Update noqa syntax (#1734)

Author: tamirkamara

docs/samples/python/example_custom_lambda_anonymizer.py

@@ -46,14 +46,14 @@ def anonymize_faker_lambda(analyzer_results, text_to_anonymize):
     analyzer = AnalyzerEngine()
     anonymizer = AnonymizerEngine()
 
-    text = "The user has the following two emails: email1@contoso.com and email2@contoso.com"  # noqa E501
+    text = "The user has the following two emails: email1@contoso.com and email2@contoso.com"  # noqa: E501
     analyzer_results = analyzer.analyze(
         text=text, entities=["EMAIL_ADDRESS"], language="en"
     )
     print(f"Original Text: {text}")
     print(f"Analyzer result: {analyzer_results}\n")
 
     print(
-        f"Reverse lambda result: {anonymize_reverse_lambda(analyzer_results, text).text}"  # noqa E501
+        f"Reverse lambda result: {anonymize_reverse_lambda(analyzer_results, text).text}"  # noqa: E501
     )
     print(f"Faker lambda result: {anonymize_faker_lambda(analyzer_results, text).text}")

presidio-analyzer/presidio_analyzer/context_aware_enhancers/lemma_context_aware_enhancer.py

@@ -64,7 +64,7 @@ def enhance_using_context(
                               accuracy of the context enhancement process
         :param recognizers: the list of recognizers
         :param context: list of context words
-        """  # noqa D205 D400
+        """  # noqa: D205,D400
 
         # create a deep copy of the results object, so we can manipulate it
         results = copy.deepcopy(raw_results)
@@ -245,7 +245,7 @@ def _find_index_of_match_token(
         word: str,
         start: int,
         tokens,
-        tokens_indices: List[int],  # noqa ANN001
+        tokens_indices: List[int],  # noqa: ANN001
     ) -> int:
         found = False
         # we use the known start index of the original word to find the actual

presidio-analyzer/presidio_analyzer/nlp_engine/ner_model_configuration.py

@@ -46,7 +46,7 @@ class NerModelConfiguration:
     Set of entity names that are likely to have low detection accuracy that should be adjusted.
     :param low_confidence_score_multiplier: A multiplier for the score given for low_score_entity_names.
     Multiplier to the score given for low_score_entity_names.
-    """  # noqa E501
+    """  # noqa: E501
 
     labels_to_ignore: Optional[Collection[str]] = None
     aggregation_strategy: Optional[str] = "max"
@@ -113,8 +113,8 @@ def to_dict(self) -> Dict:
         """Return the configuration as a dict."""
         return self.__dict__
 
-    def __str__(self) -> str:  # noqa D105
+    def __str__(self) -> str:  # noqa: D105
         return str(self.to_dict())
 
-    def __repr__(self) -> str:  # noqa D105
+    def __repr__(self) -> str:  # noqa: D105
         return str(self)

presidio-analyzer/presidio_analyzer/nlp_engine/nlp_artifacts.py

@@ -26,7 +26,7 @@ def __init__(
         tokens: Doc,
         tokens_indices: List[int],
         lemmas: List[str],
-        nlp_engine: "NlpEngine",  # noqa F821
+        nlp_engine: "NlpEngine",  # noqa: F821
         language: str,
         scores: Optional[List[float]] = None,
     ):
@@ -42,7 +42,7 @@ def __init__(
     def set_keywords(
         nlp_engine,
         lemmas: List[str],
-        language: str,  # noqa ANN001
+        language: str,  # noqa: ANN001
     ) -> List[str]:
         """
         Return keywords fpr text.

presidio-analyzer/presidio_analyzer/nlp_engine/nlp_engine.py

@@ -31,7 +31,7 @@ def process_batch(
         language: str,
         batch_size: int = 1,
         n_process: int = 1,
-        **kwargs,  # noqa ANN003
+        **kwargs,  # noqa: ANN003
     ) -> Iterator[Tuple[str, NlpArtifacts]]:
         """Execute the NLP pipeline on a batch of texts.
 

presidio-analyzer/presidio_analyzer/nlp_engine/transformers_nlp_engine.py

@@ -90,7 +90,7 @@ def load(self) -> None:
                     "annotate": "spans",
                     "stride": self.ner_model_configuration.stride,
                     "alignment_mode": self.ner_model_configuration.alignment_mode,
-                    "aggregation_strategy": self.ner_model_configuration.aggregation_strategy,  # noqa E501
+                    "aggregation_strategy": self.ner_model_configuration.aggregation_strategy,  # noqa: E501
                     "annotate_spans_key": self.entity_key,
                 },
             )

presidio-analyzer/presidio_analyzer/pattern_recognizer.py

@@ -72,7 +72,7 @@ def __init__(
         else:
             self.deny_list = []
 
-    def load(self):  # noqa D102
+    def load(self):  # noqa: D102
         pass
 
     def analyze(

presidio-analyzer/presidio_analyzer/predefined_recognizers/country_specific/poland/pl_pesel_recognizer.py

@@ -41,7 +41,7 @@ def __init__(
             supported_language=supported_language,
         )
 
-    def validate_result(self, pattern_text: str) -> bool:  # noqa D102
+    def validate_result(self, pattern_text: str) -> bool:  # noqa: D102
         digits = [int(digit) for digit in pattern_text]
         weights = [1, 3, 7, 9, 1, 3, 7, 9, 1, 3]
 

presidio-analyzer/presidio_analyzer/predefined_recognizers/country_specific/spain/es_nif_recognizer.py

@@ -46,7 +46,7 @@ def __init__(
             supported_language=supported_language,
         )
 
-    def validate_result(self, pattern_text: str) -> bool:  # noqa D102
+    def validate_result(self, pattern_text: str) -> bool:  # noqa: D102
         pattern_text = EntityRecognizer.sanitize_value(
             pattern_text, self.replacement_pairs
         )

presidio-analyzer/presidio_analyzer/predefined_recognizers/country_specific/us/aba_routing_recognizer.py

@@ -58,7 +58,7 @@ def __init__(
             supported_language=supported_language,
         )
 
-    def validate_result(self, pattern_text: str) -> bool:  # noqa D102
+    def validate_result(self, pattern_text: str) -> bool:  # noqa: D102
         sanitized_value = EntityRecognizer.sanitize_value(
             pattern_text, self.replacement_pairs
         )