chore: enable singleton-comparison and cleanup (#3849)

* enable singleton-comparison

* fix triadaptive_model bug

haystack/document_stores/base.py

@@ -444,7 +444,7 @@ class BaseDocumentStore(BaseComponent):
                 f"Split by sentence not supported.\n"
                 f"Please set 'split_by' to either 'word' or 'passage' in the supplied PreProcessor."
             )
-            assert preprocessor.split_respect_sentence_boundary == False, (
+            assert preprocessor.split_respect_sentence_boundary is False, (
                 f"split_respect_sentence_boundary not supported yet.\n"
                 f"Please set 'split_respect_sentence_boundary' to False in the supplied PreProcessor."
             )
@@ -452,15 +452,15 @@ class BaseDocumentStore(BaseComponent):
                 f"Overlapping documents are currently not supported when adding eval data.\n"
                 f"Please set 'split_overlap=0' in the supplied PreProcessor."
             )
-            assert preprocessor.clean_empty_lines == False, (
+            assert preprocessor.clean_empty_lines is False, (
                 f"clean_empty_lines currently not supported when adding eval data.\n"
                 f"Please set 'clean_empty_lines=False' in the supplied PreProcessor."
             )
-            assert preprocessor.clean_whitespace == False, (
+            assert preprocessor.clean_whitespace is False, (
                 f"clean_whitespace is currently not supported when adding eval data.\n"
                 f"Please set 'clean_whitespace=False' in the supplied PreProcessor."
             )
-            assert preprocessor.clean_header_footer == False, (
+            assert preprocessor.clean_header_footer is False, (
                 f"clean_header_footer is currently not supported when adding eval data.\n"
                 f"Please set 'clean_header_footer=False' in the supplied PreProcessor."
             )

haystack/document_stores/pinecone.py

@@ -1359,7 +1359,7 @@ class PineconeDocumentStore(BaseDocumentStore):
             # Explode dict of dicts into single flattened dict
             for key, value in meta.items():
                 # Replace any None values with empty strings
-                if value == None:
+                if value is None:
                     value = ""
                 # format key
                 new_key = f"{parent_key}.{key}" if parent_key else key

haystack/modeling/infer.py

@@ -103,7 +103,7 @@ class Inferencer:
 
         # TODO add support for multiple prediction heads
 
-        self.name = name if name != None else f"anonymous-{self.task_type}"
+        self.name = name if name is not None else f"anonymous-{self.task_type}"
         self.return_class_probs = return_class_probs
 
         model.connect_heads_with_processor(processor.tasks, require_labels=False)

haystack/modeling/model/triadaptive_model.py

@@ -305,7 +305,7 @@ class TriAdaptiveModel(nn.Module):
 
         # Forward pass for text passages and tables
         if "passage_input_ids" in kwargs.keys():
-            table_mask = torch.flatten(kwargs["is_table"]) == True
+            table_mask = torch.flatten(kwargs["is_table"]) == 1
 
             # Current batch consists of only tables
             if all(table_mask):

haystack/nodes/reader/farm.py

@@ -1089,7 +1089,7 @@ class FARMReader(BaseReader):
                     # create new one
                     else:
                         # We don't need to create an answer dict if is_impossible / no_answer
-                        if label.no_answer == True:
+                        if label.no_answer is True:
                             aggregated_per_question[aggregation_key] = {
                                 "id": str(hash(str(doc_id) + label.query)),
                                 "question": label.query,

haystack/schema.py

@@ -669,7 +669,7 @@ class MultiLabel:
         if drop_negative_labels:
             labels = [l for l in labels if is_positive_label(l)]
         if drop_no_answers:
-            labels = [l for l in labels if l.no_answer == False]
+            labels = [l for l in labels if l.no_answer is False]
 
         self._labels = labels
         self._query = self._aggregate_labels(key="query", must_be_single_value=True)[0]

haystack/utils/labels.py

@@ -51,7 +51,7 @@ def aggregate_labels(
 
     # drop no_answers in order to not create empty MultiLabels
     if drop_no_answers:
-        labels = [label for label in labels if label.no_answer == False]
+        labels = [label for label in labels if label.no_answer is False]
 
     # add filters for closed domain and dynamic metadata aggregation
     for l in labels: