n failure = True \\n\\n # test 3\\n hand = {'n': 1, 'h': 1, 'o': 1, 'y': 1, 'd':1, 'w':1, 'e': 2}\\n word = \\\"honey\\\"\\n\\n if not is_valid_word(word, hand, word_list):\\n print(\\\"FAILURE: test_is_valid_word()\\\")\\n print(\\\"\\\\tExpected True, but got False for word: '\\\"+ word +\\\"' and hand:\\\", hand)\\n\\n failure = True \\n\\n # test 4\\n hand = {'r': 1, 'a': 3, 'p': 2, 't': 1, 'u':2}\\n word = \\\"honey\\\"\\n\\n if is_valid_word(word, hand, word_list):\\n print(\\\"FAILURE: test_is_valid_word()\\\")\\n print(\\\"\\\\tExpected False, but got True for word: '\\\" + word + \\\"' and hand:\\\", hand)\\n \\n failure = True\\n\\n # test 5\\n hand = {'e':1, 'v':2, 'n':1, 'i':1, 'l':2}\\n word = \\\"EVIL\\\"\\n \\n if not is_valid_word(word, hand, word_list):\\n print(\\\"FAILURE: test_is_valid_word()\\\")\\n print(\\\"\\\\tExpected True, but got False for word: '\\\" + word + \\\"' and hand:\\\", hand)\\n \\n failure = True\\n \\n # test 6\\n word = \\\"Even\\\"\\n\\n if is_valid_word(word, hand, word_list):\\n print(\\\"FAILURE: test_is_valid_word()\\\")\\n print(\\\"\\\\tExpected False, but got True for word: '\\\" + word + \\\"' and hand:\\\", hand)\\n print(\\\"\\\\t(If this is the only failure, make sure is_valid_word() isn't mutating its inputs)\\\") \\n \\n failure = True \\n\\n if not failure:\\n print(\\\"SUCCESS: test_is_valid_word()\\\")\",\n \"def check_win(secret_word, old_letters_guessed):\\n for i in secret_word:\\n if i not in old_letters_guessed:\\n return False\\n # all letters from secret_word are in old_letters_guessed - the player won\\n return True\",\n \"def has_word(self, word):\\n return word in self.word_set\",\n \"def check_word(word):\\r\\n if word in word_master:\\r\\n valid = True\\r\\n else:\\r\\n valid = False\\r\\n return valid\",\n \"def check_word(self, word):\\n word = word.lower().strip()\\n return not word or word in self.dictionary\",\n \"def search(self, word: str) -> bool:\\n curr_chars = self.chars\\n for c in list(word):\\n if c not in curr_chars:\\n return False\\n curr_chars = curr_chars[c]\\n return self.end_of_word in curr_chars\",\n \"def is_word_complete(word, guesses):\\n # go through each letter of the word\\n # if the letter is not in guesses then False\\n # if you don't get any false, then you can consider True\\n\\n for letter in word:\\n if letter not in guesses:\\n return False\\n return True\",\n \"def is_word_guessed(secret_word, letters_guessed):\\n for char in secret_word:\\n flag = False\\n for letter in letters_guessed:\\n if char == letter:\\n flag = True\\n if flag == False:\\n break\\n\\n return flag\",\n \"def uses_only(word, available):\\n for letter in word:\\n if letter not in available:\\n return False\\n return True\",\n \"def contains(self, word: str) -> bool:\\n if not isinstance(word, str):\\n raise TypeError('word argument must be str')\\n\\n current = self._base\\n for character in word + '$':\\n try:\\n current = current[character]\\n except KeyError:\\n return False\\n else:\\n return True\",\n \"def has_letter(word):\\r\\n for char in word:\\r\\n if char.isalpha():\\r\\n return True\\r\\n return False\",\n \"def check_win(secret_word, old_letters_guessed):\\n for letters_guessed in secret_word:\\n if letters_guessed in old_letters_guessed:\\n continue\\n else:\\n return False\\n return True\",\n \"def include_word(word, chardict):\\n if (all(char in chardict.keys() for char in word)) & (len(word)<=25):\\n # Some word2vec entries are all capitals and generally are acronyms.\\n # This is unlikely to be learnable\\n if not word.isupper():\\n return True\\n\\n return False\",\n \"def avoids (word, frbdn_letters):\\n for letter in frbdn_letters:\\n if letter in word: \\n return False\\n return True\",\n \"def replacement_allowed(self, word):\\n not_list = ['was', 'were', 'is', 'are', 'have', 'has', 'had']\\n for not_word in not_list:\\n if word == not_word:\\n return False\\n return True\",\n \"def check_word(self, word):\\n\\n return self.graph.is_in(word)\",\n \"def __contains__(self, word):\\n if word in self.vocab:\\n return True\\n else:\\n char_ngrams = compute_ngrams(word, self.min_n, self.max_n)\\n return any(ng in self.ngrams for ng in char_ngrams)\",\n \"def check_win(secret_word, old_letters_guessed):\\n j=len(secret_word)\\n for i in secret_word:\\n j -= 1\\n if i in old_letters_guessed:\\n if j == 0:\\n return True\\n else: \\n continue\\n else:\\n return False\",\n \"def check_word(self, word):\\n\\n if not self.words:\\n return None\\n word = ''.join(word)\\n return next((True for w in self.words if w == word), False)\",\n \"def avoids(word, forbidden):\\n for letter in word:\\n if letter in forbidden:\\n return False\\n return True\",\n \"def is_word(self, word):\\r\\n\\r\\n return self.data(word) is not None\",\n \"def can_make_word(word, block_collection=blocks):\\n if not word:\\n return False\\n\\n blocks_remaining = block_collection[:]\\n for char in word.upper():\\n for block in blocks_remaining:\\n if char in block:\\n blocks_remaining.remove(block)\\n break\\n else:\\n return False\\n return True\",\n \"def isWordSet(self):\\n return len(self.getWord()) != 0\",\n \"def search(self, word: str) -> bool:\\n # Checking if the word is present in the list.\\n return word in self.mylist\",\n \"def match(self, words):\\n return words == self.words(len(words))\",\n \"def words_in_dictionary(word_list):\\n for word in word_list:\\n word = word.lower()\\n raw_word = word.replace(\\\"'\\\", '').replace('.', '')\\n if word not in DICTIONARY_LOWER and raw_word not in DICTIONARY_LOWER:\\n return False\\n return True\",\n \"def uses_all(w, letters):\\n\\treturn set(letters).issubset(set(w))\",\n \"def has_special_chars(word):\\n for ch in constants.SPECIAL_CHARS:\\n if ch in word:\\n return True\\n if not any(c.isalpha() for c in word):\\n return True\\n return False\",\n \"def contains(self, word: Iterable[Terminal]) -> bool:\\n # Remove epsilons\\n word = [to_terminal(x) for x in word if x != Epsilon()]\\n if not word:\\n return self.generate_epsilon()\\n cyk_table = CYKTable(self, word)\\n return cyk_table.generate_word()\",\n \"def check_win(secret_word, old_letters_guessed):\\n returned_list = list(secret_word)\\n for i in range(len(secret_word)):\\n if secret_word[i] in old_letters_guessed:\\n returned_list[i] = secret_word[i] + ' '\\n else:\\n returned_list[i] = '_ '\\n returned_str = ''.join(returned_list)\\n\\n string_no_spaces = returned_str.replace(\\\" \\\", \\\"\\\")\\n if string_no_spaces == secret_word:\\n return True\\n else:\\n return False\",\n \"def uses_only(w, letters):\\n\\treturn set(w).issubset(set(letters))\",\n \"def letter_locations_good(word, letters):\\n goods = []\\n for letter, indices in letters.items():\\n # Is the letter in the word at all?\\n goods.append(letter in word)\\n\\n # If the letter's position is known, is it in the right place?\\n if indices[\\\"yes\\\"] is not None:\\n goods.append(word[indices[\\\"yes\\\"]] == letter)\\n\\n # If there are excluded spots for the letter, is it NOT there in this word?\\n if indices[\\\"no\\\"]:\\n for index in indices[\\\"no\\\"]:\\n goods.append(word[index] != letter)\\n return all(goods)\",\n \"def _check_pauli_word(pauli_word):\\n return all(pauli in PauliRot._ALLOWED_CHARACTERS for pauli in set(pauli_word))\",\n \"def search(self, word):\\n for wc in self.get_wildcards(word):\\n # Don't forget word not in self.all_words\\n if wc in self.wc_dict and (self.wc_dict[wc] > 1 or word not in self.all_words) :\\n return True\\n return False\",\n \"def isPresent(self, word):\\n\\t\\treturn word in self.link_words\",\n \"def accepts(self, word: Iterable[str]) -> bool:\\n if self._enfa is None:\\n self._enfa = self.to_epsilon_nfa()\\n return self._enfa.accepts(word)\",\n \"def checkWord(word):\\r\\n check = word in cachedWordList\\r\\n if check:\\r\\n print(word + \\\" spelt correctly\\\")\\r\\n else:\\r\\n print(word + \\\" not found in dictionary\\\")\\r\\n return check\",\n \"def valid(phrase):\\n words = []\\n series_of_words = phrase.split(' ')\\n words.append(series_of_words.pop())\\n for word in series_of_words:\\n if word in words:\\n return False\\n words.append(word)\\n return True\",\n \"def been_played(self, word):\\n words = self.played_out or ''\\n words = words.split(' ')\\n\\n return True if ((words.count(word) > 0) or \\n (words.count( singularize(word) ) > 0) or \\n (words.count( pluralize(word) ) > 0)) else False\",\n \"def check_word(self, line, sp_ch='*'):\\n valid = []\\n le = cross.get_length()\\n for n in range(le):\\n t1 = \\\"\\\".join(line)\\n t2 = cross.get_word(n)\\n if len(t1) == len(t2):\\n z = zip(t1, t2)\\n for x in z:\\n if (x[0] != x[1]) and (x[0] != sp_ch):\\n break\\n else:\\n word_as_list = list(t2)\\n valid.append(word_as_list)\\n return valid\",\n \"def Has(cls, word_list):\\n entity = WordList.get_by_id(word_list)\\n if entity:\\n return True\\n return False\",\n \"def word_in_list(word_list):\\n word_set = set(word_list)\\n inp_word = \\\"\\\"\\n while inp_word != \\\"/q\\\":\\n if inp_word == \\\"/q\\\":\\n break\\n inp_word = input(\\\"What word do you want to check? ('/q' to stop) > \\\")\\n if inp_word in word_set:\\n print(f\\\"Word '{inp_word}' is in the list!\\\")\\n else:\\n print(f\\\"Cannot find word '{inp_word}' in the list.\\\")\",\n \"def _internal_contains(self, letters):\\n len_letters = len(letters)\\n for word in self.words:\\n len_word = len(word)\\n word_with_letters_removed = WordUtils.remove_letters_from_word(word, letters)\\n if len(word_with_letters_removed) == (len_word - len_letters):\\n yield word\",\n \"def search(self, word: str) -> bool:\\n cur = self.root\\n for letter in word:\\n if letter not in cur:\\n return False\\n cur = cur[letter]\\n if \\\"isWord\\\" not in cur:\\n return False\\n return True\",\n \"def exist(self, board: List[List[str]], word: str) -> bool:\\n for row in range(0, len(board)):\\n for col in range(0, len(board[0])):\\n board_copy = deepcopy(board)\\n if self.existRecu(board_copy, word, row, col):\\n return True\\n return False\",\n \"def search(self, word):\\n pointer = self.tries\\n for i in range(len(word)):\\n ascii = ord(word[i]) - ord('a')\\n if pointer[ascii] == None:\\n return False\\n pointer = pointer[ascii]\\n if word in pointer[26:]:\\n return True\\n else:\\n return False\",\n \"def search(self, word):\\n if len(word) not in self.length_set:\\n return False\\n for i in self.mutate(word):\\n if i in self.s:\\n return True\\n return False\",\n \"def can_make_word(word, letters):\\n grouped_chars = group_input(word)\\n for char in letters:\\n\\n if is_empty(grouped_chars):\\n return True\\n\\n if char in grouped_chars and grouped_chars[char] > 0:\\n grouped_chars[char] -= 1\\n\\n return is_empty(grouped_chars)\",\n \"def contains (self,phrase,chars):\\r\\n\\r\\n for x in chars:\\r\\n\\r\\n if x in phrase:\\r\\n return True\\r\\n return False\",\n \"def verify(self, word):\\n if len(word) < 2:\\n return (True, word)\\n\\n if word.lower() in self.replacement_words.keys():\\n return (True, self.replacement_words[word.lower()])\\n\\n if word.lower() in self.word_list:\\n return (True, word)\\n\\n if word.lower() in self.ignored_words:\\n return (True, word)\\n\\n return (False, word)\",\n \"def check_inventory(self, check_word):\\n is_there = False\\n for thing in self.bag_of_holding:\\n if check_word == thing.name:\\n is_there = True\\n\\n return is_there\",\n \"def check_word(word):\\n\\n return bool(re.match(r'^[a-z]+$', word))\",\n \"def triable(state_of_play, word, guesses):\\n if len(state_of_play) != len(word):\\n return False\\n for i in range(len(word)):\\n if state_of_play[i]!=\\\" \\\" and state_of_play[i]!=word[i]:\\n return False\\n if state_of_play[i]==\\\" \\\" and word[i] in guesses:\\n return False\\n return True\",\n \"def _is_real_word(self, token):\\n return not (token in self._non_words)\",\n \"def checkWord(self, word):\\n toFill = int(tapeLimit - len(word) / 2)\\n tape = [self.blank] * toFill + list(word) + [self.blank] * toFill\\n\\n state = self.initialState\\n pos = toFill\\n\\n while state != self.acceptState and state != self.rejectState:\\n transition = self.transitions[(state, tape[pos])]\\n state = transition[0]\\n tape[pos] = transition[1]\\n\\n if transition[2] == '>':\\n pos += 1\\n elif transition[2] == '<':\\n pos -= 1\\n\\n return state == self.acceptState\",\n \"def in_bisect(word_list, word):\\n\\ti = bisect_left(word_list, word)\\n\\tif i != len(word_list) and word_list[i] == word:\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn False\",\n \"def _does_words_matches(original_word: str, encoded_word: str) -> bool:\\n return(\\n len(original_word) == len(encoded_word) and\\n original_word[0] == encoded_word[0] and\\n original_word[-1] == encoded_word[-1] and\\n sorted(original_word[1:-1]) == sorted(encoded_word[1:-1])\\n )\",\n \"def check_present_and_add(self, word):\\n\\n current_node = self.root_node\\n is_new_word = False\\n\\n # iterate through trie adding missing notes\\n for char in word:\\n if char not in current_node:\\n is_new_word = True\\n current_node[char] = {}\\n current_node = current_node[char]\\n \\n # mark end of word so that words that are prefixes of present words are not\\n # returned - i.e. each word must have an explicit \\\"End of Word\\\" marker\\n if \\\"End of Word\\\" not in current_node:\\n is_new_word = True\\n current_node[\\\"End on Word\\\"] = {}\\n\\n return is_new_word\",\n \"def is_cap_word(self, word):\\n try:\\n return word[0].isupper()\\n except:\\n return False\",\n \"def isUnique(self, word):\\n abbr = self.gen_abbr(word)\\n\\n if abbr not in self.dict:\\n return True\\n elif len(self.dict[abbr]) == 1 and word in self.dict[abbr]:\\n return True\\n else:\\n return False\",\n \"def search(self, word):\\n length = len(word)\\n if length not in self.dic:\\n return False\\n else:\\n candidateList = self.dic[length]\\n for candidate in candidateList:\\n for i in xrange(length):\\n if candidate[i] != word[i]:\\n if candidate[i+1:] == word[i+1:]:\\n return True\\n else:\\n break\\n return False\",\n \"def _check_add_entities(self, word: str, type_: str) -> bool:\\n\\n if self.check_known and self.check_unknown:\\n return True\\n elif self.check_known and word in self.known_words[type_]:\\n return True\\n elif self.check_unknown and word not in self.known_words[type_]:\\n return True\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8187714","0.8107539","0.7983707","0.7944388","0.7943436","0.7918588","0.78547037","0.7823912","0.7772084","0.7752642","0.773667","0.77111965","0.7665853","0.76646894","0.7465163","0.7308262","0.72991234","0.7229191","0.7229191","0.7229191","0.70942104","0.7069979","0.70499206","0.6980611","0.69763803","0.6957096","0.6955949","0.6878476","0.68287677","0.67881465","0.6778319","0.676645","0.6754435","0.6703928","0.66758424","0.6656487","0.6643276","0.66398036","0.66279984","0.6589604","0.6583397","0.65694606","0.6552472","0.65490127","0.65342367","0.65174854","0.65114886","0.65099007","0.64889854","0.64887196","0.6456837","0.64568204","0.6424731","0.63978994","0.6387502","0.638176","0.63553673","0.63507193","0.6347678","0.63113964","0.62832516","0.6272664","0.62578106","0.62572503","0.62414354","0.62307745","0.621002","0.61954737","0.6167921","0.6163587","0.6151902","0.6143439","0.6142552","0.61312914","0.6126676","0.6125074","0.6117732","0.611525","0.61074394","0.61073047","0.6101631","0.6101596","0.610077","0.60931736","0.6072537","0.6065429","0.60492194","0.60325533","0.6025841","0.60240406","0.5985491","0.5979533","0.59788036","0.59489495","0.59433043","0.5930116","0.5914664","0.5910307","0.5908733","0.5907333"],"string":"[\n \"0.8187714\",\n \"0.8107539\",\n \"0.7983707\",\n \"0.7944388\",\n \"0.7943436\",\n \"0.7918588\",\n \"0.78547037\",\n \"0.7823912\",\n \"0.7772084\",\n \"0.7752642\",\n \"0.773667\",\n \"0.77111965\",\n \"0.7665853\",\n \"0.76646894\",\n \"0.7465163\",\n \"0.7308262\",\n \"0.72991234\",\n \"0.7229191\",\n \"0.7229191\",\n \"0.7229191\",\n \"0.70942104\",\n \"0.7069979\",\n \"0.70499206\",\n \"0.6980611\",\n \"0.69763803\",\n \"0.6957096\",\n \"0.6955949\",\n \"0.6878476\",\n \"0.68287677\",\n \"0.67881465\",\n \"0.6778319\",\n \"0.676645\",\n \"0.6754435\",\n \"0.6703928\",\n \"0.66758424\",\n \"0.6656487\",\n \"0.6643276\",\n \"0.66398036\",\n \"0.66279984\",\n \"0.6589604\",\n \"0.6583397\",\n \"0.65694606\",\n \"0.6552472\",\n \"0.65490127\",\n \"0.65342367\",\n \"0.65174854\",\n \"0.65114886\",\n \"0.65099007\",\n \"0.64889854\",\n \"0.64887196\",\n \"0.6456837\",\n \"0.64568204\",\n \"0.6424731\",\n \"0.63978994\",\n \"0.6387502\",\n \"0.638176\",\n \"0.63553673\",\n \"0.63507193\",\n \"0.6347678\",\n \"0.63113964\",\n \"0.62832516\",\n \"0.6272664\",\n \"0.62578106\",\n \"0.62572503\",\n \"0.62414354\",\n \"0.62307745\",\n \"0.621002\",\n \"0.61954737\",\n \"0.6167921\",\n \"0.6163587\",\n \"0.6151902\",\n \"0.6143439\",\n \"0.6142552\",\n \"0.61312914\",\n \"0.6126676\",\n \"0.6125074\",\n \"0.6117732\",\n \"0.611525\",\n \"0.61074394\",\n \"0.61073047\",\n \"0.6101631\",\n \"0.6101596\",\n \"0.610077\",\n \"0.60931736\",\n \"0.6072537\",\n \"0.6065429\",\n \"0.60492194\",\n \"0.60325533\",\n \"0.6025841\",\n \"0.60240406\",\n \"0.5985491\",\n \"0.5979533\",\n \"0.59788036\",\n \"0.59489495\",\n \"0.59433043\",\n \"0.5930116\",\n \"0.5914664\",\n \"0.5910307\",\n \"0.5908733\",\n \"0.5907333\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7354392"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":74599,"cells":{"query":{"kind":"string","value":"The set of arguments for constructing a TargetGroupAttachment resource."},"document":{"kind":"string","value":"def __init__(__self__, *,\n target: pulumi.Input['TargetGroupAttachmentTargetArgs'],\n target_group_identifier: pulumi.Input[str]):\n pulumi.set(__self__, \"target\", target)\n pulumi.set(__self__, \"target_group_identifier\", target_group_identifier)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(__self__,\n resource_name: str,\n args: TargetGroupAttachmentArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n target: Optional[pulumi.Input[pulumi.InputType['TargetGroupAttachmentTargetArgs']]] = None,\n target_group_identifier: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__, *,\n target: Optional[pulumi.Input['TargetGroupAttachmentTargetArgs']] = None,\n target_group_identifier: Optional[pulumi.Input[str]] = None):\n if target is not None:\n pulumi.set(__self__, \"target\", target)\n if target_group_identifier is not None:\n pulumi.set(__self__, \"target_group_identifier\", target_group_identifier)","def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None,\n target: Optional[pulumi.Input[pulumi.InputType['TargetGroupAttachmentTargetArgs']]] = None,\n target_group_identifier: Optional[pulumi.Input[str]] = None) -> 'TargetGroupAttachment':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = _TargetGroupAttachmentState.__new__(_TargetGroupAttachmentState)\n\n __props__.__dict__[\"target\"] = target\n __props__.__dict__[\"target_group_identifier\"] = target_group_identifier\n return TargetGroupAttachment(resource_name, opts=opts, __props__=__props__)","def AddArguments(cls, argument_group):\n argument_group.add_argument(\n '--artifact_definitions', '--artifact-definitions',\n dest='artifact_definitions_path', type=str, metavar='PATH',\n action='store', help=(\n 'Path to a directory or file containing artifact definitions, '\n 'which are .yaml files. Artifact definitions can be used to '\n 'describe and quickly collect data of interest, such as specific '\n 'files or Windows Registry keys.'))\n\n argument_group.add_argument(\n '--custom_artifact_definitions', '--custom-artifact-definitions',\n dest='custom_artifact_definitions_path', type=str, metavar='PATH',\n action='store', help=(\n 'Path to a directory or file containing custom artifact '\n 'definitions, which are .yaml files. Artifact definitions can be '\n 'used to describe and quickly collect data of interest, such as '\n 'specific files or Windows Registry keys.'))","def __init__(self, targets, group_members):\r\n # TODO(John Sirois): These validations should be happening sooner in the goal registration\r\n # process.\r\n assert len(map(lambda m: m.group, group_members)) != 1, 'Expected a single group'\r\n assert len(map(lambda m: m.name, group_members)) == len(group_members), (\r\n 'Expected group members with unique names')\r\n\r\n self._targets = maybe_list(targets, expected_type=InternalTarget, raise_type=ValueError)\r\n self._group_members = group_members","def AddArguments(cls, argument_group):","def _build_instance_group_args(self, instance_group):\r\n params = {\r\n 'InstanceCount' : instance_group.num_instances,\r\n 'InstanceRole' : instance_group.role,\r\n 'InstanceType' : instance_group.type,\r\n 'Name' : instance_group.name,\r\n 'Market' : instance_group.market\r\n }\r\n if instance_group.market == 'SPOT':\r\n params['BidPrice'] = instance_group.bidprice\r\n return params","def __init__(__self__,\n resource_name: str,\n args: GroupArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def add_args_to_group(cls, group: \"ArgumentGroup\") -> None:\n # group.description = 'For `Architect`, you can supply...'\n # group.add_argument('--server-option', help='Lets you customize')\n return","def create_args():\n return {\n \"team_id\": fields.UUID(missing=None),\n \"file_ids\": fields.List(fields.UUID(), load_from=\"image_ids\", location=\"json\"),\n }","def _get_argparse_kwargs(self, group, **kwargs):\n if not self.positional:\n dest = self.dest\n if group is not None:\n dest = group.name + '_' + dest\n kwargs['dest'] = dest\n elif not self.required:\n kwargs['nargs'] = '?'\n kwargs.update({'default': None,\n 'metavar': self.metavar,\n 'help': self.help, })\n return kwargs","def add_args_to_group(cls, group: \"ArgumentGroup\") -> None:\n super(AcuteEvalBlueprint, cls).add_args_to_group(group)\n\n group.description = \"\"\"\n AcuteEvalBlueprint: Tasks launched from acute eval blueprints\n require sets of pairings for workers to be able to compare to.\n\n These pairings can be provided as a csv or by passing a\n pairings_task_data dict into extra_args.\n \"\"\"\n group.add_argument(\n \"--annotations-per-pair\",\n dest=\"annotations_per_pair\",\n type=int,\n default=1,\n help=\"Number of annotations per conversation comparison pair\",\n )\n group.add_argument(\n \"--pairings-filepath\",\n dest=\"pairings_filepath\",\n type=str,\n default=None,\n help=\"path to the file containing the task dictionaries\",\n )\n group.add_argument(\n \"--s1-choice\",\n dest=\"s1_choice\",\n type=str,\n default=\"I would prefer to talk to <Speaker 1>\",\n help=\"text next to speaker 1 radio button\",\n )\n group.add_argument(\n \"--s2-choice\",\n dest=\"s2_choice\",\n type=str,\n default=\"I would prefer to talk to <Speaker 2>\",\n help=\"text next to speaker 2 radio button\",\n )\n group.add_argument(\n \"--eval-question\",\n dest=\"eval_question\",\n type=str,\n default=\"Who would you prefer to talk to for a long conversation?\",\n help='question to present to turker for comparison (e.g. \"Which speaker is better?\")',\n )\n group.add_argument(\n \"--block-on-onboarding-fail\",\n dest=\"block_on_onboarding_fail\",\n type=bool,\n default=True,\n help=\"whether to block on onboarding failure\",\n )\n group.add_argument(\n \"--subtasks-per-unit\",\n dest=\"subtasks_per_unit\",\n type=int,\n default=5,\n help=\"number of subtasks/comparisons to do per unit\",\n )\n group.add_argument(\n \"--onboarding-threshold\",\n dest=\"onboarding_threshold\",\n type=float,\n default=0.75,\n help=\"minimum accuracy on onboarding tasks, as a float 0-1.0\",\n )\n group.add_argument(\n \"--random-seed\",\n dest=\"random_seed\",\n type=int,\n default=42,\n help=\"seed for random\",\n )\n group.add_argument(\n \"--additional-task-description\",\n dest=\"additional_task_description\",\n type=str,\n default='',\n help=\"Additional text to show on the left pane\",\n )\n return","def __init__(self, group=None, target=None, name=None,\n args=(), kwargs={}):\n self._target = target\n if name is None:\n name = _newname()\n self._name = str(name)\n self._args, self._kwargs = args, kwargs\n self._start = self._finished = None\n self._handle = None\n self.retval = None","def __init__(__self__,\n resource_name: str,\n args: LoadBalancerSecurityGroupAttachmentArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def setup_args(self):\n self.parser = argparse.ArgumentParser()\n self.group = self.parser.add_mutually_exclusive_group()\n\n self.group.add_argument('-a', '--add', help='Adds a new task to the task list', action='store_true')\n self.group.add_argument('-r', '--remove', help='Removes a task from the task list', action='store_true')\n self.group.add_argument('-f', '--finish', help='Sets a task to be finished', action='store_true')\n self.group.add_argument('-u', '--unfinish', help='Sets a task to be not finished', action='store_true')\n self.group.add_argument('-c', '--change', help='Updates an existing task', action='store_true')\n self.group.add_argument('-v', '--view', help='View your current task list', action='store_true')\n\n return self.parser","def AddArguments(cls, argument_group):\n argument_group.add_argument(\n '-f', '--filter-file', '--filter_file', '--file-filter',\n '--file_filter', dest='file_filter', action='store', type=str,\n default=None, help=(\n 'List of files to include for targeted collection of files to '\n 'parse, one line per file path, setup is /path|file - where each '\n 'element can contain either a variable set in the preprocessing '\n 'stage or a regular expression.'))","def __init__(self,\n create_new_group=None,\n ms_groups_vec=None,\n restore_original_owners_members=None,\n restore_to_original=None,\n target_group=None,\n target_group_name=None,\n target_group_owner=None,\n ):\n\n # Initialize members of the class\n self.create_new_group = create_new_group\n self.ms_groups_vec = ms_groups_vec\n self.restore_original_owners_members = restore_original_owners_members\n self.restore_to_original = restore_to_original\n self.target_group = target_group\n self.target_group_name = target_group_name\n self.target_group_owner = target_group_owner","def create_target_groups(ctx):\n data = self.create_target_groups()\n ctx.info('Created target groups for the load balancer {}:'.format(self.get_balancer_name()))\n ctx.pp.pprint(data)","def extra_target_arguments(self):\n return {}","def build_arg_parser(self):\n super(App, self).build_arg_parser()\n\n exgroup = self.parser.add_mutually_exclusive_group()\n exgroup.add_argument('--format', action='store', choices=[\"zip\", \"tar\"], help='write files in the given format.')\n exgroup.add_argument('--filename', action='store', help='Override default filename and/or extension')","def generator_args(agroup, dlatent_dim=3):\n ### Generator options.\n agroup.add_argument('--latent_dim', type=int, metavar='N',\n default=dlatent_dim,\n help='Dimensionality of the latent vector (noise ' +\n 'input to the generator. Default: %(default)s.')\n agroup.add_argument('--latent_std', type=float, default=1.0,\n help='Standard deviation of the latent space. ' +\n 'Default: %(default)s.')","def _CommonArgs(parser):\n image_args = parser.add_mutually_exclusive_group(required=True)\n image_building_args = image_args.add_argument_group()\n parser.add_argument(\n 'template_file_gcs_path',\n metavar='TEMPLATE_FILE_GCS_PATH',\n help=('The Google Cloud Storage location of the flex template file.'\n 'Overrides if file already exists.'),\n type=arg_parsers.RegexpValidator(r'^gs://.