Change asserts in src/transformers/models/xlnet/ to raise ValueError (#14088)

* Change asserts in src/transformers/models/xlnet/ to raise ValueError

* Update src/transformers/models/xlnet/modeling_tf_xlnet.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/models/xlnet/configuration_xlnet.py

@@ -180,11 +180,13 @@ class XLNetConfig(PretrainedConfig):
         self.d_model = d_model
         self.n_layer = n_layer
         self.n_head = n_head
-        assert d_model % n_head == 0
+        if d_model % n_head != 0:
+            raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0")
         if "d_head" in kwargs:
-            assert (
-                kwargs["d_head"] == d_model // n_head
-            ), f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"
+            if kwargs["d_head"] != d_model // n_head:
+                raise ValueError(
+                    f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"
+                )
         self.d_head = d_model // n_head
         self.ff_activation = ff_activation
         self.d_inner = d_inner

src/transformers/models/xlnet/modeling_tf_xlnet.py

@@ -561,7 +561,8 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
                 bwd_pos_seq = tf.clip_by_value(bwd_pos_seq, -self.clamp_len, self.clamp_len)
 
             if bsz is not None:
-                assert bsz % 2 == 0, f"With bi_data, the batch size {bsz} should be divisible by 2"
+                if bsz % 2 != 0:
+                    raise ValueError(f"With bi_data, the batch size {bsz} should be divisible by 2")
                 fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz // 2)
                 bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz // 2)
             else: