Replace assertions with ValueError exceptions (#14061)

* Replace assertions with ValueError exceptions

* Format error messages as suggested

src/transformers/generation_utils.py

@@ -479,7 +479,8 @@ class GenerationMixin:
             model_kwargs["attention_mask"] = attention_mask.index_select(0, expanded_return_idx)
 
         if is_encoder_decoder:
-            assert encoder_outputs is not None
+            if encoder_outputs is None:
+                raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
             encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
                 0, expanded_return_idx.to(encoder_outputs.last_hidden_state.device)
             )
@@ -1327,7 +1328,8 @@ class GenerationMixin:
 
             # finished sentences should have their next token be a padding token
             if eos_token_id is not None:
-                assert pad_token_id is not None, "If eos_token_id is defined, make sure that pad_token_id is defined."
+                if pad_token_id is None:
+                    raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
                 next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
 
             # update generated ids, model inputs, and length for next step
@@ -1567,7 +1569,8 @@ class GenerationMixin:
 
             # finished sentences should have their next token be a padding token
             if eos_token_id is not None:
-                assert pad_token_id is not None, "If eos_token_id is defined, make sure that pad_token_id is defined."
+                if pad_token_id is None:
+                    raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
                 next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
 
             # update generated ids, model inputs, and length for next step
@@ -1761,9 +1764,10 @@ class GenerationMixin:
 
         batch_beam_size, cur_len = input_ids.shape
 
-        assert (
-            num_beams * batch_size == batch_beam_size
-        ), f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
+        if num_beams * batch_size != batch_beam_size:
+            raise ValueError(
+                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
+            )
 
         beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
         beam_scores[:, 1:] = -1e9
@@ -2361,9 +2365,10 @@ class GenerationMixin:
 
         batch_beam_size, cur_len = input_ids.shape
 
-        assert (
-            num_beams * batch_size == batch_beam_size
-        ), f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
+        if num_beams * batch_size != batch_beam_size:
+            raise ValueError(
+                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
+            )
 
         beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
         # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in