*',\n 'Must begin with \\'gs://\\''))\n\n image_args.add_argument(\n '--image',\n help=('Path to the any image registry location of the prebuilt flex '\n 'template image.'))\n\n parser.add_argument(\n '--sdk-language',\n help=('SDK language of the flex template job.'),\n choices=['JAVA', 'PYTHON'],\n required=True)\n\n parser.add_argument(\n '--metadata-file',\n help='Local path to the metadata json file for the flex template.',\n type=arg_parsers.FileContents())\n\n parser.add_argument(\n '--print-only',\n help=('Prints the container spec to stdout. Does not save in '\n 'Google Cloud Storage.'),\n default=False,\n action=actions.StoreBooleanProperty(\n properties.VALUES.dataflow.print_only))\n\n image_building_args.add_argument(\n '--image-gcr-path',\n help=('The Google Container Registry location to store the flex '\n 'template image to be built.'),\n type=arg_parsers.RegexpValidator(\n r'^(.*\\.){0,1}gcr.io/.*',\n ('Must begin with \\'[multi-region.]gcr.io/\\'. Please check '\n 'https://cloud.google.com/container-registry/docs/overview '\n 'for available multi-regions')),\n required=True)\n\n image_building_args.add_argument(\n '--jar',\n metavar='JAR',\n type=arg_parsers.ArgList(),\n action=arg_parsers.UpdateAction,\n help=('Local path to your dataflow pipeline jar file and all their '\n 'dependent jar files required for the flex template classpath. '\n 'You can pass them as a comma separated list or repeat '\n 'individually with --jar flag. Ex: --jar=\"code.jar,dep.jar\" or '\n '--jar code.jar, --jar dep.jar.'),\n required=True)\n\n image_building_args.add_argument(\n '--flex-template-base-image',\n help=('Flex template base image to be used while building the '\n 'container image. Allowed choices are JAVA8, JAVA11 or gcr.io '\n 'path of the specific version of the base image. For JAVA8 and '\n 'JAVA11 option, we use the latest base image version to build '\n 'the container. You can also provide a specific version from '\n 'this link https://gcr.io/dataflow-templates-base/'),\n type=arg_parsers.RegexpValidator(\n r'^JAVA11$|^JAVA8$|^gcr.io/.*',\n 'Must be JAVA11 or JAVA8 or begin with \\'gcr.io/\\''),\n required=True)\n\n image_building_args.add_argument(\n '--env',\n metavar='ENV',\n type=arg_parsers.ArgDict(),\n action=arg_parsers.UpdateAction,\n help=\n ('Environment variables to create for the Dockerfile. '\n 'You can pass them as a comma separated list or repeat individually '\n 'with --env flag. Ex: --env=\"A=B,C=D\" or --env A=B, --env C=D.'\n 'You can find the list of supported environment variables in this '\n 'link. https://cloud.google.com/dataflow/docs/guides/templates/'\n 'troubleshooting-flex-templates'\n '#setting_required_dockerfile_environment_variables'),\n required=True)","def add_group(self, *args, **kwds):\n title = kwds.pop('title', None)\n description = kwds.pop('description', None)\n if kwds:\n raise Exception('unknown keyword arguments: %s' % kwds)\n\n # set title, description if args[0] is string\n if isinstance(args[0], string_types):\n title = args[0]\n args = args[1:]\n if isinstance(args[0], string_types):\n description = args[0]\n args = args[1:]\n\n assert all(isinstance(arg, Command) for arg in args), 'all args should be instance of Command'\n self._arg_stack.append(('group', args, {'title': title, 'description': description}))\n return self","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # Add common arguments\n self.add_argument(\n \"--debug\",\n action=\"store_true\",\n default=False,\n help=\"debug mode: show debug messages\",\n )\n self.add_argument(\n \"-s\",\n \"--server\",\n required=True,\n help='Galène server to connect to, e.g. \"wss://galene.example.com/ws\"',\n )\n self.add_argument(\n \"-g\",\n \"--group\",\n required=True,\n help=\"Join this group\",\n )\n self.add_argument(\n \"-u\",\n \"--username\",\n required=True,\n help=\"Group username\",\n )\n self.add_argument(\n \"-p\",\n \"--password\",\n help=\"Group password\",\n )","def builder_will_create_target_image(self, builder, target, image_id, template, parameters):","def create_target(self, name, group_name, desc, contact_mail, could_del):\r\n\r\n target_id = self._db.target.insert(name=name,\r\n desc=desc, contact_type=\"email\",\r\n contact=contact_mail, type=\"plain\", info=\"\",\r\n candelete=could_del, tulip_counter=0,\r\n download_counter=0)\r\n self._db.commit()\r\n\r\n # extract the ID of the request group, if any, of found the default, if supported\r\n requested_group = group_name if group_name else settings['globals'].default_group\r\n if requested_group:\r\n group_id = self.get_group_id(requested_group)\r\n group_row = self._db(self._db.targetgroup.id==group_id).select().first()\r\n\r\n if group_row['targets']:\r\n comrades = json.loads(group_row['targets'])\r\n comrades.append(target_id)\r\n self.update_targetgroup(group_id, targets=json.dumps(comrades))\r\n else:\r\n starting_json = '[\"' + str(target_id) + '\"]'\r\n self.update_targetgroup(group_id, targets=starting_json)\r\n\r\n return target_id","def _build_instance_group_list_args(self, instance_groups):\r\n if type(instance_groups) != types.ListType:\r\n instance_groups = [instance_groups]\r\n\r\n params = {}\r\n for i, instance_group in enumerate(instance_groups):\r\n ig_dict = self._build_instance_group_args(instance_group)\r\n for key, value in ig_dict.iteritems():\r\n params['InstanceGroups.member.%d.%s' % (i+1, key)] = value\r\n return params","def __init__(__self__,\n resource_name: str,\n args: ManagedNetworkGroupArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def target_group_identifier(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"target_group_identifier\")","def create_target_image(self, builder, target, base_image, parameters):","def AddArguments(cls, argument_group):\n argument_group.add_argument(\n u'--case_name', dest=u'case_name', type=str, action=u'store',\n default=cls._DEFAULT_CASE, help=(\n u'Add a case name. This will be the name of the index in '\n u'ElasticSearch.'))\n argument_group.add_argument(\n u'--document_type', dest=u'document_type', type=str,\n action=u'store', default=cls._DEFAULT_DOCUMENT_TYPE, help=(\n u'Name of the document type. This is the name of the document '\n u'type that will be used in ElasticSearch.'))\n\n ElasticServer.AddArguments(argument_group)","def _render_args(self, target, output_dir):\n args = []\n\n # Glossary of used aapt flags. Aapt handles a ton of action, this will continue to expand.\n # : 'package' is the main aapt operation (see class docstring for more info).\n # : '-m' is to \"make\" a package directory under location '-J'.\n # : '-J' Points to the output directory.\n # : '-M' is the AndroidManifest.xml of the project.\n # : '-S' points to the resource_dir to \"spider\" down while collecting resources.\n # : '-I' packages to add to base \"include\" set, here it is the android.jar of the target-sdk.\n args.extend([self.aapt_tool(target.build_tools_version)])\n args.extend(['package', '-m', '-J', output_dir])\n args.extend(['-M', target.manifest.path])\n args.extend(['-S', target.resource_dir])\n args.extend(['-I', self.android_jar_tool(target.manifest.target_sdk)])\n args.extend(['--ignore-assets', self.ignored_assets])\n logger.debug('Executing: {0}'.format(' '.join(args)))\n return args","def templateargs(self, target_jar, confs=None):\r\n raise NotImplementedError()","def __init__(self, group, **kwargs):\n #group can be robot stage floor or tool\n super(PhysicalPropertiesInterventionActorPolicy, self).__init__()\n self.task_intervention_space = None\n self.group = group","def _get_target_group(self):\n return self.__target_group","def _get_target_group(self):\n return self.__target_group","def _get_target_group(self):\n return self.__target_group","def _get_target_group(self):\n return self.__target_group","def _get_target_group(self):\n return self.__target_group","def _get_target_group(self):\n return self.__target_group","def create_targetgroup(self, name, desc, tags, targets=None):\r\n # http://zimbabwenewsonline.com/top_news/2495.html \r\n prev_row = self._db(self._db.targetgroup.name==name).select().first()\r\n if prev_row:\r\n self._db.targetgroup.update(id=prev_row.id, name=name, desc=desc,\r\n tags=tags, targets=targets)\r\n ret_id = prev_row.id\r\n else:\r\n ret_id = self._db.targetgroup.insert(name=name, desc=desc,\r\n tags=tags, targets=targets)\r\n\r\n self._db.commit()\r\n return ret_id","def add_bu_group(self, **kwargs):\n _stringify_kw(kwargs)\n \n bu_group = ElementTree.Element(xml_strings['backup_group'], **kwargs)\n self._root.append(bu_group)\n return XMLGroupOverlay(bu_group, self._root)","def Args(cls, parser):\n cls.NETWORK_ARG = network_flags.NetworkArgumentForOtherResource(\n 'A reference to a network in this project',\n detailed_help=\"\"\"\\\n A reference to a network in this project to\n contain the VPN Gateway.\n \"\"\")\n cls.NETWORK_ARG.AddArgument(parser)\n cls.TARGET_VPN_GATEWAY_ARG = flags.TargetVpnGatewayArgument()\n cls.TARGET_VPN_GATEWAY_ARG.AddArgument(parser, operation_type='create')\n\n parser.add_argument(\n '--description',\n help='An optional, textual description for the target VPN Gateway.')","def attach_tgw(**kwargs):","def _get_argparse_kwargs(self, group, **kwargs):\n kwargs = super(_ConfigFileOpt, self)._get_argparse_kwargs(group)\n kwargs['action'] = self.ConfigFileAction\n return kwargs","def target_group(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"target_group\")","def __init__(self, title, mentor_id, start_date, end_date, file_name, group='0'):\n self.ID_MENTOR = mentor_id\n self.TITLE = title\n self.START_DATA = start_date\n self.END_DATA = end_date\n self.LINK = file_name\n self.GROUP = group","def from_group(group):\n if not group:\n return []\n\n main, sep, ext = group.partition(\" :\")\n arguments = main.split()\n if sep:\n arguments.append(ext)\n\n return arguments","def __init__(self, *args):\n for arg in args:\n if not isinstance(arg, Orable):\n raise ConfigException(\"argument %s is not a recognized TaskGroup arg type\" % str(arg))\n self.args = list(args)","def _set_target_group(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"target_group must be of a type compatible with leafref\"\"\",\n 'defined-type': \"leafref\",\n 'generated-type': \"\"\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\"\"\",\n })\n\n self.__target_group = t\n if hasattr(self, '_set'):\n self._set()","def _set_target_group(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"target_group must be of a type compatible with leafref\"\"\",\n 'defined-type': \"leafref\",\n 'generated-type': \"\"\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\"\"\",\n })\n\n self.__target_group = t\n if hasattr(self, '_set'):\n self._set()","def _set_target_group(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"target_group must be of a type compatible with leafref\"\"\",\n 'defined-type': \"leafref\",\n 'generated-type': \"\"\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\"\"\",\n })\n\n self.__target_group = t\n if hasattr(self, '_set'):\n self._set()","def __init__(__self__, *,\n group_ids: pulumi.Input[Sequence[pulumi.Input[str]]],\n custom_email_subject: Optional[pulumi.Input[str]] = None,\n custom_webhook_payload: Optional[pulumi.Input[str]] = None):\n pulumi.set(__self__, \"group_ids\", group_ids)\n if custom_email_subject is not None:\n pulumi.set(__self__, \"custom_email_subject\", custom_email_subject)\n if custom_webhook_payload is not None:\n pulumi.set(__self__, \"custom_webhook_payload\", custom_webhook_payload)","def _set_target_group(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"target_group must be of a type compatible with leafref\"\"\",\n 'defined-type': \"leafref\",\n 'generated-type': \"\"\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\"\"\",\n })\n\n self.__target_group = t\n if hasattr(self, '_set'):\n self._set()","def _set_target_group(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"target_group must be of a type compatible with leafref\"\"\",\n 'defined-type': \"leafref\",\n 'generated-type': \"\"\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\"\"\",\n })\n\n self.__target_group = t\n if hasattr(self, '_set'):\n self._set()","def _set_target_group(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"target_group must be of a type compatible with leafref\"\"\",\n 'defined-type': \"leafref\",\n 'generated-type': \"\"\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\"target-group\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\"\"\",\n })\n\n self.__target_group = t\n if hasattr(self, '_set'):\n self._set()","def create_group_command(client: MsGraphClient, args: dict) -> tuple[str, dict, dict]:\n required_properties = {\n 'displayName': str(args.get('display_name')),\n 'mailNickname': str(args.get('mail_nickname')),\n 'mailEnabled': args.get('mail_enabled') == 'true',\n 'securityEnabled': args.get('security_enabled')\n }\n\n # create the group\n group = client.create_group(required_properties)\n\n # display the new group and it's properties\n group_readable, group_outputs = parse_outputs(group)\n human_readable = tableToMarkdown(name=f\"{required_properties['displayName']} was created successfully:\",\n t=group_readable,\n headers=['ID', 'Display Name', 'Description', 'Created Date Time', 'Mail',\n 'Security Enabled', 'Mail Enabled'],\n removeNull=True)\n entry_context = {f'{INTEGRATION_CONTEXT_NAME}(val.ID === obj.ID)': group_outputs}\n return human_readable, entry_context, group","def __init__(__self__,\n resource_name: str,\n args: DiskReplicaGroupArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def add_argument(self, *args, **kwargs):\n\n if 'group' not in kwargs or kwargs['group'] is None:\n self._parser.add_argument(*args, **kwargs)\n\n else:\n group = self._group_by_title(kwargs['group'])\n\n if group is None:\n raise ValueError(\n 'Trying to reference nonexisten argument group.'\n )\n\n else:\n kwargsr = {k: kwargs[k] for k in kwargs.keys() if 'group' != k}\n group.add_argument(*args, **kwargsr)","def build_task_args(self, test_name):\n task_args = {'service_list': [test_name]}\n task_args['image_name'] = str(self.image.name)\n task_args['flavor_name'] = str(self.flavor.name)\n task_args['flavor_alt_name'] = str(self.flavor_alt.name)\n task_args['glance_image_location'] = str(self.filename)\n task_args['glance_image_format'] = str(self.image_format)\n task_args['tmpl_dir'] = str(self.template_dir)\n task_args['sup_dir'] = str(self.support_dir)\n task_args['users_amount'] = self.users_amount\n task_args['tenants_amount'] = self.tenants_amount\n task_args['use_existing_users'] = False\n task_args['iterations'] = self.iterations_amount\n task_args['concurrency'] = self.concurrency\n task_args['smoke'] = self.smoke\n task_args['volume_version'] = self.volume_version\n task_args['volume_service_type'] = self.volume_service_type\n task_args['block_migration'] = env.get(\"BLOCK_MIGRATION\").lower()\n task_args['username'] = self.username\n\n if self.ext_net:\n task_args['floating_network'] = str(self.ext_net.name)\n else:\n task_args['floating_network'] = ''\n\n if self.network:\n task_args['netid'] = str(self.network.id)\n else:\n LOGGER.warning(\n 'No tenant network created. '\n 'Trying EXTERNAL_NETWORK as a fallback')\n if env.get(\"EXTERNAL_NETWORK\"):\n network = self.cloud.get_network(env.get(\"EXTERNAL_NETWORK\"))\n task_args['netid'] = str(network.id) if network else ''\n else:\n task_args['netid'] = ''\n\n return task_args","def __init__(__self__,\n resource_name: str,\n args: FailoverGroupArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","async def create(\n self,\n resource_group_name: str,\n project_name: str,\n group_name: str,\n group: Optional[\"models.Group\"] = None,\n **kwargs\n ) -> \"models.Group\":\n cls = kwargs.pop('cls', None) # type: ClsType[\"models.Group\"]\n error_map = {\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\n }\n error_map.update(kwargs.pop('error_map', {}))\n api_version = \"2018-06-01\"\n content_type = kwargs.pop(\"content_type\", \"application/json\")\n accept = \"application/json\"\n\n # Construct URL\n url = self.create.metadata['url'] # type: ignore\n path_format_arguments = {\n 'subscriptionId': self._serialize.url(\"self._config.subscription_id\", self._config.subscription_id, 'str'),\n 'resourceGroupName': self._serialize.url(\"resource_group_name\", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\\w\\._\\(\\)]+$'),\n 'projectName': self._serialize.url(\"project_name\", project_name, 'str'),\n 'groupName': self._serialize.url(\"group_name\", group_name, 'str'),\n }\n url = self._client.format_url(url, **path_format_arguments)\n\n # Construct parameters\n query_parameters = {} # type: Dict[str, Any]\n query_parameters['api-version'] = self._serialize.query(\"api_version\", api_version, 'str')\n\n # Construct headers\n header_parameters = {} # type: Dict[str, Any]\n header_parameters['Content-Type'] = self._serialize.header(\"content_type\", content_type, 'str')\n header_parameters['Accept'] = self._serialize.header(\"accept\", accept, 'str')\n\n body_content_kwargs = {} # type: Dict[str, Any]\n if group is not None:\n body_content = self._serialize.body(group, 'Group')\n else:\n body_content = None\n body_content_kwargs['content'] = body_content\n request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)\n pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)\n response = pipeline_response.http_response\n\n if response.status_code not in [200, 201]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n raise HttpResponseError(response=response, error_format=ARMErrorFormat)\n\n response_headers = {}\n if response.status_code == 200:\n response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))\n deserialized = self._deserialize('Group', pipeline_response)\n\n if response.status_code == 201:\n response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))\n deserialized = self._deserialize('Group', pipeline_response)\n\n if cls:\n return cls(pipeline_response, deserialized, response_headers)\n\n return deserialized","def _get_argparse_kwargs(self, group, **kwargs):\n kwargs = super(MultiOpt, self)._get_argparse_kwargs(group)\n if not self.positional:\n kwargs['action'] = 'append'\n else:\n kwargs['nargs'] = '*'\n return kwargs","def options_argument_group(parser):\n group = parser.add_argument_group()\n\n parser.add_argument(\n \"--encryption\",\n dest=\"encryption\",\n help=\"Optionally include this flag to encrypt/decrypt a file\"\n \" using the key provided.\",\n default=None,\n )\n parser.add_argument(\n \"-f\",\n \"--ethfile\",\n dest=\"ethfilename\",\n help=\"\"\"Optionally specify a JSON file to store or load ethernet configuration data.\"\"\",\n action=\"append\",\n default=None,\n )","def subparser(cls, parent):\n parser = parent.add_parser('attach', help='attach to container')\n parser.add_argument(\n '--image',\n help='image to instantiate and attach to',\n )\n parser.add_argument(\n 'command',\n nargs='*',\n help='image to instantiate and attach to',\n )\n parser.set_defaults(class_=cls, method='attach')","def __init__(__self__,\n resource_name: str,\n args: BasicEndpointGroupArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(self, group_id, generation_id, member_id, group_assignment):\n self.group_id = group_id\n self.generation_id = generation_id\n self.member_id = member_id\n self.group_assignment = group_assignment","def _set_arguments(self):\n cert_location = f\"dependencies{sep}certificates{sep}localuser.crt\"\n key_location = f\"dependencies{sep}certificates{sep}localuser.key\"\n assert Path(cert_location).exists(), (\n f\"The certificate isn't \"\n f\"present at location {Path(cert_location).absolute()}\"\n )\n assert Path(key_location).exists(), (\n f\"The certificate key isn't \"\n f\"present at location {Path(key_location).absolute()}\"\n )\n self._arguments = [\n (\n \"test-certificate-verify\",\n [\"-k\", key_location, \"-c\", cert_location],\n ),\n (\n \"test-sig-algs\",\n [],\n ),\n (\n \"test-clienthello-md5\",\n [],\n ),\n (\n \"test-tls13-pkcs-signature\",\n [],\n ),\n ]","def __init__(__self__,\n resource_name: str,\n args: SecGroupRuleArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def test_create_resource_group(self):\n pass","def _build_arguments(self):\n # TODO: comeback to allow test path override. maybe?\n # self._parser.add_argument(\n # '--test-path',\n # type=utils.validate_path,\n # required=False,\n # help=('Path th projects test Dockerfile. Dockerfile should be in the root of the test directory.')\n # )\n self._parser.add_argument(\n '--configs',\n type=bool,\n required=False,\n default=False,\n help=\"Would you like to inject configuration files?\"\n )","def createGroup(self, *group):\n if not self.rank:\n logging.info('Creating atom group {}'.format(group))\n\n if not len(group):\n for idSS in self.pargs['idSS']:\n self.lmp.command('group group{} type {}'.format(idSS, idSS))\n else:\n self.lmp.command('group ' + ('{} ' * len(group)).format(*group))","def test_positionalargs_4(self):\n\n extra_cons = set([Constraint('animal', ['moose', 'kangaroo']),\n Constraint('colour', ['blue'])])\n\n the_process_unit = ProcessUnit([self.a_pattern_ds], '/another/%file%/%pattern%_%animal%_%colour%.txt',\n 'echo', extra_constraints=extra_cons,\n positional_args=[('animal', 0), ('colour', 1)])\n ds_result = the_process_unit.execute(simulate=True)\n\n expected_string = self.script_header + 'mkdir -p /another/file_1\\necho moose blue test_file1 /another/file_1/pattern_1_moose_blue.txt\\necho kangaroo blue test_file1 /another/file_1/pattern_1_kangaroo_blue.txt\\n'\n self.assertEqual(expected_string, the_process_unit.scheduler.job.to_str())","def _create_child_group(self, name) -> \"GroupBase\":\n pass","def AddCommandArgGroup(parser):\n command_group = parser.add_argument_group(\n help='These arguments are used to run commands using SSH.'\n )\n command_group.add_argument(\n '--command',\n required=True,\n help=\"\"\"\\\n Command to run on the Cloud TPU VM.\n\n Runs the command on the target Cloud TPU Queued Resource's nodes and then exits.\n\n Note: in the case of a TPU Pod, it will only run the command in the\n workers specified with the `--worker` flag (defaults to worker all if not\n set).\n \"\"\",\n )\n command_group.add_argument(\n '--output-directory',\n help=\"\"\"\\\n Path to the directory to output the logs of the commands.\n\n The path can be relative or absolute. The directory must already exist.\n\n If not specified, standard output will be used.\n\n The logs will be written in files named {WORKER_ID}.log. For example:\n \"2.log\".\n \"\"\",\n )","def create_group(self, **kwargs):\n post_body = json.dumps({'group': kwargs})\n resp, body = self.post('groups', post_body)\n self.expected_success(201, resp.status)\n body = json.loads(body)\n return rest_client.ResponseBody(resp, body)","def __init__(\n self,\n group,\n controls):\n self.group = group\n self.controls = controls","def create_target_groups(self):\n target_groups_config = self.get_target_groups_config()\n vpc_id = self.get_vpc_id()\n response_data = {}\n\n for short_name in target_groups_config.keys():\n target_group_name = self.get_target_group_name(short_name)\n\n if self.target_group_exists(short_name):\n self.logger.info(f'Target group {target_group_name} exists, skipping creation.')\n continue\n\n response = self.client.create_target_group(\n Name=target_group_name,\n VpcId=vpc_id,\n **target_groups_config[short_name],\n )\n assert response['ResponseMetadata']['HTTPStatusCode'] == 200\n\n self.logger.info(f'Target group {target_group_name} created.')\n response_data[target_group_name] = response['TargetGroups']\n\n return response_data","def _AcquireArgs(self):\n # A Command subclass can define a _Flags() method.\n # Calliope sets up _Flags() and should not affect the legacy setting.\n legacy = self.ai.display_info.legacy\n self._common_type._Flags(self.ai) # pylint: disable=protected-access\n self.ai.display_info.legacy = legacy\n # A command implementation can optionally define an Args() method.\n self._common_type.Args(self.ai)\n\n if self._parent_group:\n # Add parent flags to children, if they aren't represented already\n for flag in self._parent_group.GetAllAvailableFlags():\n if flag.is_replicated:\n # Each command or group gets its own unique help flags.\n continue\n if flag.do_not_propagate:\n # Don't propagate down flags that only apply to the group but not to\n # subcommands.\n continue\n if flag.required:\n # It is not easy to replicate required flags to subgroups and\n # subcommands, since then there would be two+ identical required\n # flags, and we'd want only one of them to be necessary.\n continue\n try:\n self.ai.AddFlagActionFromAncestors(flag)\n except argparse.ArgumentError:\n raise parser_errors.ArgumentException(\n 'repeated flag in {command}: {flag}'.format(\n command=self.dotted_name,\n flag=flag.option_strings))\n # Update parent display_info in children, children take precedence.\n self.ai.display_info.AddLowerDisplayInfo(\n self._parent_group.ai.display_info)","def setup_args(**kargs):\n args = [get_nupack_exec_path(kargs['exec_name']),\n '-material', kargs['material'], '-sodium', kargs['sodium'],\n '-magnesium', kargs['magnesium'], '-dangles', kargs['dangles'], '-T', kargs['T']]\n if kargs['multi']: args += ['-multi']\n if kargs['pseudo']: args += ['-pseudo']\n return args","def _get_argparse_kwargs(self, group, **kwargs):\n kwargs = super(_ConfigDirOpt, self)._get_argparse_kwargs(group)\n kwargs['action'] = self.ConfigDirAction\n return kwargs","def add_arguments(cls, key: str, parser: argparse.ArgumentParser):\n group = parser.add_argument_group(f\"{cls.alias} scheduler\")\n cls._add_arguments(_PrefixParser(parser=group, prefix=f\"--{key}-{cls.alias}-\"))\n return parser","def fill_parser(self, parser):\n parser.add_argument(\n \"charm_name\",\n metavar=\"charm-name\",\n help=\"The charm name to associate the resource\",\n )\n parser.add_argument(\"resource_name\", metavar=\"resource-name\", help=\"The resource name\")\n group = parser.add_mutually_exclusive_group(required=True)\n group.add_argument(\n \"--filepath\",\n type=SingleOptionEnsurer(useful_filepath),\n help=\"The file path of the resource content to upload\",\n )\n group.add_argument(\n \"--image\",\n type=SingleOptionEnsurer(str),\n help=\"The digest of the OCI image\",\n )","def setup_args_create(parser):\n parser.add_argument(\"--domain\", required=False)\n parser.add_argument(\"--ansible\", required=False,\n dest=\"ansible\", action=\"store_true\")\n return parser","def build_task_args(self, test_name):\n task_args = {}\n if self.ext_net:\n task_args['floating_network'] = str(self.ext_net.name)\n else:\n task_args['floating_network'] = ''\n task_args['image_name'] = str(self.image.name)\n task_args['flavor_name'] = str(self.flavor.name)\n return task_args","def __init__(self):\n self._parser = argparse.ArgumentParser(description='Arguments for talking to vCenter')\n self._standard_args_group = self._parser.add_argument_group('standard arguments')\n self._specific_args_group = self._parser.add_argument_group('sample-specific arguments')\n\n # because -h is reserved for 'help' we use -s for service\n self._standard_args_group.add_argument('-s', '--host',\n required=True,\n action='store',\n help='vSphere service address to connect to')\n\n # because we want -p for password, we use -o for port\n self._standard_args_group.add_argument('-o', '--port',\n type=int,\n default=443,\n action='store',\n help='Port to connect on')\n\n self._standard_args_group.add_argument('-u', '--user',\n required=True,\n action='store',\n help='User name to use when connecting to host')\n\n self._standard_args_group.add_argument('-p', '--password',\n required=False,\n action='store',\n help='Password to use when connecting to host')\n\n self._standard_args_group.add_argument('-nossl', '--disable-ssl-verification',\n required=False,\n action='store_true',\n help='Disable ssl host certificate verification')","def __on_group_created(self, logger, *args):","def target_group_identifier(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"target_group_identifier\")","def __init__(__self__, resource_name, opts=None, attributes=None, name=None, parent_id=None, realm_id=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['attributes'] = attributes\n __props__['name'] = name\n __props__['parent_id'] = parent_id\n if realm_id is None:\n raise TypeError(\"Missing required property 'realm_id'\")\n __props__['realm_id'] = realm_id\n __props__['path'] = None\n super(Group, __self__).__init__(\n 'keycloak:index/group:Group',\n resource_name,\n __props__,\n opts)","def create_mailing_list_group(sender, instance, **kwargs):\n\tname = instance.name\n\treturn requests.post(\"https://api.mailgun.net/v3/lists\",\n auth=('api', settings.MAILGUN_API_KEY),\n data={'address': '{}@arenbergorkest.be'.format(name),\n 'name': name})","def __init__(self, name, group=None):\n self.name = name\n self.group = group","def __init__(self,dataset=scripts,group=\"\"):\n self.dataset = dataset\n self.group=group","def add_arguments(self):\n if self.name == \"ptchunk\":\n group = self.parser\n else:\n group = self.parser.add_argument_group(\"generic optional arguments\")\n if self.name == \"ptchunk\":\n self.parser.add_argument(\n \"addresses\", nargs=\"*\", default=None,\n help=\"Address(es) to ptmalloc chunk headers\"\n )\n group.add_argument(\n \"-v\", \"--verbose\", dest=\"verbose\", action=\"count\", default=0,\n help=\"Use verbose output (multiple for more verbosity)\"\n )\n group.add_argument(\n \"-h\", \"--help\", dest=\"help\", action=\"store_true\", default=False,\n help=\"Show this help\"\n )\n if self.name == \"ptchunk\":\n group.add_argument(\n \"-c\", \"--count\", dest=\"count\", type=h.check_count_value, default=1,\n help=\"\"\"Number of chunks to print linearly (also supports \"unlimited\"/0\nor negative numbers to print chunks going backwards)\"\"\"\n )\n group.add_argument(\n \"-x\", \"--hexdump\", dest=\"hexdump\", action=\"store_true\", default=False,\n help=\"Hexdump the chunk contents\"\n )\n group.add_argument(\n \"-X\", dest=\"hexdump_unit\", type=h.check_hexdump_unit, default=1,\n help=f\"Specify hexdump unit ({h.prepare_list(h.hexdump_units)}) when using -x (default: %(default)s)\"\n )\n group.add_argument(\n \"-m\", \"--maxbytes\", dest=\"maxbytes\", type=h.string_to_int, default=0,\n help=\"Max bytes to dump with -x\"\n )\n if self.name == \"ptchunk\":\n group.add_argument(\n \"-n\", dest=\"no_newline\", action=\"store_true\", default=False,\n help=\"Do not output the trailing newline (summary representation)\"\n )\n group.add_argument(\n \"-p\", dest=\"print_offset\", type=h.string_to_int, default=0,\n help=\"Print data inside at given offset (summary representation)\"\n )\n group.add_argument(\n \"-M\", \"--metadata\", dest=\"metadata\", type=str, default=None,\n help=\"Comma separated list of metadata to print (previously stored with the 'ptmeta' command)\"\n )\n if self.name == \"ptchunk\" or self.name == \"ptlist\":\n group.add_argument(\n \"-I\", \"--highlight-types\", dest=\"highlight_types\", type=str, default=None,\n help=\"Comma separated list of chunk types (M, F, f or t) for chunks we want to highlight in the output\"\n )\n group.add_argument(\n \"-H\", \"--highlight-addresses\", dest=\"highlight_addresses\", type=str, default=None,\n help=\"Comma separated list of addresses for chunks we want to highlight in the output\"\n )\n group.add_argument(\n \"-G\", \"--highlight-metadata\", dest=\"highlight_metadata\", type=str, default=None,\n help=\"\"\"Comma separated list of metadata (previously stored with the 'ptmeta' command) \nfor chunks we want to highlight in the output\"\"\"\n )\n group.add_argument(\n \"--highlight-only\", dest=\"highlight_only\", action=\"store_true\", default=False,\n help=\"Only show the highlighted chunks (instead of just '*' them)\"\n )\n if self.name != \"ptfree\":\n group.add_argument(\n \"--use-cache\", dest=\"use_cache\", action=\"store_true\", default=False,\n help=\"\"\"Do not fetch any internal ptmalloc data if you know they haven't changed since\nlast time they were cached\"\"\"\n )\n group.add_argument(\n \"--json\", dest=\"json_filename\", type=str, default=None,\n help=\"Specify the json filename to save the output (Useful to diff 2 outputs)\"\n )\n group.add_argument(\n \"--json-append\", dest=\"json_append\", action=\"store_true\", default=False,\n help=\"Append to the filename instead of overwriting\"\n )\n group.add_argument(\n \"-s\", \"--search\", dest=\"search_value\", type=str, default=None,\n help=\"Search a value and show match/no match\"\n )\n group.add_argument(\n \"-S\", \"--search-type\", dest=\"search_type\", type=str, default=\"string\",\n help=f\"Specify search type ({h.prepare_list(ptchunk.search_types)}) when using -s (default: %(default)s)\"\n )\n group.add_argument(\n \"--match-only\", dest=\"match_only\", action=\"store_true\", default=False,\n help=\"Only show the matched chunks (instead of just show match/no match)\"\n )\n group.add_argument(\n \"--skip-header\", dest=\"skip_header\", action=\"store_true\", default=False,\n help=\"Don't include chunk header contents in search results\"\n )\n group.add_argument(\n \"--depth\", dest=\"search_depth\", type=h.string_to_int, default=0,\n help=\"How far into each chunk to search, starting from chunk header address\"\n )\n group.add_argument(\n \"--cmds\", dest=\"commands\", type=str, default=None,\n help=\"\"\"Semi-colon separated list of debugger commands to be executed for each chunk that is displayed \n('@' is replaced by the chunk address)\"\"\"\n )\n # allows to enable a different log level during development/debugging\n self.parser.add_argument(\n \"--loglevel\", dest=\"loglevel\", default=None,\n help=argparse.SUPPRESS\n )\n # Debug and force printing stuff\n self.parser.add_argument(\n \"-d\", \"--debug\", dest=\"debug\", action=\"store_true\", default=False,\n help=argparse.SUPPRESS\n )\n group.add_argument(\n \"-o\", \"--address-offset\", dest=\"address_offset\", action=\"store_true\", default=False,\n help=\"Print offsets from the first printed chunk instead of addresses\"\n )","def __init__(self, group_id=None):\n super().__init__()\n self.group_id = group_id","def builder_should_create_target_image(self, builder, target, image_id, template, parameters):","def inspect_arguments(self, args):\n if not os.path.exists(self.output_dir):\n os.makedirs(self.output_dir)\n\n if not os.path.exists(self.input_dir):\n raise ValueError('Specified input directory could not be found.')\n\n self.group_distributions_file = os.path.join(self.input_dir, self.group_distributions_file)\n if not os.path.exists(self.group_distributions_file):\n raise ValueError('Specified groups distribution file could not be found in input directory.')\n\n self.group_probabilities_file = os.path.join(self.input_dir, self.group_probabilities_file)\n if not os.path.exists(self.group_probabilities_file):\n raise ValueError('Specified groups probabilities file could not be found in input directory.')","def add_key_arguments(parser):\n group = parser.add_argument_group(\"Key management\")\n group.add_argument(\n \"-ks\", \"--key_size\",\n help='Length of the new key',\n type=int,\n default=4096\n )\n group.add_argument(\n \"-kt\", \"--key_type\",\n help=\"Method used for generating the new key\",\n choices=[\"dsa\", \"rsa\"],\n default=\"rsa\"\n )\n return group","def addTargets(self, **kwargs):\n self.targets.update(kwargs)\n for key, val in kwargs.items():\n self.namespace[key] = val","def command(self, *args, **kwargs):\n help_group = kwargs.pop(\"group\", None)\n decorator = super(GroupedGroup, self).command(*args, **kwargs)\n\n def wrapper(f):\n cmd = decorator(f)\n cmd.help_group = help_group\n return cmd\n\n return wrapper"],"string":"[\n \"def __init__(__self__,\\n resource_name: str,\\n args: TargetGroupAttachmentArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n target: Optional[pulumi.Input[pulumi.InputType['TargetGroupAttachmentTargetArgs']]] = None,\\n target_group_identifier: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__, *,\\n target: Optional[pulumi.Input['TargetGroupAttachmentTargetArgs']] = None,\\n target_group_identifier: Optional[pulumi.Input[str]] = None):\\n if target is not None:\\n pulumi.set(__self__, \\\"target\\\", target)\\n if target_group_identifier is not None:\\n pulumi.set(__self__, \\\"target_group_identifier\\\", target_group_identifier)\",\n \"def get(resource_name: str,\\n id: pulumi.Input[str],\\n opts: Optional[pulumi.ResourceOptions] = None,\\n target: Optional[pulumi.Input[pulumi.InputType['TargetGroupAttachmentTargetArgs']]] = None,\\n target_group_identifier: Optional[pulumi.Input[str]] = None) -> 'TargetGroupAttachment':\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = _TargetGroupAttachmentState.__new__(_TargetGroupAttachmentState)\\n\\n __props__.__dict__[\\\"target\\\"] = target\\n __props__.__dict__[\\\"target_group_identifier\\\"] = target_group_identifier\\n return TargetGroupAttachment(resource_name, opts=opts, __props__=__props__)\",\n \"def AddArguments(cls, argument_group):\\n argument_group.add_argument(\\n '--artifact_definitions', '--artifact-definitions',\\n dest='artifact_definitions_path', type=str, metavar='PATH',\\n action='store', help=(\\n 'Path to a directory or file containing artifact definitions, '\\n 'which are .yaml files. Artifact definitions can be used to '\\n 'describe and quickly collect data of interest, such as specific '\\n 'files or Windows Registry keys.'))\\n\\n argument_group.add_argument(\\n '--custom_artifact_definitions', '--custom-artifact-definitions',\\n dest='custom_artifact_definitions_path', type=str, metavar='PATH',\\n action='store', help=(\\n 'Path to a directory or file containing custom artifact '\\n 'definitions, which are .yaml files. Artifact definitions can be '\\n 'used to describe and quickly collect data of interest, such as '\\n 'specific files or Windows Registry keys.'))\",\n \"def __init__(self, targets, group_members):\\r\\n # TODO(John Sirois): These validations should be happening sooner in the goal registration\\r\\n # process.\\r\\n assert len(map(lambda m: m.group, group_members)) != 1, 'Expected a single group'\\r\\n assert len(map(lambda m: m.name, group_members)) == len(group_members), (\\r\\n 'Expected group members with unique names')\\r\\n\\r\\n self._targets = maybe_list(targets, expected_type=InternalTarget, raise_type=ValueError)\\r\\n self._group_members = group_members\",\n \"def AddArguments(cls, argument_group):\",\n \"def _build_instance_group_args(self, instance_group):\\r\\n params = {\\r\\n 'InstanceCount' : instance_group.num_instances,\\r\\n 'InstanceRole' : instance_group.role,\\r\\n 'InstanceType' : instance_group.type,\\r\\n 'Name' : instance_group.name,\\r\\n 'Market' : instance_group.market\\r\\n }\\r\\n if instance_group.market == 'SPOT':\\r\\n params['BidPrice'] = instance_group.bidprice\\r\\n return params\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: GroupArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def add_args_to_group(cls, group: \\\"ArgumentGroup\\\") -> None:\\n # group.description = 'For `Architect`, you can supply...'\\n # group.add_argument('--server-option', help='Lets you customize')\\n return\",\n \"def create_args():\\n return {\\n \\\"team_id\\\": fields.UUID(missing=None),\\n \\\"file_ids\\\": fields.List(fields.UUID(), load_from=\\\"image_ids\\\", location=\\\"json\\\"),\\n }\",\n \"def _get_argparse_kwargs(self, group, **kwargs):\\n if not self.positional:\\n dest = self.dest\\n if group is not None:\\n dest = group.name + '_' + dest\\n kwargs['dest'] = dest\\n elif not self.required:\\n kwargs['nargs'] = '?'\\n kwargs.update({'default': None,\\n 'metavar': self.metavar,\\n 'help': self.help, })\\n return kwargs\",\n \"def add_args_to_group(cls, group: \\\"ArgumentGroup\\\") -> None:\\n super(AcuteEvalBlueprint, cls).add_args_to_group(group)\\n\\n group.description = \\\"\\\"\\\"\\n AcuteEvalBlueprint: Tasks launched from acute eval blueprints\\n require sets of pairings for workers to be able to compare to.\\n\\n These pairings can be provided as a csv or by passing a\\n pairings_task_data dict into extra_args.\\n \\\"\\\"\\\"\\n group.add_argument(\\n \\\"--annotations-per-pair\\\",\\n dest=\\\"annotations_per_pair\\\",\\n type=int,\\n default=1,\\n help=\\\"Number of annotations per conversation comparison pair\\\",\\n )\\n group.add_argument(\\n \\\"--pairings-filepath\\\",\\n dest=\\\"pairings_filepath\\\",\\n type=str,\\n default=None,\\n help=\\\"path to the file containing the task dictionaries\\\",\\n )\\n group.add_argument(\\n \\\"--s1-choice\\\",\\n dest=\\\"s1_choice\\\",\\n type=str,\\n default=\\\"I would prefer to talk to <Speaker 1>\\\",\\n help=\\\"text next to speaker 1 radio button\\\",\\n )\\n group.add_argument(\\n \\\"--s2-choice\\\",\\n dest=\\\"s2_choice\\\",\\n type=str,\\n default=\\\"I would prefer to talk to <Speaker 2>\\\",\\n help=\\\"text next to speaker 2 radio button\\\",\\n )\\n group.add_argument(\\n \\\"--eval-question\\\",\\n dest=\\\"eval_question\\\",\\n type=str,\\n default=\\\"Who would you prefer to talk to for a long conversation?\\\",\\n help='question to present to turker for comparison (e.g. \\\"Which speaker is better?\\\")',\\n )\\n group.add_argument(\\n \\\"--block-on-onboarding-fail\\\",\\n dest=\\\"block_on_onboarding_fail\\\",\\n type=bool,\\n default=True,\\n help=\\\"whether to block on onboarding failure\\\",\\n )\\n group.add_argument(\\n \\\"--subtasks-per-unit\\\",\\n dest=\\\"subtasks_per_unit\\\",\\n type=int,\\n default=5,\\n help=\\\"number of subtasks/comparisons to do per unit\\\",\\n )\\n group.add_argument(\\n \\\"--onboarding-threshold\\\",\\n dest=\\\"onboarding_threshold\\\",\\n type=float,\\n default=0.75,\\n help=\\\"minimum accuracy on onboarding tasks, as a float 0-1.0\\\",\\n )\\n group.add_argument(\\n \\\"--random-seed\\\",\\n dest=\\\"random_seed\\\",\\n type=int,\\n default=42,\\n help=\\\"seed for random\\\",\\n )\\n group.add_argument(\\n \\\"--additional-task-description\\\",\\n dest=\\\"additional_task_description\\\",\\n type=str,\\n default='',\\n help=\\\"Additional text to show on the left pane\\\",\\n )\\n return\",\n \"def __init__(self, group=None, target=None, name=None,\\n args=(), kwargs={}):\\n self._target = target\\n if name is None:\\n name = _newname()\\n self._name = str(name)\\n self._args, self._kwargs = args, kwargs\\n self._start = self._finished = None\\n self._handle = None\\n self.retval = None\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: LoadBalancerSecurityGroupAttachmentArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def setup_args(self):\\n self.parser = argparse.ArgumentParser()\\n self.group = self.parser.add_mutually_exclusive_group()\\n\\n self.group.add_argument('-a', '--add', help='Adds a new task to the task list', action='store_true')\\n self.group.add_argument('-r', '--remove', help='Removes a task from the task list', action='store_true')\\n self.group.add_argument('-f', '--finish', help='Sets a task to be finished', action='store_true')\\n self.group.add_argument('-u', '--unfinish', help='Sets a task to be not finished', action='store_true')\\n self.group.add_argument('-c', '--change', help='Updates an existing task', action='store_true')\\n self.group.add_argument('-v', '--view', help='View your current task list', action='store_true')\\n\\n return self.parser\",\n \"def AddArguments(cls, argument_group):\\n argument_group.add_argument(\\n '-f', '--filter-file', '--filter_file', '--file-filter',\\n '--file_filter', dest='file_filter', action='store', type=str,\\n default=None, help=(\\n 'List of files to include for targeted collection of files to '\\n 'parse, one line per file path, setup is /path|file - where each '\\n 'element can contain either a variable set in the preprocessing '\\n 'stage or a regular expression.'))\",\n \"def __init__(self,\\n create_new_group=None,\\n ms_groups_vec=None,\\n restore_original_owners_members=None,\\n restore_to_original=None,\\n target_group=None,\\n target_group_name=None,\\n target_group_owner=None,\\n ):\\n\\n # Initialize members of the class\\n self.create_new_group = create_new_group\\n self.ms_groups_vec = ms_groups_vec\\n self.restore_original_owners_members = restore_original_owners_members\\n self.restore_to_original = restore_to_original\\n self.target_group = target_group\\n self.target_group_name = target_group_name\\n self.target_group_owner = target_group_owner\",\n \"def create_target_groups(ctx):\\n data = self.create_target_groups()\\n ctx.info('Created target groups for the load balancer {}:'.format(self.get_balancer_name()))\\n ctx.pp.pprint(data)\",\n \"def extra_target_arguments(self):\\n return {}\",\n \"def build_arg_parser(self):\\n super(App, self).build_arg_parser()\\n\\n exgroup = self.parser.add_mutually_exclusive_group()\\n exgroup.add_argument('--format', action='store', choices=[\\\"zip\\\", \\\"tar\\\"], help='write files in the given format.')\\n exgroup.add_argument('--filename', action='store', help='Override default filename and/or extension')\",\n \"def generator_args(agroup, dlatent_dim=3):\\n ### Generator options.\\n agroup.add_argument('--latent_dim', type=int, metavar='N',\\n default=dlatent_dim,\\n help='Dimensionality of the latent vector (noise ' +\\n 'input to the generator. Default: %(default)s.')\\n agroup.add_argument('--latent_std', type=float, default=1.0,\\n help='Standard deviation of the latent space. ' +\\n 'Default: %(default)s.')\",\n \"def _CommonArgs(parser):\\n image_args = parser.add_mutually_exclusive_group(required=True)\\n image_building_args = image_args.add_argument_group()\\n parser.add_argument(\\n 'template_file_gcs_path',\\n metavar='TEMPLATE_FILE_GCS_PATH',\\n help=('The Google Cloud Storage location of the flex template file.'\\n 'Overrides if file already exists.'),\\n type=arg_parsers.RegexpValidator(r'^gs://.*',\\n 'Must begin with \\\\'gs://\\\\''))\\n\\n image_args.add_argument(\\n '--image',\\n help=('Path to the any image registry location of the prebuilt flex '\\n 'template image.'))\\n\\n parser.add_argument(\\n '--sdk-language',\\n help=('SDK language of the flex template job.'),\\n choices=['JAVA', 'PYTHON'],\\n required=True)\\n\\n parser.add_argument(\\n '--metadata-file',\\n help='Local path to the metadata json file for the flex template.',\\n type=arg_parsers.FileContents())\\n\\n parser.add_argument(\\n '--print-only',\\n help=('Prints the container spec to stdout. Does not save in '\\n 'Google Cloud Storage.'),\\n default=False,\\n action=actions.StoreBooleanProperty(\\n properties.VALUES.dataflow.print_only))\\n\\n image_building_args.add_argument(\\n '--image-gcr-path',\\n help=('The Google Container Registry location to store the flex '\\n 'template image to be built.'),\\n type=arg_parsers.RegexpValidator(\\n r'^(.*\\\\.){0,1}gcr.io/.*',\\n ('Must begin with \\\\'[multi-region.]gcr.io/\\\\'. Please check '\\n 'https://cloud.google.com/container-registry/docs/overview '\\n 'for available multi-regions')),\\n required=True)\\n\\n image_building_args.add_argument(\\n '--jar',\\n metavar='JAR',\\n type=arg_parsers.ArgList(),\\n action=arg_parsers.UpdateAction,\\n help=('Local path to your dataflow pipeline jar file and all their '\\n 'dependent jar files required for the flex template classpath. '\\n 'You can pass them as a comma separated list or repeat '\\n 'individually with --jar flag. Ex: --jar=\\\"code.jar,dep.jar\\\" or '\\n '--jar code.jar, --jar dep.jar.'),\\n required=True)\\n\\n image_building_args.add_argument(\\n '--flex-template-base-image',\\n help=('Flex template base image to be used while building the '\\n 'container image. Allowed choices are JAVA8, JAVA11 or gcr.io '\\n 'path of the specific version of the base image. For JAVA8 and '\\n 'JAVA11 option, we use the latest base image version to build '\\n 'the container. You can also provide a specific version from '\\n 'this link https://gcr.io/dataflow-templates-base/'),\\n type=arg_parsers.RegexpValidator(\\n r'^JAVA11$|^JAVA8$|^gcr.io/.*',\\n 'Must be JAVA11 or JAVA8 or begin with \\\\'gcr.io/\\\\''),\\n required=True)\\n\\n image_building_args.add_argument(\\n '--env',\\n metavar='ENV',\\n type=arg_parsers.ArgDict(),\\n action=arg_parsers.UpdateAction,\\n help=\\n ('Environment variables to create for the Dockerfile. '\\n 'You can pass them as a comma separated list or repeat individually '\\n 'with --env flag. Ex: --env=\\\"A=B,C=D\\\" or --env A=B, --env C=D.'\\n 'You can find the list of supported environment variables in this '\\n 'link. https://cloud.google.com/dataflow/docs/guides/templates/'\\n 'troubleshooting-flex-templates'\\n '#setting_required_dockerfile_environment_variables'),\\n required=True)\",\n \"def add_group(self, *args, **kwds):\\n title = kwds.pop('title', None)\\n description = kwds.pop('description', None)\\n if kwds:\\n raise Exception('unknown keyword arguments: %s' % kwds)\\n\\n # set title, description if args[0] is string\\n if isinstance(args[0], string_types):\\n title = args[0]\\n args = args[1:]\\n if isinstance(args[0], string_types):\\n description = args[0]\\n args = args[1:]\\n\\n assert all(isinstance(arg, Command) for arg in args), 'all args should be instance of Command'\\n self._arg_stack.append(('group', args, {'title': title, 'description': description}))\\n return self\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n # Add common arguments\\n self.add_argument(\\n \\\"--debug\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n help=\\\"debug mode: show debug messages\\\",\\n )\\n self.add_argument(\\n \\\"-s\\\",\\n \\\"--server\\\",\\n required=True,\\n help='Galène server to connect to, e.g. \\\"wss://galene.example.com/ws\\\"',\\n )\\n self.add_argument(\\n \\\"-g\\\",\\n \\\"--group\\\",\\n required=True,\\n help=\\\"Join this group\\\",\\n )\\n self.add_argument(\\n \\\"-u\\\",\\n \\\"--username\\\",\\n required=True,\\n help=\\\"Group username\\\",\\n )\\n self.add_argument(\\n \\\"-p\\\",\\n \\\"--password\\\",\\n help=\\\"Group password\\\",\\n )\",\n \"def builder_will_create_target_image(self, builder, target, image_id, template, parameters):\",\n \"def create_target(self, name, group_name, desc, contact_mail, could_del):\\r\\n\\r\\n target_id = self._db.target.insert(name=name,\\r\\n desc=desc, contact_type=\\\"email\\\",\\r\\n contact=contact_mail, type=\\\"plain\\\", info=\\\"\\\",\\r\\n candelete=could_del, tulip_counter=0,\\r\\n download_counter=0)\\r\\n self._db.commit()\\r\\n\\r\\n # extract the ID of the request group, if any, of found the default, if supported\\r\\n requested_group = group_name if group_name else settings['globals'].default_group\\r\\n if requested_group:\\r\\n group_id = self.get_group_id(requested_group)\\r\\n group_row = self._db(self._db.targetgroup.id==group_id).select().first()\\r\\n\\r\\n if group_row['targets']:\\r\\n comrades = json.loads(group_row['targets'])\\r\\n comrades.append(target_id)\\r\\n self.update_targetgroup(group_id, targets=json.dumps(comrades))\\r\\n else:\\r\\n starting_json = '[\\\"' + str(target_id) + '\\\"]'\\r\\n self.update_targetgroup(group_id, targets=starting_json)\\r\\n\\r\\n return target_id\",\n \"def _build_instance_group_list_args(self, instance_groups):\\r\\n if type(instance_groups) != types.ListType:\\r\\n instance_groups = [instance_groups]\\r\\n\\r\\n params = {}\\r\\n for i, instance_group in enumerate(instance_groups):\\r\\n ig_dict = self._build_instance_group_args(instance_group)\\r\\n for key, value in ig_dict.iteritems():\\r\\n params['InstanceGroups.member.%d.%s' % (i+1, key)] = value\\r\\n return params\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ManagedNetworkGroupArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def target_group_identifier(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"target_group_identifier\\\")\",\n \"def create_target_image(self, builder, target, base_image, parameters):\",\n \"def AddArguments(cls, argument_group):\\n argument_group.add_argument(\\n u'--case_name', dest=u'case_name', type=str, action=u'store',\\n default=cls._DEFAULT_CASE, help=(\\n u'Add a case name. This will be the name of the index in '\\n u'ElasticSearch.'))\\n argument_group.add_argument(\\n u'--document_type', dest=u'document_type', type=str,\\n action=u'store', default=cls._DEFAULT_DOCUMENT_TYPE, help=(\\n u'Name of the document type. This is the name of the document '\\n u'type that will be used in ElasticSearch.'))\\n\\n ElasticServer.AddArguments(argument_group)\",\n \"def _render_args(self, target, output_dir):\\n args = []\\n\\n # Glossary of used aapt flags. Aapt handles a ton of action, this will continue to expand.\\n # : 'package' is the main aapt operation (see class docstring for more info).\\n # : '-m' is to \\\"make\\\" a package directory under location '-J'.\\n # : '-J' Points to the output directory.\\n # : '-M' is the AndroidManifest.xml of the project.\\n # : '-S' points to the resource_dir to \\\"spider\\\" down while collecting resources.\\n # : '-I' packages to add to base \\\"include\\\" set, here it is the android.jar of the target-sdk.\\n args.extend([self.aapt_tool(target.build_tools_version)])\\n args.extend(['package', '-m', '-J', output_dir])\\n args.extend(['-M', target.manifest.path])\\n args.extend(['-S', target.resource_dir])\\n args.extend(['-I', self.android_jar_tool(target.manifest.target_sdk)])\\n args.extend(['--ignore-assets', self.ignored_assets])\\n logger.debug('Executing: {0}'.format(' '.join(args)))\\n return args\",\n \"def templateargs(self, target_jar, confs=None):\\r\\n raise NotImplementedError()\",\n \"def __init__(self, group, **kwargs):\\n #group can be robot stage floor or tool\\n super(PhysicalPropertiesInterventionActorPolicy, self).__init__()\\n self.task_intervention_space = None\\n self.group = group\",\n \"def _get_target_group(self):\\n return self.__target_group\",\n \"def _get_target_group(self):\\n return self.__target_group\",\n \"def _get_target_group(self):\\n return self.__target_group\",\n \"def _get_target_group(self):\\n return self.__target_group\",\n \"def _get_target_group(self):\\n return self.__target_group\",\n \"def _get_target_group(self):\\n return self.__target_group\",\n \"def create_targetgroup(self, name, desc, tags, targets=None):\\r\\n # http://zimbabwenewsonline.com/top_news/2495.html \\r\\n prev_row = self._db(self._db.targetgroup.name==name).select().first()\\r\\n if prev_row:\\r\\n self._db.targetgroup.update(id=prev_row.id, name=name, desc=desc,\\r\\n tags=tags, targets=targets)\\r\\n ret_id = prev_row.id\\r\\n else:\\r\\n ret_id = self._db.targetgroup.insert(name=name, desc=desc,\\r\\n tags=tags, targets=targets)\\r\\n\\r\\n self._db.commit()\\r\\n return ret_id\",\n \"def add_bu_group(self, **kwargs):\\n _stringify_kw(kwargs)\\n \\n bu_group = ElementTree.Element(xml_strings['backup_group'], **kwargs)\\n self._root.append(bu_group)\\n return XMLGroupOverlay(bu_group, self._root)\",\n \"def Args(cls, parser):\\n cls.NETWORK_ARG = network_flags.NetworkArgumentForOtherResource(\\n 'A reference to a network in this project',\\n detailed_help=\\\"\\\"\\\"\\\\\\n A reference to a network in this project to\\n contain the VPN Gateway.\\n \\\"\\\"\\\")\\n cls.NETWORK_ARG.AddArgument(parser)\\n cls.TARGET_VPN_GATEWAY_ARG = flags.TargetVpnGatewayArgument()\\n cls.TARGET_VPN_GATEWAY_ARG.AddArgument(parser, operation_type='create')\\n\\n parser.add_argument(\\n '--description',\\n help='An optional, textual description for the target VPN Gateway.')\",\n \"def attach_tgw(**kwargs):\",\n \"def _get_argparse_kwargs(self, group, **kwargs):\\n kwargs = super(_ConfigFileOpt, self)._get_argparse_kwargs(group)\\n kwargs['action'] = self.ConfigFileAction\\n return kwargs\",\n \"def target_group(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"target_group\\\")\",\n \"def __init__(self, title, mentor_id, start_date, end_date, file_name, group='0'):\\n self.ID_MENTOR = mentor_id\\n self.TITLE = title\\n self.START_DATA = start_date\\n self.END_DATA = end_date\\n self.LINK = file_name\\n self.GROUP = group\",\n \"def from_group(group):\\n if not group:\\n return []\\n\\n main, sep, ext = group.partition(\\\" :\\\")\\n arguments = main.split()\\n if sep:\\n arguments.append(ext)\\n\\n return arguments\",\n \"def __init__(self, *args):\\n for arg in args:\\n if not isinstance(arg, Orable):\\n raise ConfigException(\\\"argument %s is not a recognized TaskGroup arg type\\\" % str(arg))\\n self.args = list(args)\",\n \"def _set_target_group(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"target_group must be of a type compatible with leafref\\\"\\\"\\\",\\n 'defined-type': \\\"leafref\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__target_group = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def _set_target_group(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"target_group must be of a type compatible with leafref\\\"\\\"\\\",\\n 'defined-type': \\\"leafref\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__target_group = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def _set_target_group(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"target_group must be of a type compatible with leafref\\\"\\\"\\\",\\n 'defined-type': \\\"leafref\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__target_group = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def __init__(__self__, *,\\n group_ids: pulumi.Input[Sequence[pulumi.Input[str]]],\\n custom_email_subject: Optional[pulumi.Input[str]] = None,\\n custom_webhook_payload: Optional[pulumi.Input[str]] = None):\\n pulumi.set(__self__, \\\"group_ids\\\", group_ids)\\n if custom_email_subject is not None:\\n pulumi.set(__self__, \\\"custom_email_subject\\\", custom_email_subject)\\n if custom_webhook_payload is not None:\\n pulumi.set(__self__, \\\"custom_webhook_payload\\\", custom_webhook_payload)\",\n \"def _set_target_group(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"target_group must be of a type compatible with leafref\\\"\\\"\\\",\\n 'defined-type': \\\"leafref\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__target_group = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def _set_target_group(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"target_group must be of a type compatible with leafref\\\"\\\"\\\",\\n 'defined-type': \\\"leafref\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__target_group = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def _set_target_group(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"target_group must be of a type compatible with leafref\\\"\\\"\\\",\\n 'defined-type': \\\"leafref\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=ReferenceType(referenced_path='../../../../../../../forwarding-groups/forwarding-group/config/name', caller=self._path() + ['target-group'], path_helper=self._path_helper, require_instance=True), is_leaf=True, yang_name=\\\"target-group\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/qos', defining_module='openconfig-qos', yang_type='leafref', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__target_group = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def create_group_command(client: MsGraphClient, args: dict) -> tuple[str, dict, dict]:\\n required_properties = {\\n 'displayName': str(args.get('display_name')),\\n 'mailNickname': str(args.get('mail_nickname')),\\n 'mailEnabled': args.get('mail_enabled') == 'true',\\n 'securityEnabled': args.get('security_enabled')\\n }\\n\\n # create the group\\n group = client.create_group(required_properties)\\n\\n # display the new group and it's properties\\n group_readable, group_outputs = parse_outputs(group)\\n human_readable = tableToMarkdown(name=f\\\"{required_properties['displayName']} was created successfully:\\\",\\n t=group_readable,\\n headers=['ID', 'Display Name', 'Description', 'Created Date Time', 'Mail',\\n 'Security Enabled', 'Mail Enabled'],\\n removeNull=True)\\n entry_context = {f'{INTEGRATION_CONTEXT_NAME}(val.ID === obj.ID)': group_outputs}\\n return human_readable, entry_context, group\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: DiskReplicaGroupArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def add_argument(self, *args, **kwargs):\\n\\n if 'group' not in kwargs or kwargs['group'] is None:\\n self._parser.add_argument(*args, **kwargs)\\n\\n else:\\n group = self._group_by_title(kwargs['group'])\\n\\n if group is None:\\n raise ValueError(\\n 'Trying to reference nonexisten argument group.'\\n )\\n\\n else:\\n kwargsr = {k: kwargs[k] for k in kwargs.keys() if 'group' != k}\\n group.add_argument(*args, **kwargsr)\",\n \"def build_task_args(self, test_name):\\n task_args = {'service_list': [test_name]}\\n task_args['image_name'] = str(self.image.name)\\n task_args['flavor_name'] = str(self.flavor.name)\\n task_args['flavor_alt_name'] = str(self.flavor_alt.name)\\n task_args['glance_image_location'] = str(self.filename)\\n task_args['glance_image_format'] = str(self.image_format)\\n task_args['tmpl_dir'] = str(self.template_dir)\\n task_args['sup_dir'] = str(self.support_dir)\\n task_args['users_amount'] = self.users_amount\\n task_args['tenants_amount'] = self.tenants_amount\\n task_args['use_existing_users'] = False\\n task_args['iterations'] = self.iterations_amount\\n task_args['concurrency'] = self.concurrency\\n task_args['smoke'] = self.smoke\\n task_args['volume_version'] = self.volume_version\\n task_args['volume_service_type'] = self.volume_service_type\\n task_args['block_migration'] = env.get(\\\"BLOCK_MIGRATION\\\").lower()\\n task_args['username'] = self.username\\n\\n if self.ext_net:\\n task_args['floating_network'] = str(self.ext_net.name)\\n else:\\n task_args['floating_network'] = ''\\n\\n if self.network:\\n task_args['netid'] = str(self.network.id)\\n else:\\n LOGGER.warning(\\n 'No tenant network created. '\\n 'Trying EXTERNAL_NETWORK as a fallback')\\n if env.get(\\\"EXTERNAL_NETWORK\\\"):\\n network = self.cloud.get_network(env.get(\\\"EXTERNAL_NETWORK\\\"))\\n task_args['netid'] = str(network.id) if network else ''\\n else:\\n task_args['netid'] = ''\\n\\n return task_args\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: FailoverGroupArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"async def create(\\n self,\\n resource_group_name: str,\\n project_name: str,\\n group_name: str,\\n group: Optional[\\\"models.Group\\\"] = None,\\n **kwargs\\n ) -> \\\"models.Group\\\":\\n cls = kwargs.pop('cls', None) # type: ClsType[\\\"models.Group\\\"]\\n error_map = {\\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\\n }\\n error_map.update(kwargs.pop('error_map', {}))\\n api_version = \\\"2018-06-01\\\"\\n content_type = kwargs.pop(\\\"content_type\\\", \\\"application/json\\\")\\n accept = \\\"application/json\\\"\\n\\n # Construct URL\\n url = self.create.metadata['url'] # type: ignore\\n path_format_arguments = {\\n 'subscriptionId': self._serialize.url(\\\"self._config.subscription_id\\\", self._config.subscription_id, 'str'),\\n 'resourceGroupName': self._serialize.url(\\\"resource_group_name\\\", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\\\\w\\\\._\\\\(\\\\)]+$'),\\n 'projectName': self._serialize.url(\\\"project_name\\\", project_name, 'str'),\\n 'groupName': self._serialize.url(\\\"group_name\\\", group_name, 'str'),\\n }\\n url = self._client.format_url(url, **path_format_arguments)\\n\\n # Construct parameters\\n query_parameters = {} # type: Dict[str, Any]\\n query_parameters['api-version'] = self._serialize.query(\\\"api_version\\\", api_version, 'str')\\n\\n # Construct headers\\n header_parameters = {} # type: Dict[str, Any]\\n header_parameters['Content-Type'] = self._serialize.header(\\\"content_type\\\", content_type, 'str')\\n header_parameters['Accept'] = self._serialize.header(\\\"accept\\\", accept, 'str')\\n\\n body_content_kwargs = {} # type: Dict[str, Any]\\n if group is not None:\\n body_content = self._serialize.body(group, 'Group')\\n else:\\n body_content = None\\n body_content_kwargs['content'] = body_content\\n request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)\\n pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [200, 201]:\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n raise HttpResponseError(response=response, error_format=ARMErrorFormat)\\n\\n response_headers = {}\\n if response.status_code == 200:\\n response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))\\n deserialized = self._deserialize('Group', pipeline_response)\\n\\n if response.status_code == 201:\\n response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))\\n deserialized = self._deserialize('Group', pipeline_response)\\n\\n if cls:\\n return cls(pipeline_response, deserialized, response_headers)\\n\\n return deserialized\",\n \"def _get_argparse_kwargs(self, group, **kwargs):\\n kwargs = super(MultiOpt, self)._get_argparse_kwargs(group)\\n if not self.positional:\\n kwargs['action'] = 'append'\\n else:\\n kwargs['nargs'] = '*'\\n return kwargs\",\n \"def options_argument_group(parser):\\n group = parser.add_argument_group()\\n\\n parser.add_argument(\\n \\\"--encryption\\\",\\n dest=\\\"encryption\\\",\\n help=\\\"Optionally include this flag to encrypt/decrypt a file\\\"\\n \\\" using the key provided.\\\",\\n default=None,\\n )\\n parser.add_argument(\\n \\\"-f\\\",\\n \\\"--ethfile\\\",\\n dest=\\\"ethfilename\\\",\\n help=\\\"\\\"\\\"Optionally specify a JSON file to store or load ethernet configuration data.\\\"\\\"\\\",\\n action=\\\"append\\\",\\n default=None,\\n )\",\n \"def subparser(cls, parent):\\n parser = parent.add_parser('attach', help='attach to container')\\n parser.add_argument(\\n '--image',\\n help='image to instantiate and attach to',\\n )\\n parser.add_argument(\\n 'command',\\n nargs='*',\\n help='image to instantiate and attach to',\\n )\\n parser.set_defaults(class_=cls, method='attach')\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: BasicEndpointGroupArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(self, group_id, generation_id, member_id, group_assignment):\\n self.group_id = group_id\\n self.generation_id = generation_id\\n self.member_id = member_id\\n self.group_assignment = group_assignment\",\n \"def _set_arguments(self):\\n cert_location = f\\\"dependencies{sep}certificates{sep}localuser.crt\\\"\\n key_location = f\\\"dependencies{sep}certificates{sep}localuser.key\\\"\\n assert Path(cert_location).exists(), (\\n f\\\"The certificate isn't \\\"\\n f\\\"present at location {Path(cert_location).absolute()}\\\"\\n )\\n assert Path(key_location).exists(), (\\n f\\\"The certificate key isn't \\\"\\n f\\\"present at location {Path(key_location).absolute()}\\\"\\n )\\n self._arguments = [\\n (\\n \\\"test-certificate-verify\\\",\\n [\\\"-k\\\", key_location, \\\"-c\\\", cert_location],\\n ),\\n (\\n \\\"test-sig-algs\\\",\\n [],\\n ),\\n (\\n \\\"test-clienthello-md5\\\",\\n [],\\n ),\\n (\\n \\\"test-tls13-pkcs-signature\\\",\\n [],\\n ),\\n ]\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: SecGroupRuleArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def test_create_resource_group(self):\\n pass\",\n \"def _build_arguments(self):\\n # TODO: comeback to allow test path override. maybe?\\n # self._parser.add_argument(\\n # '--test-path',\\n # type=utils.validate_path,\\n # required=False,\\n # help=('Path th projects test Dockerfile. Dockerfile should be in the root of the test directory.')\\n # )\\n self._parser.add_argument(\\n '--configs',\\n type=bool,\\n required=False,\\n default=False,\\n help=\\\"Would you like to inject configuration files?\\\"\\n )\",\n \"def createGroup(self, *group):\\n if not self.rank:\\n logging.info('Creating atom group {}'.format(group))\\n\\n if not len(group):\\n for idSS in self.pargs['idSS']:\\n self.lmp.command('group group{} type {}'.format(idSS, idSS))\\n else:\\n self.lmp.command('group ' + ('{} ' * len(group)).format(*group))\",\n \"def test_positionalargs_4(self):\\n\\n extra_cons = set([Constraint('animal', ['moose', 'kangaroo']),\\n Constraint('colour', ['blue'])])\\n\\n the_process_unit = ProcessUnit([self.a_pattern_ds], '/another/%file%/%pattern%_%animal%_%colour%.txt',\\n 'echo', extra_constraints=extra_cons,\\n positional_args=[('animal', 0), ('colour', 1)])\\n ds_result = the_process_unit.execute(simulate=True)\\n\\n expected_string = self.script_header + 'mkdir -p /another/file_1\\\\necho moose blue test_file1 /another/file_1/pattern_1_moose_blue.txt\\\\necho kangaroo blue test_file1 /another/file_1/pattern_1_kangaroo_blue.txt\\\\n'\\n self.assertEqual(expected_string, the_process_unit.scheduler.job.to_str())\",\n \"def _create_child_group(self, name) -> \\\"GroupBase\\\":\\n pass\",\n \"def AddCommandArgGroup(parser):\\n command_group = parser.add_argument_group(\\n help='These arguments are used to run commands using SSH.'\\n )\\n command_group.add_argument(\\n '--command',\\n required=True,\\n help=\\\"\\\"\\\"\\\\\\n Command to run on the Cloud TPU VM.\\n\\n Runs the command on the target Cloud TPU Queued Resource's nodes and then exits.\\n\\n Note: in the case of a TPU Pod, it will only run the command in the\\n workers specified with the `--worker` flag (defaults to worker all if not\\n set).\\n \\\"\\\"\\\",\\n )\\n command_group.add_argument(\\n '--output-directory',\\n help=\\\"\\\"\\\"\\\\\\n Path to the directory to output the logs of the commands.\\n\\n The path can be relative or absolute. The directory must already exist.\\n\\n If not specified, standard output will be used.\\n\\n The logs will be written in files named {WORKER_ID}.log. For example:\\n \\\"2.log\\\".\\n \\\"\\\"\\\",\\n )\",\n \"def create_group(self, **kwargs):\\n post_body = json.dumps({'group': kwargs})\\n resp, body = self.post('groups', post_body)\\n self.expected_success(201, resp.status)\\n body = json.loads(body)\\n return rest_client.ResponseBody(resp, body)\",\n \"def __init__(\\n self,\\n group,\\n controls):\\n self.group = group\\n self.controls = controls\",\n \"def create_target_groups(self):\\n target_groups_config = self.get_target_groups_config()\\n vpc_id = self.get_vpc_id()\\n response_data = {}\\n\\n for short_name in target_groups_config.keys():\\n target_group_name = self.get_target_group_name(short_name)\\n\\n if self.target_group_exists(short_name):\\n self.logger.info(f'Target group {target_group_name} exists, skipping creation.')\\n continue\\n\\n response = self.client.create_target_group(\\n Name=target_group_name,\\n VpcId=vpc_id,\\n **target_groups_config[short_name],\\n )\\n assert response['ResponseMetadata']['HTTPStatusCode'] == 200\\n\\n self.logger.info(f'Target group {target_group_name} created.')\\n response_data[target_group_name] = response['TargetGroups']\\n\\n return response_data\",\n \"def _AcquireArgs(self):\\n # A Command subclass can define a _Flags() method.\\n # Calliope sets up _Flags() and should not affect the legacy setting.\\n legacy = self.ai.display_info.legacy\\n self._common_type._Flags(self.ai) # pylint: disable=protected-access\\n self.ai.display_info.legacy = legacy\\n # A command implementation can optionally define an Args() method.\\n self._common_type.Args(self.ai)\\n\\n if self._parent_group:\\n # Add parent flags to children, if they aren't represented already\\n for flag in self._parent_group.GetAllAvailableFlags():\\n if flag.is_replicated:\\n # Each command or group gets its own unique help flags.\\n continue\\n if flag.do_not_propagate:\\n # Don't propagate down flags that only apply to the group but not to\\n # subcommands.\\n continue\\n if flag.required:\\n # It is not easy to replicate required flags to subgroups and\\n # subcommands, since then there would be two+ identical required\\n # flags, and we'd want only one of them to be necessary.\\n continue\\n try:\\n self.ai.AddFlagActionFromAncestors(flag)\\n except argparse.ArgumentError:\\n raise parser_errors.ArgumentException(\\n 'repeated flag in {command}: {flag}'.format(\\n command=self.dotted_name,\\n flag=flag.option_strings))\\n # Update parent display_info in children, children take precedence.\\n self.ai.display_info.AddLowerDisplayInfo(\\n self._parent_group.ai.display_info)\",\n \"def setup_args(**kargs):\\n args = [get_nupack_exec_path(kargs['exec_name']),\\n '-material', kargs['material'], '-sodium', kargs['sodium'],\\n '-magnesium', kargs['magnesium'], '-dangles', kargs['dangles'], '-T', kargs['T']]\\n if kargs['multi']: args += ['-multi']\\n if kargs['pseudo']: args += ['-pseudo']\\n return args\",\n \"def _get_argparse_kwargs(self, group, **kwargs):\\n kwargs = super(_ConfigDirOpt, self)._get_argparse_kwargs(group)\\n kwargs['action'] = self.ConfigDirAction\\n return kwargs\",\n \"def add_arguments(cls, key: str, parser: argparse.ArgumentParser):\\n group = parser.add_argument_group(f\\\"{cls.alias} scheduler\\\")\\n cls._add_arguments(_PrefixParser(parser=group, prefix=f\\\"--{key}-{cls.alias}-\\\"))\\n return parser\",\n \"def fill_parser(self, parser):\\n parser.add_argument(\\n \\\"charm_name\\\",\\n metavar=\\\"charm-name\\\",\\n help=\\\"The charm name to associate the resource\\\",\\n )\\n parser.add_argument(\\\"resource_name\\\", metavar=\\\"resource-name\\\", help=\\\"The resource name\\\")\\n group = parser.add_mutually_exclusive_group(required=True)\\n group.add_argument(\\n \\\"--filepath\\\",\\n type=SingleOptionEnsurer(useful_filepath),\\n help=\\\"The file path of the resource content to upload\\\",\\n )\\n group.add_argument(\\n \\\"--image\\\",\\n type=SingleOptionEnsurer(str),\\n help=\\\"The digest of the OCI image\\\",\\n )\",\n \"def setup_args_create(parser):\\n parser.add_argument(\\\"--domain\\\", required=False)\\n parser.add_argument(\\\"--ansible\\\", required=False,\\n dest=\\\"ansible\\\", action=\\\"store_true\\\")\\n return parser\",\n \"def build_task_args(self, test_name):\\n task_args = {}\\n if self.ext_net:\\n task_args['floating_network'] = str(self.ext_net.name)\\n else:\\n task_args['floating_network'] = ''\\n task_args['image_name'] = str(self.image.name)\\n task_args['flavor_name'] = str(self.flavor.name)\\n return task_args\",\n \"def __init__(self):\\n self._parser = argparse.ArgumentParser(description='Arguments for talking to vCenter')\\n self._standard_args_group = self._parser.add_argument_group('standard arguments')\\n self._specific_args_group = self._parser.add_argument_group('sample-specific arguments')\\n\\n # because -h is reserved for 'help' we use -s for service\\n self._standard_args_group.add_argument('-s', '--host',\\n required=True,\\n action='store',\\n help='vSphere service address to connect to')\\n\\n # because we want -p for password, we use -o for port\\n self._standard_args_group.add_argument('-o', '--port',\\n type=int,\\n default=443,\\n action='store',\\n help='Port to connect on')\\n\\n self._standard_args_group.add_argument('-u', '--user',\\n required=True,\\n action='store',\\n help='User name to use when connecting to host')\\n\\n self._standard_args_group.add_argument('-p', '--password',\\n required=False,\\n action='store',\\n help='Password to use when connecting to host')\\n\\n self._standard_args_group.add_argument('-nossl', '--disable-ssl-verification',\\n required=False,\\n action='store_true',\\n help='Disable ssl host certificate verification')\",\n \"def __on_group_created(self, logger, *args):\",\n \"def target_group_identifier(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"target_group_identifier\\\")\",\n \"def __init__(__self__, resource_name, opts=None, attributes=None, name=None, parent_id=None, realm_id=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['attributes'] = attributes\\n __props__['name'] = name\\n __props__['parent_id'] = parent_id\\n if realm_id is None:\\n raise TypeError(\\\"Missing required property 'realm_id'\\\")\\n __props__['realm_id'] = realm_id\\n __props__['path'] = None\\n super(Group, __self__).__init__(\\n 'keycloak:index/group:Group',\\n resource_name,\\n __props__,\\n opts)\",\n \"def create_mailing_list_group(sender, instance, **kwargs):\\n\\tname = instance.name\\n\\treturn requests.post(\\\"https://api.mailgun.net/v3/lists\\\",\\n auth=('api', settings.MAILGUN_API_KEY),\\n data={'address': '{}@arenbergorkest.be'.format(name),\\n 'name': name})\",\n \"def __init__(self, name, group=None):\\n self.name = name\\n self.group = group\",\n \"def __init__(self,dataset=scripts,group=\\\"\\\"):\\n self.dataset = dataset\\n self.group=group\",\n \"def add_arguments(self):\\n if self.name == \\\"ptchunk\\\":\\n group = self.parser\\n else:\\n group = self.parser.add_argument_group(\\\"generic optional arguments\\\")\\n if self.name == \\\"ptchunk\\\":\\n self.parser.add_argument(\\n \\\"addresses\\\", nargs=\\\"*\\\", default=None,\\n help=\\\"Address(es) to ptmalloc chunk headers\\\"\\n )\\n group.add_argument(\\n \\\"-v\\\", \\\"--verbose\\\", dest=\\\"verbose\\\", action=\\\"count\\\", default=0,\\n help=\\\"Use verbose output (multiple for more verbosity)\\\"\\n )\\n group.add_argument(\\n \\\"-h\\\", \\\"--help\\\", dest=\\\"help\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"Show this help\\\"\\n )\\n if self.name == \\\"ptchunk\\\":\\n group.add_argument(\\n \\\"-c\\\", \\\"--count\\\", dest=\\\"count\\\", type=h.check_count_value, default=1,\\n help=\\\"\\\"\\\"Number of chunks to print linearly (also supports \\\"unlimited\\\"/0\\nor negative numbers to print chunks going backwards)\\\"\\\"\\\"\\n )\\n group.add_argument(\\n \\\"-x\\\", \\\"--hexdump\\\", dest=\\\"hexdump\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"Hexdump the chunk contents\\\"\\n )\\n group.add_argument(\\n \\\"-X\\\", dest=\\\"hexdump_unit\\\", type=h.check_hexdump_unit, default=1,\\n help=f\\\"Specify hexdump unit ({h.prepare_list(h.hexdump_units)}) when using -x (default: %(default)s)\\\"\\n )\\n group.add_argument(\\n \\\"-m\\\", \\\"--maxbytes\\\", dest=\\\"maxbytes\\\", type=h.string_to_int, default=0,\\n help=\\\"Max bytes to dump with -x\\\"\\n )\\n if self.name == \\\"ptchunk\\\":\\n group.add_argument(\\n \\\"-n\\\", dest=\\\"no_newline\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"Do not output the trailing newline (summary representation)\\\"\\n )\\n group.add_argument(\\n \\\"-p\\\", dest=\\\"print_offset\\\", type=h.string_to_int, default=0,\\n help=\\\"Print data inside at given offset (summary representation)\\\"\\n )\\n group.add_argument(\\n \\\"-M\\\", \\\"--metadata\\\", dest=\\\"metadata\\\", type=str, default=None,\\n help=\\\"Comma separated list of metadata to print (previously stored with the 'ptmeta' command)\\\"\\n )\\n if self.name == \\\"ptchunk\\\" or self.name == \\\"ptlist\\\":\\n group.add_argument(\\n \\\"-I\\\", \\\"--highlight-types\\\", dest=\\\"highlight_types\\\", type=str, default=None,\\n help=\\\"Comma separated list of chunk types (M, F, f or t) for chunks we want to highlight in the output\\\"\\n )\\n group.add_argument(\\n \\\"-H\\\", \\\"--highlight-addresses\\\", dest=\\\"highlight_addresses\\\", type=str, default=None,\\n help=\\\"Comma separated list of addresses for chunks we want to highlight in the output\\\"\\n )\\n group.add_argument(\\n \\\"-G\\\", \\\"--highlight-metadata\\\", dest=\\\"highlight_metadata\\\", type=str, default=None,\\n help=\\\"\\\"\\\"Comma separated list of metadata (previously stored with the 'ptmeta' command) \\nfor chunks we want to highlight in the output\\\"\\\"\\\"\\n )\\n group.add_argument(\\n \\\"--highlight-only\\\", dest=\\\"highlight_only\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"Only show the highlighted chunks (instead of just '*' them)\\\"\\n )\\n if self.name != \\\"ptfree\\\":\\n group.add_argument(\\n \\\"--use-cache\\\", dest=\\\"use_cache\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"\\\"\\\"Do not fetch any internal ptmalloc data if you know they haven't changed since\\nlast time they were cached\\\"\\\"\\\"\\n )\\n group.add_argument(\\n \\\"--json\\\", dest=\\\"json_filename\\\", type=str, default=None,\\n help=\\\"Specify the json filename to save the output (Useful to diff 2 outputs)\\\"\\n )\\n group.add_argument(\\n \\\"--json-append\\\", dest=\\\"json_append\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"Append to the filename instead of overwriting\\\"\\n )\\n group.add_argument(\\n \\\"-s\\\", \\\"--search\\\", dest=\\\"search_value\\\", type=str, default=None,\\n help=\\\"Search a value and show match/no match\\\"\\n )\\n group.add_argument(\\n \\\"-S\\\", \\\"--search-type\\\", dest=\\\"search_type\\\", type=str, default=\\\"string\\\",\\n help=f\\\"Specify search type ({h.prepare_list(ptchunk.search_types)}) when using -s (default: %(default)s)\\\"\\n )\\n group.add_argument(\\n \\\"--match-only\\\", dest=\\\"match_only\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"Only show the matched chunks (instead of just show match/no match)\\\"\\n )\\n group.add_argument(\\n \\\"--skip-header\\\", dest=\\\"skip_header\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"Don't include chunk header contents in search results\\\"\\n )\\n group.add_argument(\\n \\\"--depth\\\", dest=\\\"search_depth\\\", type=h.string_to_int, default=0,\\n help=\\\"How far into each chunk to search, starting from chunk header address\\\"\\n )\\n group.add_argument(\\n \\\"--cmds\\\", dest=\\\"commands\\\", type=str, default=None,\\n help=\\\"\\\"\\\"Semi-colon separated list of debugger commands to be executed for each chunk that is displayed \\n('@' is replaced by the chunk address)\\\"\\\"\\\"\\n )\\n # allows to enable a different log level during development/debugging\\n self.parser.add_argument(\\n \\\"--loglevel\\\", dest=\\\"loglevel\\\", default=None,\\n help=argparse.SUPPRESS\\n )\\n # Debug and force printing stuff\\n self.parser.add_argument(\\n \\\"-d\\\", \\\"--debug\\\", dest=\\\"debug\\\", action=\\\"store_true\\\", default=False,\\n help=argparse.SUPPRESS\\n )\\n group.add_argument(\\n \\\"-o\\\", \\\"--address-offset\\\", dest=\\\"address_offset\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"Print offsets from the first printed chunk instead of addresses\\\"\\n )\",\n \"def __init__(self, group_id=None):\\n super().__init__()\\n self.group_id = group_id\",\n \"def builder_should_create_target_image(self, builder, target, image_id, template, parameters):\",\n \"def inspect_arguments(self, args):\\n if not os.path.exists(self.output_dir):\\n os.makedirs(self.output_dir)\\n\\n if not os.path.exists(self.input_dir):\\n raise ValueError('Specified input directory could not be found.')\\n\\n self.group_distributions_file = os.path.join(self.input_dir, self.group_distributions_file)\\n if not os.path.exists(self.group_distributions_file):\\n raise ValueError('Specified groups distribution file could not be found in input directory.')