flake8

nyu-mll · Apr 27, 2021 · df43f26 · df43f26
1 parent 28fea64
commit df43f26
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Showing 2 changed files with 17 additions and 15 deletions.
diff --git a/jiant/tasks/lib/templates/squad_style/core.py b/jiant/tasks/lib/templates/squad_style/core.py
@@ -91,7 +91,7 @@ def to_feature_list(
             end_position = self.end_position
 
             # If the answer cannot be found in the text, then skip this example.
-            actual_text = " ".join(self.doc_tokens[start_position : (end_position + 1)])
+            actual_text = " ".join(self.doc_tokens[start_position: (end_position + 1)])
             cleaned_answer_text = " ".join(whitespace_tokenize(self.answer_text))
             if actual_text.find(cleaned_answer_text) == -1:
                 logger.warning(
@@ -193,7 +193,7 @@ def to_feature_list(
                         - 1
                         - encoded_dict["input_ids"][::-1].index(tokenizer.pad_token_id)
                     )
-                    non_padded_ids = encoded_dict["input_ids"][last_padding_id_position + 1 :]
+                    non_padded_ids = encoded_dict["input_ids"][last_padding_id_position + 1:]
 
             else:
                 non_padded_ids = encoded_dict["input_ids"]
@@ -244,13 +244,14 @@ def to_feature_list(
             # Identify the position of the CLS token
             cls_index = span["input_ids"].index(tokenizer.cls_token_id)
 
-            # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
+            # p_mask: mask with 1 for token than cannot be in the answer (0 for token
+            # which can be in an answer)
             # Original TF implementation also keep the classification token (set to 0)
             p_mask = np.ones_like(span["token_type_ids"])
             if tokenizer.padding_side == "right":
-                p_mask[len(truncated_query) + sequence_added_tokens :] = 0
+                p_mask[len(truncated_query) + sequence_added_tokens:] = 0
             else:
-                p_mask[-len(span["tokens"]) : -(len(truncated_query) + sequence_added_tokens)] = 0
+                p_mask[-len(span["tokens"]): -(len(truncated_query) + sequence_added_tokens)] = 0
 
             pad_token_indices = np.where(span["input_ids"] == tokenizer.pad_token_id)
             special_token_indices = np.asarray(
@@ -537,7 +538,7 @@ def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_ans
 
     for new_start in range(input_start, input_end + 1):
         for new_end in range(input_end, new_start - 1, -1):
-            text_span = " ".join(doc_tokens[new_start : (new_end + 1)])
+            text_span = " ".join(doc_tokens[new_start: (new_end + 1)])
             if text_span == tok_answer_text:
                 return new_start, new_end
 

diff --git a/jiant/tasks/lib/templates/squad_style/utils.py b/jiant/tasks/lib/templates/squad_style/utils.py
@@ -35,20 +35,21 @@ class SquadResult:
 
 
 class ExplicitEnum(Enum):
-    """
-    Enum with more explicit error message for missing values.
+    """Enum with more explicit error message for missing values.
     """
 
     @classmethod
     def _missing_(cls, value):
         raise ValueError(
-            f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}"
+            f"{value} is not a valid {cls.__name__}, please select one \
+            of {list(cls._value2member_map_.keys())}"
         )
 
 
 class TruncationStrategy(ExplicitEnum):
     """
-    Possible values for the ``truncation`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for
+    Possible values for the ``truncation`` argument in
+    :meth:`PreTrainedTokenizerBase.__call__`. Useful for
     tab-completion in an IDE.
     """
 
@@ -176,10 +177,10 @@ def compute_predictions_logits_v2(
                 break
             feature = features[pred.feature_index]
             if pred.start_index > 0:  # this is a non-null prediction
-                tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
+                tok_tokens = feature.tokens[pred.start_index: (pred.end_index + 1)]
                 orig_doc_start = feature.token_to_orig_map[pred.start_index]
                 orig_doc_end = feature.token_to_orig_map[pred.end_index]
-                orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
+                orig_tokens = example.doc_tokens[orig_doc_start: (orig_doc_end + 1)]
 
                 tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
 
@@ -374,10 +375,10 @@ def compute_predictions_logits(
                 break
             feature = features[pred.feature_index]
             if pred.start_index > 0:  # this is a non-null prediction
-                tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
+                tok_tokens = feature.tokens[pred.start_index: (pred.end_index + 1)]
                 orig_doc_start = feature.token_to_orig_map[pred.start_index]
                 orig_doc_end = feature.token_to_orig_map[pred.end_index]
-                orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
+                orig_tokens = example.doc_tokens[orig_doc_start: (orig_doc_end + 1)]
 
                 tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
 
@@ -548,7 +549,7 @@ def _strip_spaces(text):
     if orig_end_position is None:
         return orig_text
 
-    output_text = orig_text[orig_start_position : (orig_end_position + 1)]
+    output_text = orig_text[orig_start_position: (orig_end_position + 1)]
     return output_text