mypy-status mypy exited with status 1.

ruff pytest_ruff.RuffError: flair/datasets/entity_linking.py:1:1: I001 [*] Import block is un-sorted or un-formatted | 1 | / import csv 2 | | import logging 3 | | import os 4 | | from pathlib import Path 5 | | from typing import Any, Dict, Iterable, Iterator, List, Optional, Union 6 | | 7 | | import requests 8 | | 9 | | import flair 10 | | from flair.data import Corpus, EntityCandidate, MultiCorpus, Sentence 11 | | from flair.datasets.sequence_labeling import (ColumnCorpus, 12 | | MultiFileColumnCorpus) 13 | | from flair.file_utils import cached_path, unpack_file 14 | | from flair.splitter import SegtokSentenceSplitter, SentenceSplitter 15 | | 16 | | log = logging.getLogger("flair") | |_^ I001 | = help: Organize imports

Black format check --- /home/runner/work/flair/flair/flair/datasets/entity_linking.py 2024-01-12 11:13:01.193661+00:00 +++ /home/runner/work/flair/flair/flair/datasets/entity_linking.py 2024-01-12 11:16:34.739765+00:00 @@ -6,12 +6,11 @@ import requests import flair from flair.data import Corpus, EntityCandidate, MultiCorpus, Sentence -from flair.datasets.sequence_labeling import (ColumnCorpus, - MultiFileColumnCorpus) +from flair.datasets.sequence_labeling import ColumnCorpus, MultiFileColumnCorpus from flair.file_utils import cached_path, unpack_file from flair.splitter import SegtokSentenceSplitter, SentenceSplitter log = logging.getLogger("flair")

ruff pytest_ruff.RuffError: flair/models/entity_mention_linking.py:1:1: I001 [*] Import block is un-sorted or un-formatted | 1 | / import copy 2 | | import inspect 3 | | import logging 4 | | import os 5 | | import re 6 | | import stat 7 | | import string 8 | | import subprocess 9 | | import tempfile 10 | | from abc import ABC, abstractmethod 11 | | from collections import defaultdict 12 | | from enum import Enum, auto 13 | | from pathlib import Path 14 | | from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast, Sequence, Set 15 | | from collections.abc import Iterable 16 | | 17 | | import numpy as np 18 | | import torch 19 | | from scipy import sparse 20 | | from torch.utils.data import Dataset 21 | | from tqdm import tqdm 22 | | 23 | | import flair 24 | | from flair.class_utils import get_state_subclass_by_name 25 | | from flair.data import DT, Dictionary, Sentence 26 | | from flair.datasets import ( 27 | | CTD_CHEMICALS_DICTIONARY, 28 | | CTD_DISEASES_DICTIONARY, 29 | | NCBI_GENE_HUMAN_DICTIONARY, 30 | | NCBI_TAXONOMY_DICTIONARY, 31 | | EntityLinkingDictionary, 32 | | HunerEntityLinkingDictionary, 33 | | ) 34 | | from flair.datasets.entity_linking import InMemoryEntityLinkingDictionary 35 | | from flair.embeddings import DocumentEmbeddings, DocumentTFIDFEmbeddings, TransformerDocumentEmbeddings 36 | | from flair.embeddings.base import load_embeddings 37 | | from flair.file_utils import cached_path, hf_download 38 | | from flair.training_utils import Result 39 | | 40 | | logger = logging.getLogger("flair") | |_^ I001 41 | 42 | PRETRAINED_DENSE_MODELS = [ | = help: Organize imports flair/models/entity_mention_linking.py:769:9: D212 [*] Multi-line docstring summary should start at the first line | 767 | dictionary: EntityLinkingDictionary, 768 | ): 769 | """ | _________^ 770 | | Initializes an entity mention linker 771 | | 772 | | Args: 773 | | candidate_generator: Strategy to find matching entities for a given mention 774 | | preprocessor: Pre-processing strategy to transform / clean entity mentions 775 | | entity_label_types: A label type or sequence of label types of the required relation entities. You can also specify a label filter in a dictionary with the label type as key and the valid entity labels as values in a set. E.g. to use only 'disease' and 'chemical' labels from a NER-tagger: `{'ner': {'disease', 'chemical'}}`. To use all labels from 'ner', pass 'ner' 776 | | label_type: The label under which the predictions of the linker should be stored 777 | | dictionary: The dictionary listing all entities 778 | | """ | |___________^ D212 779 | self.preprocessor = preprocessor 780 | self.candidate_generator = candidate_generator | = help: Remove whitespace after opening quotes flair/models/entity_mention_linking.py:769:9: D415 First line should end with a period, question mark, or exclamation point | 767 | dictionary: EntityLinkingDictionary, 768 | ): 769 | """ | _________^ 770 | | Initializes an entity mention linker 771 | | 772 | | Args: 773 | | candidate_generator: Strategy to find matching entities for a given mention 774 | | preprocessor: Pre-processing strategy to transform / clean entity mentions 775 | | entity_label_types: A label type or sequence of label types of the required relation entities. You can also specify a label filter in a dictionary with the label type as key and the valid entity labels as values in a set. E.g. to use only 'disease' and 'chemical' labels from a NER-tagger: `{'ner': {'disease', 'chemical'}}`. To use all labels from 'ner', pass 'ner' 776 | | label_type: The label under which the predictions of the linker should be stored 777 | | dictionary: The dictionary listing all entities 778 | | """