\\n\\n self.group_probabilities_file = os.path.join(self.input_dir, self.group_probabilities_file)\\n if not os.path.exists(self.group_probabilities_file):\\n raise ValueError('Specified groups probabilities file could not be found in input directory.')\",\n \"def add_key_arguments(parser):\\n group = parser.add_argument_group(\\\"Key management\\\")\\n group.add_argument(\\n \\\"-ks\\\", \\\"--key_size\\\",\\n help='Length of the new key',\\n type=int,\\n default=4096\\n )\\n group.add_argument(\\n \\\"-kt\\\", \\\"--key_type\\\",\\n help=\\\"Method used for generating the new key\\\",\\n choices=[\\\"dsa\\\", \\\"rsa\\\"],\\n default=\\\"rsa\\\"\\n )\\n return group\",\n \"def addTargets(self, **kwargs):\\n self.targets.update(kwargs)\\n for key, val in kwargs.items():\\n self.namespace[key] = val\",\n \"def command(self, *args, **kwargs):\\n help_group = kwargs.pop(\\\"group\\\", None)\\n decorator = super(GroupedGroup, self).command(*args, **kwargs)\\n\\n def wrapper(f):\\n cmd = decorator(f)\\n cmd.help_group = help_group\\n return cmd\\n\\n return wrapper\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77026486","0.74800813","0.7191172","0.61384636","0.57487035","0.5611284","0.56056815","0.5473555","0.5462698","0.5453623","0.5441378","0.54287297","0.54172635","0.53831756","0.51655686","0.51062083","0.5021804","0.5019914","0.50194347","0.501577","0.5005552","0.49847272","0.49401894","0.49053487","0.48868665","0.48712045","0.48579642","0.48458067","0.48096326","0.48077473","0.48002097","0.47940004","0.47931182","0.4782493","0.4777999","0.47687408","0.47687408","0.47687408","0.47687408","0.47687408","0.47687408","0.47662467","0.47522306","0.47494787","0.47430575","0.4735151","0.4729942","0.47281635","0.47261596","0.4719306","0.4708943","0.4708943","0.4708943","0.47081625","0.470202","0.470202","0.470202","0.47009057","0.46951732","0.46929315","0.46632764","0.4642575","0.46371147","0.46324196","0.46308357","0.4629207","0.46175858","0.46141636","0.45930234","0.45899215","0.45593256","0.45441988","0.45405668","0.45404315","0.45261028","0.4519363","0.4516188","0.45121437","0.45063406","0.45052373","0.45040768","0.4498982","0.4490006","0.44838467","0.44715944","0.44641486","0.44608563","0.4447805","0.44461513","0.44435948","0.4440404","0.4430319","0.44289896","0.44232082","0.44159275","0.44096693","0.44094443","0.44075155","0.44065648","0.439325"],"string":"[\n \"0.77026486\",\n \"0.74800813\",\n \"0.7191172\",\n \"0.61384636\",\n \"0.57487035\",\n \"0.5611284\",\n \"0.56056815\",\n \"0.5473555\",\n \"0.5462698\",\n \"0.5453623\",\n \"0.5441378\",\n \"0.54287297\",\n \"0.54172635\",\n \"0.53831756\",\n \"0.51655686\",\n \"0.51062083\",\n \"0.5021804\",\n \"0.5019914\",\n \"0.50194347\",\n \"0.501577\",\n \"0.5005552\",\n \"0.49847272\",\n \"0.49401894\",\n \"0.49053487\",\n \"0.48868665\",\n \"0.48712045\",\n \"0.48579642\",\n \"0.48458067\",\n \"0.48096326\",\n \"0.48077473\",\n \"0.48002097\",\n \"0.47940004\",\n \"0.47931182\",\n \"0.4782493\",\n \"0.4777999\",\n \"0.47687408\",\n \"0.47687408\",\n \"0.47687408\",\n \"0.47687408\",\n \"0.47687408\",\n \"0.47687408\",\n \"0.47662467\",\n \"0.47522306\",\n \"0.47494787\",\n \"0.47430575\",\n \"0.4735151\",\n \"0.4729942\",\n \"0.47281635\",\n \"0.47261596\",\n \"0.4719306\",\n \"0.4708943\",\n \"0.4708943\",\n \"0.4708943\",\n \"0.47081625\",\n \"0.470202\",\n \"0.470202\",\n \"0.470202\",\n \"0.47009057\",\n \"0.46951732\",\n \"0.46929315\",\n \"0.46632764\",\n \"0.4642575\",\n \"0.46371147\",\n \"0.46324196\",\n \"0.46308357\",\n \"0.4629207\",\n \"0.46175858\",\n \"0.46141636\",\n \"0.45930234\",\n \"0.45899215\",\n \"0.45593256\",\n \"0.45441988\",\n \"0.45405668\",\n \"0.45404315\",\n \"0.45261028\",\n \"0.4519363\",\n \"0.4516188\",\n \"0.45121437\",\n \"0.45063406\",\n \"0.45052373\",\n \"0.45040768\",\n \"0.4498982\",\n \"0.4490006\",\n \"0.44838467\",\n \"0.44715944\",\n \"0.44641486\",\n \"0.44608563\",\n \"0.4447805\",\n \"0.44461513\",\n \"0.44435948\",\n \"0.4440404\",\n \"0.4430319\",\n \"0.44289896\",\n \"0.44232082\",\n \"0.44159275\",\n \"0.44096693\",\n \"0.44094443\",\n \"0.44075155\",\n \"0.44065648\",\n \"0.439325\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7296756"},"document_rank":{"kind":"string","value":"2"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":745,"numItemsPerPage":100,"numTotalItems":95772,"offset":74500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjY5MzI4OCwianRpIjoiYjg5OTUwNzItYTgzMi00ZDIwLWI2ODUtMzIyMzg5YmJkNjcwIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY2OTY4ODgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.jt7kYOOA51exXjEaCueKd7kWnYBDR4gWpFfn8aaFHZNAU8xuKHS1AlaDtjh-4ZXoy1-igrzeTJN30w6ZsaPjBw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Create a new clip | def add_clip(md5=None, pos=None):
m = request.args.get('md5', None) if md5 is None else md5
u = Upload.objects.filter(md5=m).first()
if not u:
abort(404)
p = request.args.get('pos', None) if pos is None else pos
xt, t, xb, b = parse_pos(p)
# make the form
form = ReferenceForm(formdat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def newClip(self, book, content, typ, date):\n sql = ''' insert into clippings values (NULL, '%s', '%s', '%s', '%s', '%s')\n''' % (book, '0', typ, date, content)\n\n self.__execute__(sql)\n pass",
"def __init__(self,clip_list):\n self.requested_clips=clip_list",
"def create_sourc... | [
"0.6420943",
"0.6345022",
"0.6147171",
"0.6049804",
"0.59528327",
"0.5941645",
"0.5813467",
"0.5794194",
"0.57903296",
"0.5784902",
"0.5754241",
"0.5682217",
"0.5648178",
"0.564117",
"0.5551281",
"0.5475857",
"0.5473375",
"0.5430865",
"0.54280514",
"0.53824615",
"0.5374996",
... | 0.5473534 | 16 |
Create a new clip | def add_excerpt(md5=None, pos=None):
m = request.args.get('md5', None) if md5 is None else md5
u = Upload.objects.filter(md5=m).first()
if not u:
abort(404)
p = request.args.get('pos', None) if pos is None else pos
xt, t, xb, b = parse_pos(p)
# make the form
form = ReferenceForm(form... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def newClip(self, book, content, typ, date):\n sql = ''' insert into clippings values (NULL, '%s', '%s', '%s', '%s', '%s')\n''' % (book, '0', typ, date, content)\n\n self.__execute__(sql)\n pass",
"def __init__(self,clip_list):\n self.requested_clips=clip_list",
"def create_sourc... | [
"0.6420943",
"0.6345022",
"0.6147171",
"0.6049804",
"0.59528327",
"0.5941645",
"0.5813467",
"0.5794194",
"0.57903296",
"0.5784902",
"0.5754241",
"0.5682217",
"0.5648178",
"0.564117",
"0.5551281",
"0.5475857",
"0.5473534",
"0.5473375",
"0.5430865",
"0.54280514",
"0.53824615",
... | 0.0 | -1 |
Conduct a search inside the text | def search_inside(md5):
search_results = {}
query = request.args.get('query', '')
if not query == '':
subqueries = query.split(',')
q_idx = 0
for q in subqueries:
if q_idx == 3:
continue
results = elastic.search('page',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search(self, word):",
"def search(self, term):",
"def search(query_string):",
"def search(text):\n s = Search()\n result = _search(s, text)\n _print_results(result)\n return result",
"def exact_match(self):\n text_to_match = input(\"Enter the text to search for> \")\n return t... | [
"0.7854131",
"0.7629025",
"0.69847673",
"0.69339067",
"0.6899407",
"0.67962325",
"0.6743438",
"0.6727335",
"0.6530044",
"0.6522045",
"0.64815676",
"0.64747185",
"0.64260924",
"0.6422477",
"0.64208305",
"0.64173126",
"0.64115167",
"0.64115167",
"0.6405989",
"0.6405989",
"0.637... | 0.0 | -1 |
The filename here is the structured filename | def references(md5):
u = Upload.objects.filter(md5=md5).first()
if not u:
abort(404)
# first, is this searchable?
is_searchable = False
count = elastic.count('page', filter={'md5': md5})
if count > 0:
is_searchable = True
#annotations = Reference.objects.filter(upload=u, ref_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getfilename(self):\n pass",
"def filename(self):\r\n return self._filename",
"def filename(self):\n return '%s%s' % (self.identifier, self.extension)",
"def filename(self):\r\n\t\treturn None",
"def GetFileName():\r\n d = GetData()\r\n return d.filename",
"def _prettyf... | [
"0.7109889",
"0.70361847",
"0.6928823",
"0.68720216",
"0.6811418",
"0.68030715",
"0.67876595",
"0.67835504",
"0.67760605",
"0.6728516",
"0.6689405",
"0.66837925",
"0.6678177",
"0.6658768",
"0.6623499",
"0.66140795",
"0.6607564",
"0.66051894",
"0.66051894",
"0.66051894",
"0.66... | 0.0 | -1 |
The filename here is the structured filename | def figleaf(md5, user_id=None):
u = Upload.objects.filter(md5=md5).first()
if not u:
abort(404)
thing = Thing.objects.filter(files=u).first()
if thing.takedown:
return thing.takedown
preview = u.preview()
preview_url = url_for('reference.preview',
filen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getfilename(self):\n pass",
"def filename(self):\r\n return self._filename",
"def filename(self):\n return '%s%s' % (self.identifier, self.extension)",
"def filename(self):\r\n\t\treturn None",
"def GetFileName():\r\n d = GetData()\r\n return d.filename",
"def _prettyf... | [
"0.7110782",
"0.7036862",
"0.6930487",
"0.6872694",
"0.6812203",
"0.68041015",
"0.67890775",
"0.6783583",
"0.6777206",
"0.6729934",
"0.6691172",
"0.66841674",
"0.66787475",
"0.66599196",
"0.66246134",
"0.6615134",
"0.6608568",
"0.66060024",
"0.66060024",
"0.66060024",
"0.6606... | 0.0 | -1 |
Adds a reference notation to an upload | def create_reference(md5, pos):
url = request.args.get('url', '')
if not rfc3987.match(url, rule='URI'):
return "Sorry, that's not a valid URL:\n" % url
u = Upload.objects.filter(md5=md5).first()
if not u:
abort(404)
# Create the reference
r = Reference(upload=u, ref_url=url, pos... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_file_ref(self, file_name: str) -> None:\n self.referenced_in.add(file_name)",
"def _add_ref_record(\n self, term: str, concept_id: str, ref_type: str, src_name: SourceName\n ) -> None:\n label_and_type = f\"{term.lower()}##{ref_type}\"\n record = {\n \"label_and_... | [
"0.63955927",
"0.6236799",
"0.6201154",
"0.61226004",
"0.5879003",
"0.5841867",
"0.58209884",
"0.57595927",
"0.57595927",
"0.5746349",
"0.570964",
"0.56970924",
"0.56944036",
"0.5693124",
"0.5685071",
"0.5650007",
"0.5620138",
"0.560453",
"0.5603132",
"0.5598935",
"0.5551821"... | 0.57105976 | 10 |
Adds a reference notation to an upload | def create_reference2(md5, pos, ref_md5, ref_pos):
ua = Upload.objects.filter(md5=md5).first()
ub = Upload.objects.filter(md5=ref_md5).first()
if not ua or not ub:
abort(404)
# Create the reference
try:
r = Reference(upload=ua, raw_pos=pos, ref_upload=ub, raw_ref_pos=ref_pos)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_file_ref(self, file_name: str) -> None:\n self.referenced_in.add(file_name)",
"def _add_ref_record(\n self, term: str, concept_id: str, ref_type: str, src_name: SourceName\n ) -> None:\n label_and_type = f\"{term.lower()}##{ref_type}\"\n record = {\n \"label_and_... | [
"0.63955927",
"0.6236799",
"0.6201154",
"0.5879003",
"0.5841867",
"0.58209884",
"0.57595927",
"0.57595927",
"0.5746349",
"0.57105976",
"0.570964",
"0.56970924",
"0.56944036",
"0.5693124",
"0.5685071",
"0.5650007",
"0.5620138",
"0.560453",
"0.5603132",
"0.5598935",
"0.5551821"... | 0.61226004 | 3 |
Adds a reference notation to an upload | def create_annotation(md5, pos):
note = request.args.get('note', '')
u = Upload.objects.filter(md5=md5).first()
if not u:
abort(404)
# Create the reference
r = Reference(upload=u, note=note, raw_pos=pos)
# try and extract a url
urls = re.findall(r'(https?://\S+)', note)
for url i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_file_ref(self, file_name: str) -> None:\n self.referenced_in.add(file_name)",
"def _add_ref_record(\n self, term: str, concept_id: str, ref_type: str, src_name: SourceName\n ) -> None:\n label_and_type = f\"{term.lower()}##{ref_type}\"\n record = {\n \"label_and_... | [
"0.63955927",
"0.6236799",
"0.6201154",
"0.61226004",
"0.5879003",
"0.5841867",
"0.58209884",
"0.57595927",
"0.57595927",
"0.5746349",
"0.57105976",
"0.570964",
"0.56970924",
"0.56944036",
"0.5693124",
"0.5685071",
"0.5650007",
"0.5620138",
"0.560453",
"0.5603132",
"0.5598935... | 0.554887 | 22 |
A page made of clips/ highlights of the text | def clips(md5, user_id=None):
u = Upload.objects.filter(md5=md5).first()
thing = Thing.objects.filter(files=u).first()
if not u:
abort(404)
# load annotations
if user_id:
annotations = Reference.objects.filter(
upload=u, creator=user_id).order_by('pos')
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_page(page, stream):\n bleed = {\n side: page.style[f'bleed_{side}'].value\n for side in ('top', 'right', 'bottom', 'left')}\n marks = page.style['marks']\n stacking_context = StackingContext.from_page(page)\n draw_background(\n stream, stacking_context.box.background, clip... | [
"0.60184145",
"0.58296853",
"0.58064336",
"0.5726351",
"0.5624344",
"0.55240214",
"0.5515679",
"0.54783005",
"0.5467475",
"0.5448062",
"0.53821886",
"0.5334517",
"0.5334213",
"0.5322485",
"0.5315104",
"0.5283029",
"0.52648276",
"0.52377385",
"0.5237235",
"0.5227399",
"0.52244... | 0.4854962 | 81 |
A page made of clips | def reference_clips(md5):
u = Upload.objects.filter(md5=md5).first()
thing = Thing.objects.filter(files=u).first()
if not u:
abort(404)
# load annotations
annotations = Reference.objects.filter(upload=u).order_by('pos')
clips = []
for a in annotations:
if a.ref_upload and a.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clips(md5, user_id=None):\n u = Upload.objects.filter(md5=md5).first()\n thing = Thing.objects.filter(files=u).first()\n if not u:\n abort(404)\n # load annotations\n if user_id:\n annotations = Reference.objects.filter(\n upload=u, creator=user_id).order_by('pos')\n ... | [
"0.63247615",
"0.58544445",
"0.57726026",
"0.5435741",
"0.5298782",
"0.5247598",
"0.5226496",
"0.5225621",
"0.51966417",
"0.51824707",
"0.5177477",
"0.51289487",
"0.51272714",
"0.50894016",
"0.5084546",
"0.5059843",
"0.50533634",
"0.50295776",
"0.5029249",
"0.5023784",
"0.502... | 0.5818967 | 2 |
Returns the start state for the search problem | def getStartState(self):
util.raiseNotDefined() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getStartState(self):\n\t\tutil.raiseNotDefined()",
"def getStartState(self):\n\t\tutil.raiseNotDefined()",
"def getStartState(self):\n return self._start_loc",
"def getStartState(self):\r\n\t\tutil.raiseNotDefined()",
"def get_start_state(self):\r\n util.raiseNotDefined()",
"def getStar... | [
"0.72630936",
"0.72630936",
"0.72455",
"0.7215881",
"0.72052455",
"0.71886414",
"0.7183883",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
"0.711945",
... | 0.7191885 | 11 |
Returns a sequence of moves that solves tinyMaze. For any other maze, the sequence of moves will be incorrect, so only use this for tinyMaze | def tinyMazeSearch(problem):
from game import Directions
s = Directions.SOUTH
w = Directions.WEST
return [s,s,w,s,w,w,s,w] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tinyMazeSearch(problem):\n from game import Directions\n s = Directions.SOUTH\n w = Directions.WEST\n n = Directions.NORTH\n\n return [s, s, n, s, w, s, w, w, s, w]",
"def tinyMazeSearch(problem):\n from game import Directions\n\n s = Directions.SOUTH\n w = Directions.WEST\n retur... | [
"0.7231158",
"0.72270834",
"0.72268534",
"0.7179877",
"0.7179877",
"0.7179877",
"0.7173173",
"0.7173173",
"0.7173173",
"0.7173173",
"0.7173173",
"0.7173173",
"0.7173173",
"0.7159364",
"0.7159364",
"0.7159364",
"0.7159364",
"0.7159364",
"0.7159364",
"0.7159364",
"0.7159364",
... | 0.7181364 | 8 |
Search the deepest nodes in the search tree first | def depthFirstSearch(problem):
"*** YOUR CODE HERE ***"
frontier = util.Stack()
start_node = problem.getStartState()
if problem.isGoalState(start_node):
return ['Stop']
frontier.push((start_node,[]))
explored = set()
while True:
if frontier.isEmpty():
return []
node = frontier... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def depth_first_search(self):\r\n queue = [self.root]\r\n ordered = []\r\n while queue:\r\n node = queue.pop()\r\n ordered.append(node)\r\n queue.extend(node.children)\r\n \r\n while ordered:\r\n yield ordered.pop()",
"def depthFirstS... | [
"0.72788286",
"0.6862371",
"0.6833252",
"0.6678099",
"0.66124",
"0.6571768",
"0.65458554",
"0.6541518",
"0.65328586",
"0.65185565",
"0.6516232",
"0.63959724",
"0.6336154",
"0.63223875",
"0.6305312",
"0.63027865",
"0.6276852",
"0.6257763",
"0.6247078",
"0.62291026",
"0.6222544... | 0.6142328 | 30 |
Search the shallowest nodes in the search tree first. | def breadthFirstSearch(problem):
#import pdb;pdb.set_trace()
frontier = util.Queue()
start_node = problem.getStartState()
if problem.isGoalState(start_node):
return ['Stop']
frontier.push((start_node,()))
explored = set()
while True:
if frontier.isEmpty():
return []
node = fron... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def depth_first_search(self):\r\n queue = [self.root]\r\n ordered = []\r\n while queue:\r\n node = queue.pop()\r\n ordered.append(node)\r\n queue.extend(node.children)\r\n \r\n while ordered:\r\n yield ordered.pop()",
"def depth_first... | [
"0.69203365",
"0.6734959",
"0.659188",
"0.65315974",
"0.65235966",
"0.6515641",
"0.64349604",
"0.63706106",
"0.6349397",
"0.63105273",
"0.62683547",
"0.62573814",
"0.6188355",
"0.61810166",
"0.6174985",
"0.60993695",
"0.60754585",
"0.6070981",
"0.60292786",
"0.6028391",
"0.60... | 0.0 | -1 |
Search the shallowest nodes in the search tree first. | def breadthFirstSearchPaths(problem):
#import pdb;pdb.set_trace()
frontier = util.Queue()
start_node = problem.getStartState()
if problem.isGoalState(start_node):
return ['Stop']
frontier.push((start_node,()))
explored = set()
while True:
if frontier.isEmpty():
return []
node =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def depth_first_search(self):\r\n queue = [self.root]\r\n ordered = []\r\n while queue:\r\n node = queue.pop()\r\n ordered.append(node)\r\n queue.extend(node.children)\r\n \r\n while ordered:\r\n yield ordered.pop()",
"def depth_first... | [
"0.69203365",
"0.6734959",
"0.659188",
"0.65315974",
"0.65235966",
"0.6515641",
"0.64349604",
"0.63706106",
"0.6349397",
"0.63105273",
"0.62683547",
"0.62573814",
"0.6188355",
"0.61810166",
"0.6174985",
"0.60993695",
"0.60754585",
"0.6070981",
"0.60292786",
"0.6028391",
"0.60... | 0.0 | -1 |
Less is more accepts (node, [path, ...]) | def fun(item):
node, path = item
value = len(path)
if node in problem.food:
value -= 20
return value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def less_nested_example_vanilla():\n return",
"def less_nested_example_rst():\n\n return",
"def less_nested_example_googlestyle(a):\n return a",
"def explore(self, *args):",
"def traverse(name, furtherPath):",
"def process_lessjs(source, filepath):\n # This is a simple pass through, we don't ... | [
"0.58981407",
"0.5750831",
"0.5233719",
"0.5231554",
"0.5133759",
"0.5094836",
"0.50221986",
"0.50221986",
"0.49672624",
"0.489807",
"0.4756346",
"0.46777958",
"0.45781782",
"0.45700714",
"0.45614952",
"0.4553109",
"0.45303732",
"0.44701698",
"0.4464686",
"0.44632816",
"0.445... | 0.0 | -1 |
A heuristic function estimates the cost from the current state to the nearest goal in the provided SearchProblem. This heuristic is trivial. | def nullHeuristic(state, problem=None):
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def uniformCostSearch(problem):\n # Initialization\n startState = problem.getStartState()\n\n if problem.isGoalState(startState):\n return [] # No action needed\n\n closedSet = set()\n queue = util.PriorityQueue()\n queue.push((startState, None, 0), 0)\n cameFrom = dict() # Stores most ... | [
"0.7426861",
"0.7402951",
"0.7289034",
"0.7282451",
"0.72603494",
"0.7188548",
"0.7183276",
"0.7157552",
"0.710176",
"0.7078661",
"0.7068745",
"0.7043972",
"0.7041973",
"0.7037503",
"0.7026081",
"0.70163196",
"0.7001862",
"0.7000626",
"0.69820946",
"0.6965764",
"0.6959505",
... | 0.0 | -1 |
write internal data to hdf5 file | def _write_data(self, filename=None, mode="a", file_kwargs=None, **kwargs):
super()._write_data(filename, mode=mode, file_kwargs=file_kwargs, **kwargs)
if filename is None:
filename = self._cache_filename
if file_kwargs is None:
file_kwargs = {}
# writing extra... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_as_hdf5(self, filename):",
"def write_hdf5(filename, data):\n \n if '.h5' in filename:\n fid = h5py.File(filename, 'w')\n else:\n filename = filename+'.h5'\n fid = h5py.File(filename, 'w')\n\n print('Writing %s...'%filename)\n\n write_hdf5_group(fid, data)\n\n fid.... | [
"0.7799721",
"0.7452895",
"0.73623675",
"0.73379844",
"0.72146076",
"0.71774447",
"0.7131744",
"0.71247923",
"0.71233344",
"0.708684",
"0.70274025",
"0.7017833",
"0.70083266",
"0.69717515",
"0.69588876",
"0.6957693",
"0.69514626",
"0.69412285",
"0.69244695",
"0.6864818",
"0.6... | 0.6128824 | 75 |
Retrieve one modulation mask for all kids. | def mod_mask(self):
# Check the *_masq values
self.__log.debug("Checking the *_masq arrays")
# Retrieve the kid boxes
masq_names = np.unique(["{}_masq".format(item[1]) for item in self.list_detector])
self.__check_attributes(masq_names, read_missing=False)
# Check that th... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_mask(self, h, k):\n return self.mask[(self.h==h)&(self.k==k)]",
"def mask(self):\n return self._mask",
"def mask(self):\n return self._mask",
"def mask(self):\n return self._mask",
"def mask(self):\n return self._mask",
"def mask_id(self):\n m = 2 * self.mask... | [
"0.62485987",
"0.600278",
"0.600278",
"0.600278",
"0.600278",
"0.59038883",
"0.58914554",
"0.58771306",
"0.58382463",
"0.58314395",
"0.58291423",
"0.5816771",
"0.5796399",
"0.5791533",
"0.5784373",
"0.5745093",
"0.5737748",
"0.5737748",
"0.5713765",
"0.56971776",
"0.5696885",... | 0.6057825 | 1 |
Compute undersampled obstime. Properly compute the undersampled timestamp at the middle of the observed bloc | def u_obstime(self):
mask = self.mod_mask == 0
obstime = self.obstime
_u_mjd = [np.mean(_mjd[_mask]) for _mjd, _mask in zip(obstime.mjd, mask)]
if np.any(np.isnan(_u_mjd)):
_u_mjd = interp_nan(_u_mjd)
return Time(_u_mjd, format="mjd", scale=obstime.scale) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def samplingTime(self):\n return self._AWG.samplingTime_ns()",
"def __call__ (self, t):\n #if t <= self.last_t:\n #raise SpaceTimeContinuumError(\n #\"We're moving back in time! Last t = {}, now = {}\".format(\n #self.last_t, t))\n\n #samp = self._sample(t)\n #self.last_t = t... | [
"0.6286706",
"0.5756881",
"0.5651613",
"0.55432194",
"0.5527626",
"0.54772675",
"0.5466851",
"0.5460345",
"0.5403053",
"0.5351182",
"0.53404397",
"0.5338423",
"0.53370214",
"0.5308798",
"0.53036666",
"0.52976394",
"0.52803427",
"0.5275267",
"0.52614385",
"0.52611965",
"0.5260... | 0.5138226 | 38 |
Retrieve interpolated telescope position, without shift. | def _get_positions(self, coord="pdiff", undersampled=True):
if undersampled:
mjd = self.u_obstime
else:
mjd = self.obstime.flatten()
return self.telescope_positions.get_interpolated_positions(mjd, key=coord) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_original_observation(self) -> Tuple[float, np.ndarray]:\n toe_position = np.array(\n self._robot.foot_positions(), dtype=self._dtype).flatten()\n\n return self._robot.timestamp, toe_position",
"def get_pos(self):\n current_angles = self.chain.get_current_values()\n current_xyz... | [
"0.65298414",
"0.60537887",
"0.6028942",
"0.5994292",
"0.5923824",
"0.59053487",
"0.5886543",
"0.58772236",
"0.58618027",
"0.58261776",
"0.5751239",
"0.5709363",
"0.5693063",
"0.56841815",
"0.56787586",
"0.56739885",
"0.5669671",
"0.56431454",
"0.5637774",
"0.56372565",
"0.56... | 0.5776665 | 10 |
Calibrate the KIDS timeline. | def calib_raw(self, calib_func="kidsdata.kids_calib.get_calfact", clean_raw=False, **kwargs):
if getattr(self, "__calib", None) is None:
self.__log.debug("calibration using {}".format(calib_func))
self.__check_attributes(["I", "Q"], read_missing=False)
fmod = self.fmod
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calibrate(self):\n self.current_state = \"calibrate\"\n self.next_state = \"idle\"\n self.kinect.loadCameraCalibration()\n # location_strings = [\"lower left corner of board\",\n # \"upper left corner of board\",\n # \"upper righ... | [
"0.6088966",
"0.5734548",
"0.57042384",
"0.5601051",
"0.5569389",
"0.5563158",
"0.5531026",
"0.5407101",
"0.536125",
"0.534526",
"0.5302023",
"0.5276291",
"0.52127695",
"0.51961476",
"0.5159317",
"0.5137682",
"0.51142454",
"0.5114052",
"0.5102007",
"0.50755095",
"0.505544",
... | 0.0 | -1 |
Check if the data has been read an attribute and read in it if not. | def __check_attributes(self, attr_list, **kwargs):
dependancies = [
# I & Q will need A_masq
(["I", "Q"], ["I", "Q", "A_masq"]),
# Calibration data depends on the I, Q & A_masq raw data
(["calfact", "Icc", "Qcc", "P0", "R0", "interferograms", "continuum"], ["I", "... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_attribute_reads(self):\n for serialized, attrs_read in sorted(\n six.iteritems(self.attributes_read), key=self._cls_sort\n ):\n typ = self.unserialize_type(serialized)\n if typ is None:\n continue\n # we setattr on it with an unreso... | [
"0.7005799",
"0.6510016",
"0.64761245",
"0.64538723",
"0.60407066",
"0.59270316",
"0.59244365",
"0.5909545",
"0.59069735",
"0.5895281",
"0.5894877",
"0.58773875",
"0.5852896",
"0.5836397",
"0.57878715",
"0.5777165",
"0.57314855",
"0.56984305",
"0.5665628",
"0.5655421",
"0.561... | 0.52089477 | 83 |
Plot azimuth and elevation to check pointing. | def plot_pointing(self, *args, coord="tl", **kwargs):
# TODO: Generalize that function
warnings.warn("Deprecated function needs update.", DeprecationWarning)
self.__check_attributes(["F_{}_az".format(coord), " F_{}_el".format(coord)])
return kids_plots.checkPointing(self, *args, **kwargs... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fun_azimuth(self):\n\n energy_kev = self.energy_kev.get()\n hkl = self.hkl_magnetic.get()\n hkl = hkl.replace(',', ' ') # remove commas\n hkl = hkl.replace('(', '').replace(')', '') # remove brackets\n hkl = hkl.replace('[', '').replace(']', '') # remove brackets\n ... | [
"0.67514294",
"0.6518079",
"0.6481947",
"0.6122301",
"0.6084989",
"0.5980787",
"0.595821",
"0.591427",
"0.59134316",
"0.5898727",
"0.58961886",
"0.5819812",
"0.58101857",
"0.5774152",
"0.5765768",
"0.56982815",
"0.5673791",
"0.5673638",
"0.56489426",
"0.56288075",
"0.5618178"... | 0.53098726 | 59 |
Read raw data. Also read the calibrated data in the cache file if present. Notes The different Kiss telescope positions are also handled | def read_data(self, *args, cache=False, array=np.array, **kwargs):
super().read_data(*args, cache=cache, array=array, **kwargs)
if cache and self._cache is not None:
self.__log.info("Reading cached data :")
datas = []
for data in ["calib"]:
datas.appe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_calibration_data(self):\n #Declare global variables.\n global calDig_T1\n global calDig_T2\n global calDig_T3\n global calDig_P1\n global calDig_P2\n global calDig_P3\n global calDig_P4\n global calDig_P5\n global calDig_P6\n gl... | [
"0.6597639",
"0.6477725",
"0.636037",
"0.63598573",
"0.628827",
"0.62297404",
"0.6228401",
"0.62251353",
"0.6199615",
"0.6101772",
"0.6099161",
"0.60946184",
"0.6057053",
"0.6055482",
"0.60491145",
"0.60060436",
"0.5973747",
"0.5915173",
"0.5907625",
"0.5891119",
"0.5857807",... | 0.6502552 | 1 |
Save this measurement to the database. | def save (self) :
try:
# This opens and closes the connections to the database.
# Since writings occur only every 10 minutes it wouldn't be efficient to
# let the connection open.
db = MySQLdb.connect(
host=config.db_host, db=config.db_name, user=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self):\n raise NotImplementedError",
"def save(self):\n raise NotImplementedError",
"def save(self):\n raise NotImplementedError",
"def save(self):\n\n pass",
"def save(self) -> None:\n pass",
"def save(self) -> None:\n pass",
"def save(self) -> None:\... | [
"0.70428246",
"0.70428246",
"0.70428246",
"0.69544524",
"0.6933446",
"0.6933446",
"0.6933446",
"0.6923404",
"0.6907648",
"0.6900205",
"0.6877056",
"0.68489957",
"0.6845812",
"0.6827726",
"0.6827726",
"0.6827726",
"0.6827726",
"0.6827726",
"0.6798161",
"0.6755691",
"0.674851",... | 0.73555917 | 0 |
Generate a forest to a rectangular area. | def generate_trees(self, dx, dy, width, height, freq):
forest_colors = get_forest_colors()
entities = []
for y in range(dy, height):
for x in range(dx, width):
if not self.tiles[x][y].occupied:
self.tiles[x][y].spawnable = False
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_forest(self, verbose):\n _antecessors = []\n for key, cluster in self.clusters.items():\n if cluster.leaf_cluster is True:\n _antecessors.append(cluster.antecessor)\n _antecessors = remdup_preserve_order(_antecessors)\n _antecessors = sorted(_antecessors, key=get_cluster_idx, ... | [
"0.6471019",
"0.6346258",
"0.6215654",
"0.5859186",
"0.5726233",
"0.5726233",
"0.5700902",
"0.57001555",
"0.555477",
"0.5551676",
"0.52313495",
"0.5187418",
"0.5162394",
"0.51584244",
"0.513954",
"0.5130539",
"0.5126125",
"0.5117172",
"0.5082368",
"0.50747824",
"0.50643945",
... | 0.49609655 | 33 |
Create a door entity in map. If no coordinates are given, pick a random (x, y) from room walls. If any of the room walls are against the map border, make sure that door cannot be placed there. | def create_door(self, room=None, status="open", tile=None, name="door", color=None, random=False, x=None, y=None):
walls = room.get_walls()
if random:
door_seed = randint(0, len(room.get_walls()) - 1)
while walls[door_seed][0] == 1 or walls[door_seed][0] == room.w - 1 or walls[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_room(room):\n global map\n for x in range(room.x1+1, room.x2):\n for y in range(room.y1+1, room.y2):\n map[x][y].blocked = False\n map[x][y].block_sight = False",
"def create(self):\n\t\t# Pick a random starting position not on the parameter\n\t\tx = random.randint(1... | [
"0.65702087",
"0.64936954",
"0.64772934",
"0.61564773",
"0.6066264",
"0.5991652",
"0.59913564",
"0.59820735",
"0.5963644",
"0.5950242",
"0.59451026",
"0.5929143",
"0.59172434",
"0.58972824",
"0.58554417",
"0.58528894",
"0.5851147",
"0.58323514",
"0.57961446",
"0.57167244",
"0... | 0.74560183 | 0 |
Check the availability of the given car to be rented in the provided time period car The availability of this car is verified start, end The time span. The car is available if it is not rented in this time span Return True if the car is available, False otherwise | def isCarAvailable(self, car, start, end):
rentals = self.filterRentals(None, car)
for rent in rentals:
if start > rent.end or end < rent.start:
continue
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_availability(car):\n plate_num = int(car.plate[-1]) # Get the last number of the plate\n date = car.date # Get the date \n weekday = (date.weekday() + 1)*2 # Get the number of the week day\n time = date.time() # Get the time \n restricted = [(weekday-1) , weekday % 10] # Create an interva... | [
"0.74762523",
"0.72714204",
"0.6433187",
"0.6268924",
"0.6050666",
"0.5963115",
"0.59383965",
"0.59198993",
"0.58526516",
"0.5742015",
"0.5701476",
"0.5678647",
"0.5672865",
"0.56615305",
"0.5639994",
"0.56220275",
"0.56133354",
"0.5568478",
"0.5555473",
"0.55534595",
"0.5498... | 0.7850646 | 0 |
Return a list of rentals performed by the provided client for the provided car client The client performing the rental. None means all clients cars The rented car. None means all cars | def filterRentals(self, client, car):
result = []
for rental in self.__repository.getAll():
if client != None and rental.client != client:
continue
if car != None and rental.car != car:
continue
result.append(rental)
return resu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clients_sorted_by_rentals(self):\n rentals = self.get_list()\n number_of_rented_movies = dict.fromkeys([rental.client for rental in rentals], 0)\n for rental in rentals:\n number_of_rented_movies[rental.client] += 1\n items = sorted(number_of_rented_movies.items(), key = ... | [
"0.60510564",
"0.57855946",
"0.57267207",
"0.5495068",
"0.5456945",
"0.5285654",
"0.52423316",
"0.52244574",
"0.5095199",
"0.5042458",
"0.5001045",
"0.48717806",
"0.48693237",
"0.48531705",
"0.4853079",
"0.4828688",
"0.48279813",
"0.48155066",
"0.47878057",
"0.47713622",
"0.4... | 0.7709649 | 0 |
Simple match and replace of string within request | def ip_match_replace(request, injectionstring):
newrequest = copy.deepcopy(request)
rawrequest = request.get_raw_request()
for r in INJECT_MATCH_REPLACE:
regex = re.compile(r)
if regex.search(rawrequest):
LOG.tamper("IP detected! Replaced with {}".format(injectionstring))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def FillForm(string_for_substitution, dictionary_of_vars):\n return_string = string_for_substitution\n for i in re.findall(\"//%%(.*)%%//\", string_for_substitution):\n return_string = re.sub(\"//%%\" + i + \"%%//\", dictionary_of_vars[i],\n return_string)\n return return_string",
... | [
"0.6405884",
"0.6129814",
"0.60701466",
"0.6006177",
"0.5986332",
"0.5944586",
"0.59011203",
"0.5892035",
"0.5869508",
"0.58229357",
"0.5802855",
"0.5774343",
"0.57707906",
"0.57680464",
"0.5750596",
"0.5736021",
"0.57268953",
"0.56956047",
"0.56938297",
"0.5669029",
"0.56484... | 0.6794092 | 0 |
Generates a list of new requests with modified headers | def inject_headers(request, injectionstring):
newrequest = copy.deepcopy(request)
print(injectionstring)
for header in INJECT_HEADERS:
headername, headervalue = header.split(":", 1)
if not request.get_header(headername):
LOG.debug("Added {} header to the request".format(hea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _GetNewRequests(self):\n new_requests = self._GetRequestsByState(self._REQUESTED)\n if new_requests:\n while self._MakeRequestId() == new_requests[-1]:\n pass\n for request_id in new_requests:\n self._TransitionRequest(request_id, self._REQUESTED, self._PENDING)\n return new_reques... | [
"0.6238918",
"0.58712333",
"0.57931423",
"0.5742827",
"0.57357794",
"0.57014704",
"0.56626743",
"0.5645553",
"0.5633721",
"0.5582525",
"0.5544495",
"0.55196536",
"0.55148923",
"0.54854023",
"0.54710114",
"0.54703796",
"0.545906",
"0.5444627",
"0.5426233",
"0.54260164",
"0.542... | 0.53847367 | 22 |
Generates a list of new requests with replaced/modified post parameters | def inject_post_param(request, injectionstring):
requests = []
return requests | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def modify_requests(rf):\n post_url = '/docs/modify/'\n request_succ = [{\n \"request\": gen_request(rf, post_url, {\n \"user_id\": 1,\n \"type\": \"fetch\",\n \"document_id\": 1,\n }),\n \"response\": {\n \"code\": 200,\n }\n },\... | [
"0.6704369",
"0.64402634",
"0.6109998",
"0.6028123",
"0.58909523",
"0.5875955",
"0.58750314",
"0.5556154",
"0.55441815",
"0.554336",
"0.5475512",
"0.54604536",
"0.5455437",
"0.5452279",
"0.54406506",
"0.54406506",
"0.53951675",
"0.538495",
"0.538451",
"0.53750795",
"0.5369305... | 0.64869267 | 1 |
Generates a list of new requests with replaced/modified POST values | def inject_get_param(request, injectionstring):
requests = []
return requests | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def modify_requests(rf):\n post_url = '/docs/modify/'\n request_succ = [{\n \"request\": gen_request(rf, post_url, {\n \"user_id\": 1,\n \"type\": \"fetch\",\n \"document_id\": 1,\n }),\n \"response\": {\n \"code\": 200,\n }\n },\... | [
"0.67716086",
"0.634673",
"0.6332899",
"0.6027528",
"0.57943386",
"0.57905877",
"0.5782188",
"0.56972075",
"0.56329113",
"0.56018573",
"0.5592356",
"0.5592356",
"0.55672026",
"0.5521018",
"0.5496054",
"0.54681826",
"0.5431428",
"0.5411524",
"0.5411177",
"0.53842956",
"0.53448... | 0.48825723 | 84 |
Generates a list of new pipelined requests | def inject_pipeline(request, injectionstring):
requests = []
return requests | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __http_requests_generator(request_template, parameters):\n for payload in itertools.product(*parameters):\n yield request_template.format(*payload), payload",
"def _GetNewRequests(self):\n new_requests = self._GetRequestsByState(self._REQUESTED)\n if new_requests:\n while self._MakeReque... | [
"0.6724289",
"0.6341322",
"0.6295706",
"0.61274356",
"0.61229724",
"0.6103675",
"0.60066134",
"0.59773403",
"0.5966576",
"0.59564644",
"0.5929572",
"0.59252894",
"0.589654",
"0.58816415",
"0.58682007",
"0.585584",
"0.57956004",
"0.5764536",
"0.5764282",
"0.5758561",
"0.575848... | 0.60393435 | 6 |
Blocks can be quite large, so we use this method to strip it down and only retain the bare essentials in memory, also converting hashes (ByteArray) into regular hexstrings | def to_dict(block):
uncles = [hash_of(u) for u in block['uncles']]
parentHash = hash_of(block['parentHash'])
return {
'number': block['number'],
'timestamp': block['timestamp'],
'hash': hash_of(block),
'difficulty': block['difficulty'],
'totalDifficulty': block['total... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess_hashes(tex):\n blocks = catlist()\n rx = hash_rx\n m = rx.search(tex)\n while m:\n if len(m.group(2)) > 40:\n tex2htm.warn(\"Possible runaway hash: {}\".format(text_sample(m.group(2))))\n raise(None)\n blocks.append(tex[:m.start()])\n blocks.app... | [
"0.65894026",
"0.5872917",
"0.5733087",
"0.570575",
"0.5691035",
"0.5496473",
"0.5469316",
"0.5465705",
"0.5374875",
"0.5371656",
"0.53713965",
"0.5356473",
"0.5353301",
"0.5333334",
"0.5304726",
"0.5301991",
"0.5291938",
"0.5291055",
"0.52286994",
"0.5220841",
"0.52200973",
... | 0.50917363 | 31 |
'type' for argparse check that filename exists | def existingFile(filename):
if not os.path.exists(filename):
raise argparse.ArgumentTypeError("{0} does not exist".format(filename))
return filename | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_is_file(_string: str) -> str:\n if os.path.isfile(_string):\n return _string\n else:\n raise argparse.ArgumentTypeError(\"{0} file does \"\n \"not exists.\".format(_string))",
"def is_file(filename):\n if not os.path.isfile(filename):\n ... | [
"0.7953582",
"0.7749537",
"0.7593216",
"0.75587696",
"0.7522878",
"0.7518145",
"0.74682087",
"0.7462185",
"0.7449789",
"0.73844075",
"0.73225045",
"0.7258668",
"0.7172093",
"0.707981",
"0.70450455",
"0.6986201",
"0.69795257",
"0.6910114",
"0.6910114",
"0.6910114",
"0.6910114"... | 0.7269128 | 11 |
Returns the dataset metadata. | def _info(self) -> tfds.core.DatasetInfo:
# TODO(a2o): Specifies the tfds.core.DatasetInfo object
return tfds.core.DatasetInfo(
builder=self,
description=_DESCRIPTION,
features=tfds.features.FeaturesDict({
# These are the features of your dataset like images, labels ...
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_dataset_metadata(fields=[]):\n return get_dict_from_db(key='metadata', fields=fields)",
"def metadata(self, ds_name):\n return self.runinfos.get(self._infer_fqn(ds_name)).metadata._metadata",
"def metadata(self):\n return {\n \"namespace\": self.namespace,\n \"sho... | [
"0.7847299",
"0.77888906",
"0.7739118",
"0.7640124",
"0.761992",
"0.76041394",
"0.7602396",
"0.7559567",
"0.7550998",
"0.75146365",
"0.7502366",
"0.7455677",
"0.7444215",
"0.7444215",
"0.7444215",
"0.7444215",
"0.7444215",
"0.7418739",
"0.7383594",
"0.73539376",
"0.7330649",
... | 0.6999377 | 44 |
Solves a quadratic program related to a given qubit operator. | def get_classical_solver_result(qubit_operator, offset):
qp = QuadraticProgram()
qp.from_ising(qubit_operator, offset)
exact = MinimumEigenOptimizer(NumPyMinimumEigensolver())
return exact.solve(qp) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def diop_quadratic(eq, param=symbols(\"t\", integer=True)):\n var, coeff, diop_type = classify_diop(eq, _dict=False)\n\n if diop_type == BinaryQuadratic.name:\n if param is not None:\n parameters = [param, Symbol(\"u\", integer=True)]\n else:\n parameters = None\n r... | [
"0.65440845",
"0.6320849",
"0.63020766",
"0.6229912",
"0.61942685",
"0.6190736",
"0.6186001",
"0.6118183",
"0.60612786",
"0.6060127",
"0.59786564",
"0.59761536",
"0.5858187",
"0.5843685",
"0.5826548",
"0.57481503",
"0.57365835",
"0.5698943",
"0.56609064",
"0.5646106",
"0.5635... | 0.65034807 | 1 |
Returns a binary vector encoding an optimal solution to an optimization problem. | def get_exact_classical_binary_solution(qubit_operator, offset):
result = get_classical_solver_result(qubit_operator, offset)
return result.x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def optimization_vector(self) -> np.ndarray:\n filtered_iterator = compress(self.vector, self.optimization_mask)\n optimization_vector = np.array(list(filtered_iterator))\n return optimization_vector",
"def solve(self):\n self.m.optimize()\n if self.m.status == GRB.OPTIMAL:\n ... | [
"0.6893173",
"0.665314",
"0.6557473",
"0.647703",
"0.6345918",
"0.633206",
"0.63156945",
"0.628036",
"0.6168579",
"0.61620003",
"0.61477494",
"0.6007469",
"0.60018325",
"0.59688574",
"0.5909438",
"0.58359194",
"0.577035",
"0.5728536",
"0.5710103",
"0.56864226",
"0.5670657",
... | 0.5557718 | 30 |
Returns a value of an optimal solution to an optimization problem. | def get_exact_classical_fval_solution(qubit_operator, offset):
result = get_classical_solver_result(qubit_operator, offset)
return result.fval | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve(self):\n self.m.optimize()\n if self.m.status == GRB.OPTIMAL:\n self.solution = self.sol_as_mat()\n return self.solution",
"def objective(self) -> Optional[Union[int, float]]:\n if self.solution is not None:\n if isinstance(self.solution, list):\n ... | [
"0.71841055",
"0.69984436",
"0.6975628",
"0.6838121",
"0.6757776",
"0.6716559",
"0.6687618",
"0.66042393",
"0.657063",
"0.65611017",
"0.6551528",
"0.6525855",
"0.65247995",
"0.65053076",
"0.64765835",
"0.64513785",
"0.6442589",
"0.6405521",
"0.63852507",
"0.6353897",
"0.63113... | 0.0 | -1 |
Create a new type of bow. | def main(bow: api_models.Bow):
found_matching = list(
BowModel.scan((BowModel.type == bow.type) & (BowModel.size == bow.size))
)
if found_matching:
return bad_request(f"{bow.size} {bow.type} bows already exist")
new_bow = BowModel(**bow.dict())
new_bow.save()
new_bow.refresh()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_bb_object(name, data):\n global BLACKBOARD, TRACE_LEVEL\n bb_obj = BB_object(name, data)\n if TRACE_LEVEL > 2:\n print \"\\tCreating {0} object: {1}\".format( type(data), bb_obj )\n BLACKBOARD[name] = bb_obj\n signal_creation_event(bb_obj)\n return bb_obj",
"def create_b_obj(ob_... | [
"0.60078704",
"0.5955134",
"0.5922723",
"0.58871335",
"0.5825036",
"0.5780621",
"0.56854033",
"0.56650245",
"0.56517565",
"0.5644541",
"0.5636475",
"0.56316453",
"0.55873847",
"0.5506105",
"0.54752946",
"0.5444",
"0.54189473",
"0.54167676",
"0.54038286",
"0.53963405",
"0.5381... | 0.5628096 | 12 |
Load configuration from the command line. | def load_configuration():
argparser = ArgumentParser(description="Zoe application tester - Container Analytics as a Service core component",
default_config_files=CONFIG_PATHS,
auto_env_var_prefix="ZOE_MASTER_",
args_for_set... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli_load_config(self, args) -> str:\n path = args.config_path\n if not os.path.isfile(path):\n return error(\"Path {} DNE\".format(path))\n\n try:\n self.config = config.from_file(path)\n return ok(\"Configuration loaded from {}\".format(path))\n exc... | [
"0.7120539",
"0.68881625",
"0.681006",
"0.6799535",
"0.6736724",
"0.66192234",
"0.6616781",
"0.65878206",
"0.65739673",
"0.6562336",
"0.65211356",
"0.6520706",
"0.65050936",
"0.63761",
"0.6373241",
"0.6360475",
"0.6336217",
"0.628978",
"0.6279129",
"0.6274624",
"0.62703437",
... | 0.63583165 | 16 |
The main entrypoint function. | def main():
conf = load_configuration()
config.load_configuration(conf)
args = config.get_conf()
if args.debug:
logging.basicConfig(level=logging.DEBUG, format=LOG_FORMAT)
else:
logging.basicConfig(level=logging.INFO, format=LOG_FORMAT)
logging.getLogger('kazoo').setLevel(loggin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n return",
"def main():\n pass",
"def entry_point():",
"def entry_point():",
"def entry_point():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",... | [
"0.8444882",
"0.82910585",
"0.82036114",
"0.82036114",
"0.82036114",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",
"0.8200299",... | 0.0 | -1 |
Inicializamos la escena Creando los objetos pertinentes a partir de un archivo xml. Este archivo Contiene los datos de inicio del mapa, objetos, y personajes de la presente escena | def __init__(self, xdoc):
self.xdoc = parse(xdoc)
self.mapa = mapa.Mapa(self.xdoc)
self.scrollx = 0
self.scrolly = 0
self.eventos = self.load_events() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, xml_file):\n try:\n # Load xml file\n fd = resource_stream(__name__, 'map/{}'.format(xml_file))\n self.spec = parse(fd)\n self.remove_whitespace_nodes(self.spec, True)\n fd.close()\n\n except OSError:\n raise EDIFile... | [
"0.60758257",
"0.5842823",
"0.5804946",
"0.57069564",
"0.56295884",
"0.5622552",
"0.5592632",
"0.55789083",
"0.55294085",
"0.5513903",
"0.54790515",
"0.5420149",
"0.5392184",
"0.5384201",
"0.53596526",
"0.5341668",
"0.53402734",
"0.5339765",
"0.53388315",
"0.53207237",
"0.531... | 0.5259194 | 24 |
Movemos el scroll, esto simula el efecto "Camara" | def mover_scroll(self, x, y):
self.scrollx += x
self.scrolly += y | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scroll(*args):",
"def _on_scroll(self, event):",
"def start_scroll():\n send_command(0x2F)",
"def __window_scroll(self, x, y):\n pass",
"def flash(self, **kwargs):\n self.scroll(**kwargs) # Temporary, will be replaced",
"def scroll(self):\n x_position = self._player.get_posit... | [
"0.7052662",
"0.67604154",
"0.6638475",
"0.64753455",
"0.6438798",
"0.6330283",
"0.62574166",
"0.61876434",
"0.6171355",
"0.6170008",
"0.60271",
"0.5980288",
"0.5942342",
"0.5936811",
"0.59058535",
"0.58835864",
"0.58601457",
"0.58331525",
"0.5827912",
"0.58076257",
"0.579227... | 0.6232758 | 7 |
Actualiza el mapa y los personajes | def update(self, wd):
self.eventos[0].update(wd)
self.eventos[1].update(wd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self):\n\n # Ordenamos los personajes por fila, para luego dibujarlos correctamente. Para que no se solapen.\n self._personajes.sort(self._comparar_coordenadas_personajes)\n\n for personaje in self._personajes:\n if (personaje.andando): # Si está andando.\n ... | [
"0.7645867",
"0.60846424",
"0.6079712",
"0.591618",
"0.5849605",
"0.58402264",
"0.5831927",
"0.58309704",
"0.57731956",
"0.5655802",
"0.5642587",
"0.56421083",
"0.5594711",
"0.55926394",
"0.55504715",
"0.5531594",
"0.5530021",
"0.5497458",
"0.54852766",
"0.5479607",
"0.546547... | 0.0 | -1 |
ordering of health networks should be by priority, name | def test_health_networks_ordering(self):
state = State.objects.get(name="NC")
hns = HealthNetwork.objects.filter(state=state)
hns_p = {n: [hn for hn in hns if hn.priority == n] for n in PRIORITY.keys()}
self.assertEqual(
hns_p[0], hns[: len(hns_p[0])], msg="priority 0 health ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def priority(name):\n try:\n manager = Actions()\n priority = Priority[name]\n ordered_tasks = manager.order_by_priority(priority)\n click.echo(\"Ordered by priority:\" + click.style(name, bg='red', fg='white'))\n click.echo()\n console_utils.format_print_ordered(ordere... | [
"0.5870013",
"0.58105636",
"0.57861495",
"0.5686132",
"0.56482583",
"0.56482583",
"0.5623373",
"0.53816515",
"0.53049576",
"0.5292775",
"0.5250956",
"0.52294445",
"0.52216697",
"0.515409",
"0.5107319",
"0.51055664",
"0.50733155",
"0.506472",
"0.50563586",
"0.5054701",
"0.5054... | 0.795008 | 0 |
creating a network with the same name in a different state is fine, but creating a network with the same name in the same state raises an IntegrityError | def test_unique_health_networks_per_state(self):
state1 = State.objects.get(name="NC")
state2 = State.objects.get(name="SC")
kwargs = dict(priority=0, name="My Non-existent Health Network")
HealthNetwork.objects.create(state=state1, **kwargs)
HealthNetwork.objects.create(state=st... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_and_set_network(self) -> None:\n from hathor.transaction.storage.exceptions import WrongNetworkError\n\n network = settings.NETWORK_NAME\n stored_network = self.get_network()\n\n if stored_network is None:\n # no network is set, let's try to infer it\n s... | [
"0.62125087",
"0.61994624",
"0.6185668",
"0.61371464",
"0.6014497",
"0.59862936",
"0.5978605",
"0.5846337",
"0.5810719",
"0.5770879",
"0.5749314",
"0.57286835",
"0.57163084",
"0.56837964",
"0.5681908",
"0.5677037",
"0.5669655",
"0.5665639",
"0.5654778",
"0.5654763",
"0.564764... | 0.6288055 | 0 |
adding a network with an unsupported priority raises an error | def test_add_wrong_priority(self):
state = State.objects.get(name="NC")
bad_priority = len(list(PRIORITY.keys())) # 1 higher than the available priorities.
