delete print and make style

src/transformers/modeling_tf_utils.py

@@ -926,7 +926,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
             if temperature != 1.0:
                 next_token_logits = next_token_logits / temperature
 
-#             calculate log softmax score
+            #             calculate log softmax score
             scores = tf.nn.log_softmax(next_token_logits, axis=-1)  # (batch_size * num_beams, vocab_size)
 
             # set eos token prob to zero if min_length is not reached
@@ -937,9 +937,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                 )
                 eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [batch_size, vocab_size])
 
-                scores = set_tensor_by_indices_to_value(
-                    scores, eos_token_indices_mask, -float("inf")
-                )
+                scores = set_tensor_by_indices_to_value(scores, eos_token_indices_mask, -float("inf"))
 
             if no_repeat_ngram_size > 0:
                 # calculate a list of banned tokens to prevent repetitively generating the same ngrams
@@ -992,7 +990,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                 )  # (batch_size, num_beams * vocab_size)
 
                 next_scores, next_tokens = tf.math.top_k(next_scores, k=2 * num_beams, sorted=True)
-                print(next_tokens)
 
             assert shape_list(next_scores) == shape_list(next_tokens) == [batch_size, 2 * num_beams]