black

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"]
@@ -249,9 +249,9 @@ def to_feature_list(
  # 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(
@@ -538,7 +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
 

jiant/tasks/lib/templates/squad_style/utils.py

@@ -177,10 +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)
 
@@ -375,10 +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)
 
@@ -549,7 +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