kwargs = dict(state=state, priority=bad_priority, name="My Non-existent Health Network")
self.assertRaises(ValidationError, Health... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_add_network(self):\n pass",
"def add_network(self, router, network, distance):\n self.routers[router].add_network(network, distance)",
"def add_network(self, network, distance):\n self.networks[network] = distance",
"def add_nei(self, dest: int, weight: float):\r\n if not... | [
"0.6310278",
"0.5923847",
"0.5907903",
"0.56655437",
"0.56389827",
"0.55968845",
"0.55506074",
"0.54959756",
"0.5477577",
"0.5470298",
"0.54657966",
"0.5449305",
"0.54388195",
"0.5422788",
"0.5414837",
"0.54006034",
"0.5377785",
"0.5374897",
"0.5343064",
"0.5321249",
"0.53177... | 0.61365 | 1 |
Postprocess a symbol table after collecting from one source. If the symbol table contains FILE symbols, they will be removed and replaced by a 'file' column on other symbols. If the symbol table contains ARM mode symbols, they will be removed and replaced by an 'arm' column on other symbols. | def postprocess_symbols(config: Config, symbols: SymbolDF) -> SymbolDF:
files = []
arms = []
arm_symbols = {}
current_file = ''
current_arm = ''
has_file = False
if config['collect.prefix-file']:
prefixes = config.get_re('collect.prefix')
else:
prefixes = None
if 'typ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def postprocess_file(config: Config, dfs: DFs) -> None:\n if SymbolDF.name in dfs:\n dfs[SymbolDF.name] = postprocess_symbols(config, dfs[SymbolDF.name])",
"def parse_symbol_table(data, sections, elf_header):\n if is64bit(elf_header):\n symbol_entry_str = symbol_64_entry_str\n symbol_e... | [
"0.6027799",
"0.57213014",
"0.5678896",
"0.53370655",
"0.53370655",
"0.5240379",
"0.510453",
"0.5102045",
"0.50985175",
"0.50433695",
"0.49907583",
"0.4950016",
"0.49235138",
"0.4910098",
"0.49089348",
"0.48771235",
"0.48708987",
"0.4854355",
"0.48283255",
"0.48272",
"0.47998... | 0.72918737 | 0 |
Postprocess tables after collecting from one source. | def postprocess_file(config: Config, dfs: DFs) -> None:
if SymbolDF.name in dfs:
dfs[SymbolDF.name] = postprocess_symbols(config, dfs[SymbolDF.name]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _postprocess_arena(self):\n # Create tables\n for i, (offset, rot, half_size, friction, legs) in enumerate(\n zip(self.table_offsets, self.table_rots, self.table_half_sizes, self.table_frictions, self.has_legs)\n ):\n self._add_table(\n name=f\"table{i}... | [
"0.61825305",
"0.60425353",
"0.59419334",
"0.58763474",
"0.58517617",
"0.5847505",
"0.5841374",
"0.5800364",
"0.578673",
"0.5686195",
"0.56758976",
"0.56543654",
"0.56543654",
"0.56543654",
"0.56543654",
"0.5629716",
"0.56151396",
"0.5608233",
"0.5608233",
"0.5608233",
"0.560... | 0.0 | -1 |
Account for space not used by any symbol, or by multiple symbols. | def fill_holes(config: Config, symbols: SymbolDF, sections: SectionDF) -> DFs:
# These symbols mark the start or end of unused space.
start_unused = frozenset(config.get('symbol.free.start', []))
end_unused = frozenset(config.get('symbol.free.end', []))
extent_columns = ['address', 'size', 'section', ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_space(self):\n return self.pos == self.SPACE_POS",
"def take_symbol(self, symbol):\r\n if symbol not in self._taken_symbols:\r\n for cell in self.cells:\r\n cell.remove_possible_symbol(symbol)\r\n self._taken_symbols.add(symbol)",
"def check_spacers(\n ... | [
"0.6041976",
"0.5827814",
"0.5804597",
"0.57901514",
"0.57184106",
"0.568177",
"0.56775665",
"0.566192",
"0.565856",
"0.56135005",
"0.5584242",
"0.55753756",
"0.5569027",
"0.5534547",
"0.55102926",
"0.54688996",
"0.54683703",
"0.54683703",
"0.54659444",
"0.5464312",
"0.546431... | 0.0 | -1 |
Add a row for a unaccounted gap or unused space. | def fill_gap(previous, current, from_address,
to_address) -> Tuple[str, List]:
size = to_address - from_address
if (previous is None or previous.symbol in start_unused
or current.symbol in end_unused):
use = 'unused'
name = memdf.name.unused(from_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_row(self, index):\n if index is None:\n index = self.size\n\n if index < self.size:\n raise ValueError(f\"Duplicate row index: {index}\")\n\n for empty in range(self.size, index):\n self._add_row(empty)\n\n self._data.append([None] * len(self._c... | [
"0.6710395",
"0.6708118",
"0.66811764",
"0.66745555",
"0.6542515",
"0.64227355",
"0.63794637",
"0.6323013",
"0.62881213",
"0.62115365",
"0.6197191",
"0.6190543",
"0.6182664",
"0.61804724",
"0.61694866",
"0.61547035",
"0.6145907",
"0.6137686",
"0.60447454",
"0.6037223",
"0.602... | 0.0 | -1 |
Add a row for overlap. | def fill_overlap(previous, current, from_address,
to_address) -> Tuple[str, List]:
size = to_address - from_address
return ('overlap',
filler(memdf.name.overlap(from_address, -size), from_address,
size, previous, current)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _merge_row(self, row1, row2):\n\n duprow = list(row1)\n duprow.extend(list(row2))\n row1.clear()\n overlap_map = {}\n\n for body, overlap in duprow:\n if body not in overlap_map:\n overlap_map[body] = 0\n overlap_map[body] += overlap\n\n ... | [
"0.75222784",
"0.59805727",
"0.5978246",
"0.5941416",
"0.5901618",
"0.58065987",
"0.5796185",
"0.5759986",
"0.5754992",
"0.5733107",
"0.572763",
"0.57233435",
"0.5666602",
"0.5605347",
"0.5599895",
"0.55902666",
"0.5554072",
"0.55292296",
"0.5527764",
"0.5526137",
"0.54562134... | 0.4893149 | 84 |
Postprocess tables after reading all sources. | def postprocess_collected(config: Config, dfs: DFs) -> None:
# Prune tables according to configuration options. This happens before
# fill_holes() so that space of any pruned symbols will be accounted for,
# and to avoid unnecessary work for pruned sections.
for c in [SymbolDF, SectionDF]:
if c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _postprocess_arena(self):\n # Create tables\n for i, (offset, rot, half_size, friction, legs) in enumerate(\n zip(self.table_offsets, self.table_rots, self.table_half_sizes, self.table_frictions, self.has_legs)\n ):\n self._add_table(\n name=f\"table{i}... | [
"0.64844763",
"0.62486094",
"0.62363154",
"0.6221706",
"0.6031505",
"0.60259247",
"0.6016668",
"0.6016668",
"0.6016668",
"0.6016668",
"0.59789",
"0.59558296",
"0.59290904",
"0.5916375",
"0.5908061",
"0.5895049",
"0.5893003",
"0.5863377",
"0.584784",
"0.583412",
"0.58197707",
... | 0.5442561 | 45 |
Read a filtered memory map from a set of files. | def collect_files(config: Config,
files: Optional[List[str]] = None,
method: Optional[str] = None) -> DFs:
filenames = files if files else config.get('args.inputs', [])
if method is None:
method = config.get('collect.method', 'csv')
frames: Dict[str, List[DF]] = {... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_mapfiles():\n mappings = []\n\n # matches stuff like\n # \"/GLOW/*\" glow\n # \"/cms/Role=pilot/Capability=NULL\" cmspilot\n # and extracts the stuff between the quotes, and the username in the second field\n regex = re.compile(r'^\\s*[\"](/[^\"]+)[\"]\\s+([A-Za-z0-9_]+)\\s*(?:$|[#])... | [
"0.6457381",
"0.607028",
"0.60247785",
"0.5874819",
"0.5851086",
"0.5847088",
"0.570969",
"0.5702015",
"0.5685579",
"0.56646115",
"0.5615684",
"0.55467254",
"0.55415946",
"0.5541046",
"0.5515222",
"0.5513438",
"0.54860705",
"0.5483462",
"0.5472742",
"0.54673123",
"0.5456363",... | 0.0 | -1 |
Common argument parsing for collection tools. | def parse_args(config_desc: Mapping, argv: Sequence[str]) -> Config:
config = Config().init({
**memdf.util.config.CONFIG,
**CONFIG,
**config_desc
})
config.argparse.add_argument('inputs', metavar='FILE', nargs='+')
return config.parse(argv) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_arguments(args):",
"def Args(parser):",
"def __parse_args(self):\n for argument in self.args:\n source_arg = re.match(\"^(--source=(([A-Z]|[a-z]|/|_|[0-9]|.)+))$\", argument)\n input_arg = re.match(\"^(--input=(([A-Z]|[a-z]|/|_|[0-9]|.)+))$\", argument)\n stats... | [
"0.7560228",
"0.73836064",
"0.71728504",
"0.7085921",
"0.6995286",
"0.69865096",
"0.68940294",
"0.68828773",
"0.6829608",
"0.6814625",
"0.68021065",
"0.6786284",
"0.6759703",
"0.67539513",
"0.67370564",
"0.6736484",
"0.67044693",
"0.6697017",
"0.6683502",
"0.6677242",
"0.6626... | 0.0 | -1 |
Make a test database to serve information to the rrlyrae test | def makeRRlyTable(size=100, database=VARIABILITY_DB, **kwargs):
# a haphazard sample of stellar SEDs
sedFiles = ['kp10_8750.fits_g35_8950', 'kp03_10500.fits_g45_10600', 'km50_6750.fits_g20_6750']
# a haphazard sample of RRLyrae light curves
lcFiles = ['rrly_lc/RRc/959802_per.txt', 'rrly_lc/RRc/1078860... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n\n # Get the Flask test client.\n self.client = app.test_client()\n app.config[\"TESTING\"] = True\n app.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\n # Connect to the test database.\n connect_to_db(app, db_uri=\"postgresql:///testnourish\") \n\n... | [
"0.72048223",
"0.7181469",
"0.71430147",
"0.71430147",
"0.71430147",
"0.7121495",
"0.71103686",
"0.7101406",
"0.70937717",
"0.7093083",
"0.70615804",
"0.7047677",
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"0.69542927",
"0.69425315",
"0.6937596",
"0.6917083",
"0.69113886",
"0.6874605",
"0.686... | 0.0 | -1 |
Make a test database to serve information to the cepheid test | def makeCepheidTable(size=100, database=VARIABILITY_DB, **kwargs):
# a haphazard sample of stellar SEDs
sedFiles = ['kp10_8750.fits_g35_8950', 'kp03_10500.fits_g45_10600', 'km50_6750.fits_g20_6750']
# a haphazard sample of cepheid light curves
lcFiles = ['cepheid_lc/classical_longPer_specfile', 'cephe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n\n # Get the Flask test client.\n self.client = app.test_client()\n app.config[\"TESTING\"] = True\n app.config[\"SECRET_KEY\"] = \"key\"\n app.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\n with self.client as c:\n with c.session_trans... | [
"0.74151266",
"0.7258446",
"0.721034",
"0.71372515",
"0.71106255",
"0.7081062",
"0.70735544",
"0.70537037",
"0.70427746",
"0.7021754",
"0.7007703",
"0.7007703",
"0.7007703",
"0.69921845",
"0.6984731",
"0.6974774",
"0.6953756",
"0.69427204",
"0.6927968",
"0.6910455",
"0.689305... | 0.0 | -1 |
Make a test database to serve information to the Eb test | def makeEbTable(size=100, database=VARIABILITY_DB, **kwargs):
# a haphazard sample of eclipsing binary light curves
lcFiles = ['eb_lc/EB.2294.inp', 'eb_lc/EB.1540.inp', 'eb_lc/EB.2801.inp']
conn = sqlite3.connect(database)
c = conn.cursor()
try:
c.execute('''CREATE TABLE eb
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n\n # Get the Flask test client.\n self.client = app.test_client()\n app.config[\"TESTING\"] = True\n app.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\n # Connect to the test database.\n connect_to_db(app, db_uri=\"postgresql:///testnourish\") \n\n... | [
"0.7369731",
"0.7358841",
"0.73523945",
"0.73413175",
"0.73236394",
"0.73226213",
"0.72732764",
"0.72732764",
"0.72732764",
"0.7254238",
"0.72534025",
"0.7238794",
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"0.7181675",
"0.71753734",
"0.71528864",
"0.7105111",
"0.70832896",
"0.7079517",
"0.... | 0.0 | -1 |
Make a test database to serve information to the microlensing test | def makeMicrolensingTable(size=100, database=VARIABILITY_DB, **kwargs):
# a haphazard sample of stellar SEDs
sedFiles = ['kp10_8750.fits_g35_8950', 'kp03_10500.fits_g45_10600', 'km50_6750.fits_g20_6750']
# there are two microlensing methods; they should be equivalent
method = ['applyMicrolensing', 'ap... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n\n # Get the Flask test client.\n self.client = app.test_client()\n app.config[\"TESTING\"] = True\n app.config[\"SECRET_KEY\"] = \"key\"\n app.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\n with self.client as c:\n with c.session_trans... | [
"0.6949616",
"0.6874204",
"0.67825353",
"0.67642397",
"0.6715823",
"0.6712028",
"0.669866",
"0.66828644",
"0.66631645",
"0.6657591",
"0.66463125",
"0.66290164",
"0.66002905",
"0.6599407",
"0.6549921",
"0.6536971",
"0.6536971",
"0.6536971",
"0.6516672",
"0.65152085",
"0.651215... | 0.0 | -1 |
Make a test database to serve information to the BHmicrolensing test | def makeBHMicrolensingTable(size=100, database=VARIABILITY_DB, **kwargs):
# a haphazard sample of stellar SEDs
sedFiles = ['kp10_8750.fits_g35_8950', 'kp03_10500.fits_g45_10600', 'km50_6750.fits_g20_6750']
# a sample of black hole microlensing light curves that do not repeat time steps
# (repeating ti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dummydb_basic(self):\n db = DummyDB()",
"def setUp(self):\n\n # Get the Flask test client.\n self.client = app.test_client()\n app.config[\"TESTING\"] = True\n app.config[\"SECRET_KEY\"] = \"key\"\n app.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\n ... | [
"0.7080517",
"0.69557416",
"0.6924083",
"0.6852441",
"0.6830986",
"0.6815932",
"0.6808822",
"0.6800836",
"0.6785046",
"0.67655325",
"0.67539275",
"0.6749509",
"0.6723518",
"0.6722012",
"0.66994596",
"0.6666832",
"0.6664048",
"0.6664048",
"0.6664048",
"0.66402483",
"0.66355497... | 0.0 | -1 |
Make a test database to serve information to the AMCVN test | def makeAmcvnTable(size=100, database=VARIABILITY_DB, **kwargs):
# a haphazard sample of white dwarf SEDs
sedFiles = ['bergeron_He_4750_70.dat_4950', 'bergeron_50000_85.dat_54000']
conn = sqlite3.connect(database)
c = conn.cursor()
try:
c.execute('''CREATE TABLE amcvn
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def test_db():\n test_uta_db = UTADatabase()\n await test_uta_db._create_genomic_table()\n return test_uta_db",
"def create_test_db(self, *args, **kw):\n self.destroy_test_db()\n self.connection.use_test_datastore = True\n self.connection.flush()",
"def setUp(self):\n\n # Get the... | [
"0.70171046",
"0.6997887",
"0.6953645",
"0.69216573",
"0.690113",
"0.68899894",
"0.6856438",
"0.6852487",
"0.68396735",
"0.68202394",
"0.68202394",
"0.68202394",
"0.68062794",
"0.67793113",
"0.67728156",
"0.6721817",
"0.6707253",
"0.6691685",
"0.66833436",
"0.66819924",
"0.66... | 0.0 | -1 |
Make a test database to serve information to the microlensing test | def makeAgnTable(size=100, database=VARIABILITY_DB, **kwargs):
# a haphazard sample of galaxy SEDs
sedFiles = ['Exp.31E06.0005Z.spec', 'Inst.79E06.1Z.spec', 'Const.50E07.0005Z.spec']
conn = sqlite3.connect(database)
c = conn.cursor()
try:
c.execute('''CREATE TABLE agn
(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n\n # Get the Flask test client.\n self.client = app.test_client()\n app.config[\"TESTING\"] = True\n app.config[\"SECRET_KEY\"] = \"key\"\n app.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\n with self.client as c:\n with c.session_trans... | [
"0.6950125",
"0.6874638",
"0.6783422",
"0.6764957",
"0.67164266",
"0.67127717",
"0.66987395",
"0.6683703",
"0.66637313",
"0.665844",
"0.6646214",
"0.6629767",
"0.6601191",
"0.65999836",
"0.65497684",
"0.65371394",
"0.65371394",
"0.65371394",
"0.6516805",
"0.65154845",
"0.6512... | 0.0 | -1 |
Make a test database that contains a mix of Cepheid variables and 'testVar' variables (variables that use the applySineVar method defined in the TestVariabilityMixin) | def makeHybridTable(size=100, database='VariabilityTestDatabase.db', **kwargs):
# a haphazard sample of stellar SEDs
sedFiles = ['kp10_8750.fits_g35_8950', 'kp03_10500.fits_g45_10600', 'km50_6750.fits_g20_6750']
# a haphazard sample of cepheid light curves
lcFiles = ['cepheid_lc/classical_longPer_spec... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_variational():\n # iris\n #pres = \"Test pour le data set Iris (facile, classique)\"\n #test_from_func_variational(pres, 15, 10, 3, True, Iris)\n\n # breast cancer\n pres = \"Test pour le data set Breast Cancer (facile, classique)\"\n test_from_func_variational(pres, 15, 10, 3, True, Bre... | [
"0.6573082",
"0.58943033",
"0.57813627",
"0.5771824",
"0.57339275",
"0.57289785",
"0.5722299",
"0.5717864",
"0.55727744",
"0.5547445",
"0.5542909",
"0.55299455",
"0.5520713",
"0.5497947",
"0.54763407",
"0.5471372",
"0.5468235",
"0.545974",
"0.5452203",
"0.54360193",
"0.543247... | 0.5262734 | 37 |
Verify that a catalog generated by the unit tests below contains the rows it ought to. This is done by looking for a corresponding catalog in tests/testData and verifying that the two catalogs have identical rows. | def verify_catalogs(self, cat_name):
control_dir = os.path.join(getPackageDir('sims_catUtils'),
'tests', 'testData')
_, control_name = os.path.split(cat_name)
control_name = os.path.join(control_dir, control_name)
with open(control_name, 'r') as contro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_see_all_different_products(self):\n\n # create new unittest data\n ImportUnitTestData.import_data_handler()\n # create new data\n try:\n current_dir = os.getcwd()\n directory_name = current_dir + '\\\\lesson5\\\\data\\\\'\n file_name_dict = {'pr... | [
"0.64208776",
"0.61701244",
"0.6072419",
"0.60301155",
"0.6008713",
"0.6000412",
"0.59985",
"0.5942207",
"0.59310716",
"0.5895737",
"0.5893529",
"0.5886061",
"0.58684117",
"0.5866197",
"0.5834321",
"0.5827727",
"0.5790548",
"0.5764453",
"0.5762863",
"0.575223",
"0.5746735",
... | 0.6838003 | 0 |
Test that we can generate a catalog which inherits from multiple variability mixins (in this case, TestVariability and VariabilityStars). This is to make sure that the register_method and register_class decorators do not mangle inheritance of methods from mixins. | def testHybridVariability(self):
cat_name = os.path.join(self.scratch_dir, 'hybridTestCatalog.dat')
makeHybridTable(database=self.variability_db)
myDB = CatalogDBObject.from_objid('hybridTest', database=self.variability_db)
myCatalog = StellarVariabilityCatalogWithTest(myDB, obs_metadata... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ProcessMixins(self, mixins):\n self._mixins = mixins",
"def test():\n # the component superclass\n from p2.components.Component import Component\n # the trait factory\n from p2.traits.Measure import Measure as measure\n\n\n # subclass\n class Base(Component):\n \"\"\"\n A s... | [
"0.57527846",
"0.57445997",
"0.5671585",
"0.5648246",
"0.551333",
"0.5491303",
"0.54901725",
"0.5403399",
"0.5254813",
"0.5250898",
"0.5245918",
"0.5179942",
"0.5150097",
"0.5106484",
"0.5104359",
"0.5093766",
"0.5088895",
"0.5082123",
"0.5071844",
"0.5065457",
"0.5057772",
... | 0.63517714 | 0 |
Use the hybrid variability catalog class from testHybridVariability to verify that, if we pass an array of expmjd into applyVariability, we get the expected result out. | def testApplyVariabilityWithManyMJD(self):
makeHybridTable(database=self.variability_db)
hybrid_db = hybridDB(database=self.variability_db)
hybrid_cat = StellarVariabilityCatalogWithTest(hybrid_db, obs_metadata=self.obs_metadata,
column_out... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_var_exp(self):\n with self.test_context() as session:\n test_setups, F, feed = self.prepare()\n for test_setup in test_setups:\n l = test_setup.likelihood\n y = test_setup.Y\n l.compile()\n r1 = session.run(l.logp(F, ... | [
"0.62441033",
"0.5478328",
"0.54694235",
"0.53534585",
"0.5264933",
"0.5245997",
"0.5183478",
"0.5169352",
"0.5164767",
"0.5156014",
"0.50279623",
"0.5003747",
"0.49608055",
"0.49578118",
"0.49560577",
"0.4934729",
"0.49292457",
"0.487284",
"0.4847929",
"0.484281",
"0.4840532... | 0.74344283 | 0 |
Just verify that the catalog generation code runs in this case | def testAgn(self):
cat_name = os.path.join(self.scratch_dir, 'agnTestCatalog.dat')
makeAgnTable(database=self.variability_db)
myDB = CatalogDBObject.from_objid('agnTest', database=self.variability_db)
obs = ObservationMetaData(pointingRA=self.obs_metadata.pointingRA,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_hyperflex_app_catalog(self):\n pass",
"def final_check(self, test_collection):\n assert True",
"def test_update_hyperflex_app_catalog(self):\n pass",
"def test_get_catalogue(self):\n s1 = System()\n self.assertEqual(len(s1.get_catalogue()), 0)",
"def testC... | [
"0.6712688",
"0.6389315",
"0.6382587",
"0.6253937",
"0.62304395",
"0.61791164",
"0.6091699",
"0.6074117",
"0.6063963",
"0.60027957",
"0.5953458",
"0.58575106",
"0.5855261",
"0.5855261",
"0.582376",
"0.58095646",
"0.57964206",
"0.5795534",
"0.579538",
"0.5768308",
"0.57592714"... | 0.5705332 | 23 |
Export data in the JSON format | def search_and_export_json():
url = 'https://jsonplaceholder.typicode.com/'
users_dict = requests.get("{}users".format(url)).json()
tasks_dict = requests.get("{}todos".format(url)).json()
my_dict = {}
usernames_dict = {}
for info in users_dict:
user_id = info.get('id')
my_dic... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def export_data(self):\n\n # We need to remove spaces for TW-1504, use custom separators\n data_tuples = ((key, self._serialize(key, value))\n for key, value in six.iteritems(self._data))\n\n # Empty string denotes empty serialized value, we do not want\n # to pass... | [
"0.77344227",
"0.7713919",
"0.75653756",
"0.73680156",
"0.7116531",
"0.7103052",
"0.7090365",
"0.7061575",
"0.7035093",
"0.6930469",
"0.6928332",
"0.69176066",
"0.68976426",
"0.68957514",
"0.6857897",
"0.68488026",
"0.6827105",
"0.6797737",
"0.6782044",
"0.6780814",
"0.677170... | 0.0 | -1 |
Method to change to yellow, then red light | def run(self):
global moving_line
global change_requested
global thick_1_a, thick_1_b, thick_1_c, thick_2_a, thick_2_b, thick_2_c
global lap_to_go
global lights
line = moving_line
if line == 1:
thick_1_a = 1
lights[2].write(1)
t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def yellow(self, new_value):\r\n if self.empty is True and self.yellow is False and self.red is False and new_value is True:\r\n self._yellow = new_value\r\n self.empty = False\r\n else:\r\n raise DomainError('Square already full! ')",
"def set_color(self, new_color... | [
"0.7079132",
"0.7040418",
"0.70376426",
"0.69809306",
"0.6953118",
"0.69365674",
"0.68913335",
"0.68814415",
"0.68514913",
"0.6788533",
"0.67882794",
"0.677916",
"0.676236",
"0.6749317",
"0.6723421",
"0.67104745",
"0.6708115",
"0.67057747",
"0.66678286",
"0.6667042",
"0.66471... | 0.0 | -1 |
Return the input number if its odd, or return input number + 1 if its even or zero. | def make_odd(number):
if number == 0:
number += 1
if number % 2 == 0:
number += -1
return number | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __odd_or_even(number):\n if(number % 2) == 0:\n return 0\n else:\n return 1",
"def make_number_odd(num):\n if num % 2 == 0:\n num += 1\n return num",
"def even_numbers(number):\r\n\r\n if number%2 ==0:\r\n return number",
"def even_odd(number):\r\n if number ... | [
"0.8168115",
"0.7859249",
"0.719919",
"0.718165",
"0.71592253",
"0.7086521",
"0.7042811",
"0.7012443",
"0.687709",
"0.68506926",
"0.68103355",
"0.67186725",
"0.66446716",
"0.6642592",
"0.66274685",
"0.6626728",
"0.6619069",
"0.66058695",
"0.6584184",
"0.6577917",
"0.65761554"... | 0.7791604 | 2 |
Draw filled circles to simulate semaphore light. | def light_circles():
global frame_1, frame_2