flair/models/entity_mention_linking.py 816: error: Dict entry 0 has incompatible type "str": "List[Never]"; expected "str": "Optional[Set[str]]" [dict-item] 818: error: Value expression in dictionary comprehension has incompatible type "List[Never]"; expected type "Optional[Set[str]]" [misc] 833: error: Argument 1 to "len" has incompatible type "Optional[Set[str]]"; expected "Sized" [arg-type] 834: error: Unsupported right operand type for in ("Optional[Set[str]]") [operator]

Black format check --- /home/runner/work/flair/flair/flair/models/entity_mention_linking.py 2024-01-12 11:13:01.197661+00:00 +++ /home/runner/work/flair/flair/flair/models/entity_mention_linking.py 2024-01-12 11:16:40.604077+00:00 @@ -765,18 +765,18 @@ entity_label_types: Union[str, Sequence[str], Dict[str, Optional[Set[str]]]], label_type: str, dictionary: EntityLinkingDictionary, ): """ - Initializes an entity mention linker - - Args: - candidate_generator: Strategy to find matching entities for a given mention - preprocessor: Pre-processing strategy to transform / clean entity mentions - entity_label_types: A label type or sequence of label types of the required relation entities. You can also specify a label filter in a dictionary with the label type as key and the valid entity labels as values in a set. E.g. to use only 'disease' and 'chemical' labels from a NER-tagger: `{'ner': {'disease', 'chemical'}}`. To use all labels from 'ner', pass 'ner' - label_type: The label under which the predictions of the linker should be stored - dictionary: The dictionary listing all entities + Initializes an entity mention linker + + Args: + candidate_generator: Strategy to find matching entities for a given mention + preprocessor: Pre-processing strategy to transform / clean entity mentions + entity_label_types: A label type or sequence of label types of the required relation entities. You can also specify a label filter in a dictionary with the label type as key and the valid entity labels as values in a set. E.g. to use only 'disease' and 'chemical' labels from a NER-tagger: `{'ner': {'disease', 'chemical'}}`. To use all labels from 'ner', pass 'ner' + label_type: The label under which the predictions of the linker should be stored + dictionary: The dictionary listing all entities """ self.preprocessor = preprocessor self.candidate_generator = candidate_generator self.entity_label_types = entity_label_types self._label_type = label_type @@ -794,11 +794,11 @@ def predict( self, sentences: Union[List[Sentence], Sentence], top_k: int = 1, pred_label_type: Optional[str] = None, - entity_label_types: Optional[Union[str, Sequence[str], Dict[str, Optional[Set[str]]]]] = None + entity_label_types: Optional[Union[str, Sequence[str], Dict[str, Optional[Set[str]]]]] = None, ) -> None: """Predicts the best matching top-k entity / concept identifiers of all named entities annotated with tag input_entity_annotation_layer. Args: sentences: One or more sentences to run the prediction on

Black format check --- /home/runner/work/flair/flair/tests/test_biomedical_entity_linking.py 2024-01-12 11:13:01.217661+00:00 +++ /home/runner/work/flair/flair/tests/test_biomedical_entity_linking.py 2024-01-12 11:16:53.816970+00:00 @@ -87,11 +87,11 @@ disease_dictionary = disease_linker.dictionary disease_linker.predict(sentence, pred_label_type="disease-nen", entity_label_types="diseases") gene_linker = EntityMentionLinker.load("masaenger/bio-nen-gene") gene_dictionary = gene_linker.dictionary - gene_linker.predict(sentence, pred_label_type="gene-nen", entity_label_types="genes") + gene_linker.predict(sentence, pred_label_type="gene-nen", entity_label_types="genes") print("Diseases") for label in sentence.get_labels("disease-nen"): candidate = disease_dictionary[label.value] print(f"Candidate: {candidate.concept_name}")