Replace all assert by ValueError in src/transformers/models/electra

src/transformers/models/electra/modeling_electra.py

@@ -139,9 +139,8 @@ def load_tf_weights_in_electra(model, config, tf_checkpoint_path, discriminator_
  elif m_name == "kernel":
  array = np.transpose(array)
  try:
- assert (
- pointer.shape == array.shape
- ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
+ if not pointer.shape == array.shape:
+ raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
  except AssertionError as e:
  e.args += (pointer.shape, array.shape)
  raise
@@ -447,7 +446,8 @@ def __init__(self, config):
  self.is_decoder = config.is_decoder
  self.add_cross_attention = config.add_cross_attention
  if self.add_cross_attention:
- assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
+ if not self.is_decoder:
+ raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
  self.crossattention = ElectraAttention(config)
  self.intermediate = ElectraIntermediate(config)
  self.output = ElectraOutput(config)
@@ -482,9 +482,8 @@ def forward(
 
  cross_attn_present_key_value = None
  if self.is_decoder and encoder_hidden_states is not None:
- assert hasattr(
- self, "crossattention"
- ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
+ if not hasattr(self, "crossattention"):
+ raise ValueError(f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`")
 
  # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
  cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None

src/transformers/models/electra/modeling_tf_electra.py

@@ -404,7 +404,9 @@ def call(
  Returns:
  final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
  """
- assert not (input_ids is None and inputs_embeds is None)
+ if input_ids is None and inputs_embeds is None:
+ raise ValueError()
+
 
  if input_ids is not None:
  inputs_embeds = tf.gather(params=self.weight, indices=input_ids)