x_circle_center = int(camera_width/11)
y_circle_center = int(camera_hight/9)
circle_radius = int(camera_width*.07)
cv2.circle(
frame_1, (x_circle_center, (circle_radius+(4*circle_radius))),
circle_radius, green_color, thickness=thick_1_a)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw(self):\r\n arcade.draw_circle_filled(self.center.x, self.center.y, self.radius, TARGET_COLOR)",
"def draw_circle_filled(self, x0, y0, r, color=None):\n self._draw_fast_vline(x0, y0 - r, 2 * r + 1, color)\n self._draw_circle_filled_helper(x0, y0, r, 3, 0, color)",
"def draw_circle_... | [
"0.7010427",
"0.698562",
"0.69655234",
"0.69196063",
"0.67997384",
"0.6717115",
"0.67054826",
"0.67054826",
"0.67018795",
"0.66960144",
"0.6645909",
"0.6633102",
"0.6598114",
"0.65929836",
"0.65786374",
"0.6557192",
"0.6549002",
"0.65449375",
"0.6515338",
"0.65085894",
"0.647... | 0.0 | -1 |
Draw road lines to define valid detection areas. | def road_lines():
cv2.polylines(frame_1, [pts_1], True, yellow_color)
cv2.polylines(frame_2, [pts_2], True, yellow_color) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drawLines(self):\n\t\tintersections = [[], []]\n\t\tfor l in self.lines:\n\t\t\tif l.direction == 'v':\n\t\t\t\tif l.rtc:\n\t\t\t\t\tposition = l.coordinate + int((self.width - 1) / 2)\n\t\t\t\telse:\n\t\t\t\t\tposition = int((l.coordinate * self.width / 100) if type(l.coordinate) == float else l.coordinate)\n... | [
"0.6935589",
"0.6575923",
"0.6425404",
"0.6305132",
"0.61926347",
"0.6187055",
"0.6155874",
"0.61299604",
"0.61294866",
"0.6076995",
"0.60432255",
"0.60113436",
"0.6008314",
"0.6002908",
"0.59739286",
"0.59024054",
"0.5898999",
"0.5898862",
"0.5894378",
"0.58699995",
"0.58633... | 0.7002146 | 0 |
Takes a MongoDB cursor and returns the number of documents it contains. | def _get_count(results):
return len(results) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def count(self, spec=None, cursor=None):\n if not cursor:\n cursor = self._cursor\n if not spec:\n spec = {}\n spec[DELETE_FLAG] = {\"$ne\": True}\n n = await cursor.count_documents(spec)\n return n, None",
"def cursor_size(cursor):\n count = 0\n ... | [
"0.76128787",
"0.7467919",
"0.6940241",
"0.6897004",
"0.68674344",
"0.6788739",
"0.6714874",
"0.6706902",
"0.6651172",
"0.66301596",
"0.6595173",
"0.64706737",
"0.64036924",
"0.6355133",
"0.6307477",
"0.6277702",
"0.6205081",
"0.61664677",
"0.61638486",
"0.6155807",
"0.608861... | 0.58574283 | 33 |
Takes a list of dictionaries and a list index. Returns the id for that index. | def _get_id(results, index):
return results[index]['_id'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_list_id(collection, index = \"id\", ignore_filter = False):\n\tif (not ignore_filter):\n\t\tcollection = [register[index] for register in list(collection)]\n\treturn \"'\" + (\"', '\".join(collection)) + \"'\"",
"def get_id(self, index):\n return self.__keys[index]",
"def get_indexed_item_from_l... | [
"0.6608865",
"0.6461903",
"0.6270167",
"0.6254719",
"0.62439704",
"0.62224084",
"0.60979915",
"0.5953362",
"0.5770354",
"0.5758881",
"0.57466406",
"0.57018924",
"0.56850445",
"0.5634907",
"0.5571361",
"0.55570155",
"0.55042535",
"0.54714113",
"0.5466338",
"0.5462254",
"0.5448... | 0.6934078 | 0 |
Takes a list of dictionaries and a list index. Returns the document for that index. | def _get_doc(results, index):
return results[index] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _index_document(index_list):\n if isinstance(index_list, abc.Mapping):\n raise TypeError(\"passing a dict to sort/create_index/hint is not \"\n \"allowed - use a list of tuples instead. did you \"\n \"mean %r?\" % list(index_list.items()))\n elif not i... | [
"0.7619862",
"0.6181496",
"0.6016992",
"0.59967166",
"0.59627914",
"0.5940448",
"0.59366274",
"0.5891136",
"0.5872575",
"0.5839089",
"0.58277404",
"0.5806781",
"0.5760332",
"0.5748127",
"0.5702271",
"0.5691555",
"0.5679571",
"0.56673163",
"0.5663057",
"0.5657615",
"0.5626698"... | 0.6746059 | 1 |
Tests the _create_text_index() method when a schema is provided. | def test_create_text_index_w_schema(self):
actual = self.engine._create_text_index(self.engine.schema)
expected = 'TextIndex'
self.assertEqual(actual, expected) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_text_index_noschema(self):\n actual = self.engine._create_text_index()\n expected = 'TextIndex'\n self.assertEqual(actual, expected)",
"def test_creating_index_type(self):",
"def test_string_index():\n\tlib.backup_and_restore(\n\t\tlambda context: create_indexes(lib.create_... | [
"0.8399966",
"0.68847084",
"0.6653272",
"0.65279675",
"0.6232708",
"0.61420846",
"0.6077263",
"0.5866493",
"0.57903063",
"0.57808346",
"0.5726722",
"0.57109994",
"0.5680334",
"0.5675512",
"0.56721723",
"0.56721723",
"0.5643351",
"0.5582189",
"0.5579941",
"0.55570096",
"0.5553... | 0.8753069 | 0 |
Tests the _create_text_index() method when a schema is not provided. | def test_create_text_index_noschema(self):
actual = self.engine._create_text_index()
expected = 'TextIndex'
self.assertEqual(actual, expected) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_text_index_w_schema(self):\n actual = self.engine._create_text_index(self.engine.schema)\n expected = 'TextIndex'\n self.assertEqual(actual, expected)",
"def test_creating_index_type(self):",
"def test_string_index():\n\tlib.backup_and_restore(\n\t\tlambda context: create_i... | [
"0.85630506",
"0.6538482",
"0.6379478",
"0.62192476",
"0.6107749",
"0.60360074",
"0.6031513",
"0.59643614",
"0.5895175",
"0.5890334",
"0.58874357",
"0.57891434",
"0.56733567",
"0.5590555",
"0.55498284",
"0.55498284",
"0.55243",
"0.5514081",
"0.5476263",
"0.5456776",
"0.544013... | 0.86921626 | 0 |
Tests the __str__ method. | def test_str(self):
name = str(self.engine)
self.assertTrue(name.startswith('test_database_'))
self.assertTrue(name.endswith('.test_docs')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_str_method(self):\n b1 = BaseModel()\n b1printed = b1.__str__()\n self.assertEqual(b1printed,\n \"[BaseModel] ({}) {}\".format(b1.id, b1.__dict__))",
"def test_str(self):\n self.assertEqual(str(self.bs), str(self.wbs))\n self.assertEqual(str(sel... | [
"0.82420087",
"0.8141123",
"0.81234753",
"0.8109608",
"0.8077134",
"0.80220187",
"0.800164",
"0.7937999",
"0.7932961",
"0.7748346",
"0.77479196",
"0.77436966",
"0.77424705",
"0.77423435",
"0.7715638",
"0.7699125",
"0.76134163",
"0.75954235",
"0.7555366",
"0.75448424",
"0.7537... | 0.0 | -1 |
Sets up an ElasticSearch index and inserts the test_docs provided by FilterTestCaseMixin. | def setUp(self):
super(MongoDbFilterTestCase, self).setUp()
for doc in self.test_docs:
self.engine.insert(doc) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n body = {\n \"settings\": {\n \"number_of_shards\": 1,\n \"number_of_replicas\": 0\n }\n }\n self.assertTrue(self.es.create_index('contacts_esclient_test', body))\n self.assertFalse(self.es.create_index('contacts_escl... | [
"0.770078",
"0.7126145",
"0.68322456",
"0.6620452",
"0.6540127",
"0.64694273",
"0.6463141",
"0.63948095",
"0.63571036",
"0.6345623",
"0.63434756",
"0.62436575",
"0.6241861",
"0.62167126",
"0.6173552",
"0.6168257",
"0.6160923",
"0.61488116",
"0.61431104",
"0.6114989",
"0.61126... | 0.6929581 | 2 |
Limit result list with "MAX_RESULT" | def limit_result(result_set):
max_result = MAX_RESULT
result = []
if max_result > 0:
result = result_set[:max_result]
result.append("Total result: {}".format(len(result_set)))
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_result_limit(self, data):\n self.add_payload('resultLimit', data)\n self._result_limit = self._uni(data)",
"def limit(self, limit):\n\n # Return between 1 and 250 results, defaults to 10\n return max(1, min(250, int(limit) if limit else 10))",
"def max_results(self, max_resu... | [
"0.673919",
"0.64389324",
"0.62612617",
"0.6195268",
"0.6166678",
"0.6131141",
"0.60837257",
"0.60630274",
"0.6061646",
"0.60587096",
"0.6015139",
"0.59791905",
"0.59546673",
"0.59319866",
"0.5929677",
"0.59154874",
"0.58818",
"0.58628124",
"0.5826882",
"0.58234286",
"0.58110... | 0.7924585 | 0 |
Get an Github Operator | def _github_operator(self, msg):
token = self[msg.frm.person + "github_token"]
result = GithubOperator(token)
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_github_credendial(cls) -> 'ApiCredential':\n return cls.select_token_for_api(GITHUB_API_NAME)",
"def get(self, endpoint, params=None):\n res = requests.get(\"https://api.github.com/\" + endpoint,\n auth=requests.auth.HTTPBasicAuth(self.credentials['username'], self.cre... | [
"0.60614455",
"0.594694",
"0.5812482",
"0.5780396",
"0.5726921",
"0.56730396",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
"0.5613916",
... | 0.73203856 | 0 |
Get an Translation Util | def _translation_util(self, msg):
token = self[msg.frm.person + "github_token"]
return TranslateUtil(REPOSITORY_CONFIG_FILE, token) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_translation(self):",
"def get_gettext():\n local_path = os.path.realpath(os.path.dirname(sys.argv[0])) + \\\n '/translations'\n langs = []\n lc, encoding = locale.getdefaultlocale()\n if (lc):\n langs = [lc]\n osLanguage = os.environ.get('LANGUAGE', None)\n if (os... | [
"0.692908",
"0.6249203",
"0.6228129",
"0.62209576",
"0.6138939",
"0.6135836",
"0.61316675",
"0.60002774",
"0.588955",
"0.5859885",
"0.57699263",
"0.57648146",
"0.57179856",
"0.5707253",
"0.57066154",
"0.569066",
"0.56857437",
"0.5651666",
"0.56276864",
"0.5602541",
"0.5530169... | 0.66220915 | 1 |
List all branches in current repository | def list_branches(self, msg, args):
trans = self._translation_util(msg)
yield "\n".join(trans.list_branches(REPOSITORY_NAME)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def branches(self) -> list[str]:\n _args: list[Arg] = []\n _ctx = self._select(\"branches\", _args)\n return _ctx.execute_sync(list[str])",
"def list_all_branches(self) -> dict:\n try:\n branches_response = self.repo.get_branches()\n branches_list = []\n ... | [
"0.7749532",
"0.7738944",
"0.7703959",
"0.7614574",
"0.7598028",
"0.7515656",
"0.75033426",
"0.7465316",
"0.740539",
"0.73894024",
"0.72446",
"0.7178419",
"0.7082727",
"0.7047357",
"0.70421684",
"0.6949491",
"0.6943486",
"0.69368696",
"0.6858128",
"0.6777088",
"0.6739843",
... | 0.7706611 | 2 |
Check if user had bound their github token. | def _github_bound(self, person):
result = True
try:
result = len(self[person + "github_token"]) > 0
except BaseException:
result = False
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_token(self):\n api_response = requests.get(self.api_config.get_api_url() + \"greetings/isloggedin\",\n headers={\"Authorization\": \"Bearer \" + self.API_TOKEN})\n\n if api_response.status_code == 401 or 403:\n return False\n else:\n ... | [
"0.65147024",
"0.6480705",
"0.63269347",
"0.6250972",
"0.62163246",
"0.6209365",
"0.6175841",
"0.6166921",
"0.6119698",
"0.6116468",
"0.6107161",
"0.6025452",
"0.6002667",
"0.5976334",
"0.59564173",
"0.59537226",
"0.59510547",
"0.59139603",
"0.5908455",
"0.5898309",
"0.582852... | 0.77639025 | 0 |
Find issues with the label "welcome" | def whatsnew(self, msg, args):
self._asset_bind(msg)
client = self._github_operator(msg)
cmd = "repo:{} label:welcome is:open type:issue".format(
task_repository_name())
issue_list = client.search_issue(cmd, 10)
result = limit_result(
["{}: {}".format(i.nu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_issue_get_label(self):\n pass",
"def test_issue_get_labels(self):\n pass",
"def test_issue_list_labels(self):\n pass",
"def test_issue_add_label(self):\n pass",
"def test_issue_replace_labels(self):\n pass",
"def test_issue_create_label(self):\n pass",
... | [
"0.62451565",
"0.6179279",
"0.59356964",
"0.5735456",
"0.5484781",
"0.54358155",
"0.5402358",
"0.5395643",
"0.53929466",
"0.5289219",
"0.528681",
"0.5269851",
"0.52506924",
"0.5225221",
"0.5182275",
"0.5177378",
"0.5172387",
"0.51599",
"0.5157391",
"0.5143768",
"0.5141218",
... | 0.0 | -1 |
Find issues with the label "welcome" then replace with "pending" label | def confirm_recent_new_issues(self, msg, hour):
self._asset_bind(msg)
yield ("Processing....")
trans = self._translation_util(msg)
client = self._github_operator(msg)
cmd = "repo:{} is:open type:issue".format(
task_repository_name())
issue_list = client.search... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_issue_replace_labels(self):\n pass",
"def change_issues_label(self, msg, old_label, new_label):\n self._asset_bind(msg)\n yield (\"Processing....\")\n trans = self._translation_util(msg)\n client = self._github_operator(msg)\n cmd = \"repo:{} label:{} is:open ty... | [
"0.7193442",
"0.67526186",
"0.63840276",
"0.6223574",
"0.62127376",
"0.5960943",
"0.5946542",
"0.58994776",
"0.58699584",
"0.58600855",
"0.5739906",
"0.57178646",
"0.56967795",
"0.56701887",
"0.56290764",
"0.5619469",
"0.5574466",
"0.55743533",
"0.554854",
"0.54189855",
"0.53... | 0.46344647 | 83 |
Bump the version label of issues from old_label to new_label | def change_issues_label(self, msg, old_label, new_label):
self._asset_bind(msg)
yield ("Processing....")
trans = self._translation_util(msg)
client = self._github_operator(msg)
cmd = "repo:{} label:{} is:open type:issue".format(
task_repository_name(), old_label)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_issue_replace_labels(self):\n pass",
"def post_issue_labels(repo, project, issue_number):\n issue_path = '%s/%s/issues/%d' % (repo, project, issue_number)\n\n new_label_ids = request.json\n\n current_label_ids = dao.get_issue_label_ids(issue_path)\n\n revised_label_ids = current_label... | [
"0.6792092",
"0.6410261",
"0.63336664",
"0.62997633",
"0.6212655",
"0.6188349",
"0.6131808",
"0.6078506",
"0.60697824",
"0.5982465",
"0.59715044",
"0.58801395",
"0.58639926",
"0.5816984",
"0.576189",
"0.57202476",
"0.57188237",
"0.5707912",
"0.570286",
"0.565688",
"0.5632423"... | 0.75369704 | 0 |
Create comment for an issue | def comment_issue(self, msg, issue_id, comment):
self._asset_bind(msg)
client = self._github_operator(msg)
comment_obj = client.issue_comment(task_repository_name(), issue_id, comment)
yield comment_obj.html_url | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_issue_create_comment(self):\n pass",
"def create_comment(self, body):\n return self.client.request(\n \"{}/issues/{}/comments\".format(self.repo.base_path, self.num),\n params={\"body\": body},\n method=\"POST\"\n )",
"def test_issue_get_comment(se... | [
"0.8301317",
"0.7754753",
"0.72062963",
"0.7073606",
"0.69860274",
"0.6936139",
"0.6936139",
"0.6907152",
"0.6887853",
"0.6854214",
"0.6846589",
"0.68176407",
"0.67910486",
"0.67743784",
"0.67724115",
"0.6749794",
"0.6749794",
"0.67153496",
"0.66674685",
"0.664144",
"0.662029... | 0.71473557 | 3 |
Show issue url by its id. | def show_issue(self, msg, issue_id):
self._asset_bind(msg)
yield "https://github.com/{}/issues/{}".format(task_repository_name(), issue_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def issue(ctx, number: Option(int, \"Issue number\")):\n url = f\"{repo}/issues/{number}\"\n view = discord.ui.View()\n view.add_item(discord.ui.Button(label=\"View Issue\", url=url))\n await ctx.respond(f\"Here's a link\", view=view)",
"def build_issue_tracker_url(issue_id):\n issue_tracker_t... | [
"0.6973641",
"0.67749053",
"0.64473206",
"0.63432825",
"0.63432825",
"0.6329339",
"0.6315905",
"0.6126929",
"0.6092506",
"0.603496",
"0.60116416",
"0.6002365",
"0.5886086",
"0.58832836",
"0.5794558",
"0.579267",
"0.57825845",
"0.5674633",
"0.565682",
"0.5643689",
"0.563147",
... | 0.77199227 | 0 |
Save opening issues into a text file | def cache_issue(self, msg, args):
self._asset_bind(msg)
yield ("Processing....")
trans = self._translation_util(msg)
query = "repo:{} is:open type:issue".format(
task_repository_name()
)
res = trans.cache_issues(query, OPEN_CACHE, MAX_RESULT)
yield "{}... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def issue_text_dump(issue):\n file_path = os.path.join(BIO_REQUESTS_DIR,\n str(issue.id),\n str(issue.id) + '_' + str(issue.subject) + '_redmine_details.txt')\n with open(file_path, 'w+') as file:\n for attr in dir(issue):\n file.write... | [
"0.67435944",
"0.59890133",
"0.58801615",
"0.58345985",
"0.5740403",
"0.5711492",
"0.5682778",
"0.56753564",
"0.5620525",
"0.55917394",
"0.55783993",
"0.55623657",
"0.5539575",
"0.5539575",
"0.5538157",
"0.55351144",
"0.5503311",
"0.55032104",
"0.5501654",
"0.5491713",
"0.549... | 0.0 | -1 |
Release the translating issues that have been accepted for more than specified delay days (at least 3 days) | def find_delay_issues(self, msg, delay_days, unassign):
self._asset_bind(msg)
yield ("Processing....")
trans = self._translation_util(msg)
client = self._github_operator(msg)
issues = find_delay_issues(trans, client, delay_days)
if unassign == 0:
yield ("\n".j... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def maintain_leases(self) -> None:\n while not self._stop_event.is_set():\n # Determine the appropriate duration for the lease. This is\n # based off of how long previous messages have taken to ack, with\n # a sensible default and within the ranges allowed by Pub/Sub.\n ... | [
"0.5600951",
"0.5391433",
"0.53077644",
"0.530182",
"0.5256958",
"0.5214244",
"0.52004874",
"0.5079904",
"0.50456727",
"0.5010773",
"0.49739408",
"0.49477166",
"0.49456263",
"0.4935877",
"0.4900822",
"0.48766607",
"0.48477307",
"0.4822325",
"0.48165026",
"0.48124647",
"0.4810... | 0.57937604 | 0 |
Find new files from a branch for a language. | def find_new_files_in(self, msg, branch, create_issue):
self._asset_bind(msg)
trans = self._translation_util(msg)
new_file_list = trans.find_new_files(
REPOSITORY_NAME, branch, TARGET_LANG)
if create_issue == 0:
yield ("\n".join(limit_result(new_file_list)))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_updated_files_in(self, msg, branch, create_issue):\n self._asset_bind(msg)\n trans = self._translation_util(msg)\n updated_files = trans.find_updated_files(REPOSITORY_NAME, branch, TARGET_LANG)\n if create_issue == 0:\n yield (\"\\n\".join(limit_result(list(updated_f... | [
"0.5788485",
"0.57070714",
"0.5683101",
"0.5395942",
"0.53917253",
"0.5343768",
"0.5339315",
"0.52852684",
"0.5283768",
"0.52731115",
"0.5249437",
"0.52391535",
"0.5239085",
"0.52318555",
"0.5212088",
"0.51945984",
"0.5188822",
"0.5180843",
"0.51768464",
"0.5143592",
"0.51398... | 0.64677894 | 0 |
Find updated files from a branch for a language. | def find_updated_files_in(self, msg, branch, create_issue):
self._asset_bind(msg)
trans = self._translation_util(msg)
updated_files = trans.find_updated_files(REPOSITORY_NAME, branch, TARGET_LANG)
if create_issue == 0:
yield ("\n".join(limit_result(list(updated_files.keys()))... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_files(folder):\n print_header(\"READING FILES FROM FOLDER (RECURSIVE)\", \"=\")\n files = []\n for dirpath, dirnames, filenames in os.walk(folder):\n if not dirpath.endswith(\"updates\"):\n for filename in filenames:\n root, ext = os.path.splitext(filename)\n ... | [
"0.60385823",
"0.5747651",
"0.5746556",
"0.5706303",
"0.56910634",
"0.5666029",
"0.56308615",
"0.56270456",
"0.54923",
"0.54485804",
"0.5438877",
"0.54278654",
"0.54130405",
"0.5369421",
"0.536918",
"0.5358993",
"0.53467005",
"0.53339535",
"0.5308779",
"0.52719516",
"0.525351... | 0.6335095 | 0 |
Find deleted/moved files from a branch for a language. | def find_deleted_files_in(self, msg, branch, create_issue):
self._asset_bind(msg)
trans = self._translation_util(msg)
deleted_file_list = trans.find_deleted_files(
REPOSITORY_NAME, branch, TARGET_LANG)
moved_files = trans.find_moved_files_in_deleted_files(REPOSITORY_NAME, br... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_files(folder):\n print_header(\"READING FILES FROM FOLDER (RECURSIVE)\", \"=\")\n files = []\n for dirpath, dirnames, filenames in os.walk(folder):\n if not dirpath.endswith(\"updates\"):\n for filename in filenames:\n root, ext = os.path.splitext(filename)\n ... | [
"0.56908476",
"0.55866516",
"0.54323393",
"0.532697",
"0.5286356",
"0.5263294",
"0.52617866",
"0.5237295",
"0.5224555",
"0.5216255",
"0.52129155",
"0.52084595",
"0.5203285",
"0.5158452",
"0.5138303",
"0.5135574",
"0.5109658",
"0.51079535",
"0.51076376",
"0.51058733",
"0.50981... | 0.6073978 | 0 |
Train a torch model | def train(get_generator, loss_fn, optimizer, num_epochs=1, monitor=None,
on_epoch_start=None, on_finish=None, on_error=None):
logger = logging.getLogger(__name__)
t_begin_train = timer()
for epoch in range(num_epochs):
avg_loss = 0
if on_epoch_start:
on_epoch_start(epo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(train_loader : torch.utils.data.DataLoader, model : nn.Module, criterion : nn.Module, optimizer : torch.optim.Optimizer) -> logger.Result:",
"def trainNet():",
"def train(args, model, train_data_loader, dev_data_loader, device):\n\n\tmodel.train()\n\toptimizer = torch.optim.Adam(model.parameters())\n... | [
"0.78621304",
"0.72798127",
"0.72797334",
"0.7267533",
"0.72607505",
"0.7249732",
"0.7220984",
"0.72000355",
"0.7183494",
"0.71330434",
"0.7132294",
"0.71074426",
"0.7061743",
"0.7004282",
"0.6996066",
"0.6973272",
"0.6952807",
"0.6951445",
"0.690291",
"0.68992347",
"0.688795... | 0.0 | -1 |
Returns True if word is in the wordList and is entirely composed of letters in the hand. Otherwise, returns False. Does not mutate hand or wordList. | def isValidWord(word, hand, wordList):
# TO DO ... <-- Remove this comment when you code this function
newword = {}
for x in word:
newword[x] = newword.get(x,0) + 1
for l in newword:
if newword.get(l,0) > hand.get(l, 0):
return False
is_valid = F... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isValidWord(word, hand, wordList):\r\n \r\n \r\n if type(word) != str:\r\n return False\r\n \r\n if word not in wordList:\r\n return False\r\n \r\n for letter in word:\r\n if letter not in hand:\r\n return False\r\n \r\n updatedHand = hand.copy()\r\n ... | [
"0.8187714",
"0.8107539",
"0.7983707",
"0.7944388",
"0.7943436",
"0.7918588",
"0.78547037",
"0.7823912",
"0.7772084",
"0.7752642",
"0.773667",
"0.77111965",
"0.7665853",
"0.76646894",
"0.7465163",
"0.7308262",
"0.72991234",
"0.7229191",
"0.7229191",
"0.7229191",
"0.70942104",... | 0.7354392 | 15 |
The set of arguments for constructing a TargetGroupAttachment resource. | def __init__(__self__, *,
target: pulumi.Input['TargetGroupAttachmentTargetArgs'],
target_group_identifier: pulumi.Input[str]):
pulumi.set(__self__, "target", target)
pulumi.set(__self__, "target_group_identifier", target_group_identifier) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(__self__,\n resource_name: str,\n args: TargetGroupAttachmentArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...",
"def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] =... | [
"0.77026486",
"0.74800813",
"0.7191172",
"0.61384636",
"0.57487035",
"0.5611284",
"0.56056815",
"0.5473555",
"0.5462698",
"0.5453623",
"0.5441378",
"0.54287297",
"0.54172635",
"0.53831756",
"0.51655686",
"0.51062083",
"0.5021804",
"0.5019914",
"0.50194347",
"0.501577",
"0.500... | 0.7296756 | 2 |
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