Minor error condition bug fix (#33781)

* Error condition bug fix

* Update error message

* Update src/transformers/models/qwen2_vl/modeling_qwen2_vl.py

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* Making change in the rest of the repo

* Formatting

* Formatting with ruff

---------

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

examples/modular-transformers/modeling_dummy.py

@@ -882,7 +882,7 @@ class DummyModel(DummyPreTrainedModel):
 
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+                "You must specify exactly one of input_ids or inputs_embeds"
             )
 
         if self.gradient_checkpointing and self.training and use_cache:

examples/modular-transformers/modeling_my_new_model2.py

@@ -759,7 +759,7 @@ class MyNewModel2Model(MyNewModel2PreTrainedModel):
 
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+                "You must specify exactly one of input_ids or inputs_embeds"
             )
 
         if self.gradient_checkpointing and self.training and use_cache:

src/transformers/models/bloom/modeling_bloom.py

@@ -620,9 +620,7 @@ class BloomModel(BloomPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/chameleon/modeling_chameleon.py

@@ -1278,9 +1278,7 @@ class ChameleonModel(ChameleonPreTrainedModel):
             use_cache = False
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if pixel_values is not None and inputs_embeds is not None:
             raise ValueError(

src/transformers/models/codegen/modeling_codegen.py

@@ -459,9 +459,7 @@ class CodeGenModel(CodeGenPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/cohere/modeling_cohere.py

@@ -842,9 +842,7 @@ class CohereModel(CoherePreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/dbrx/modeling_dbrx.py

@@ -991,9 +991,7 @@ class DbrxModel(DbrxPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/falcon/modeling_falcon.py

@@ -964,9 +964,7 @@ class FalconModel(FalconPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/falcon_mamba/modeling_falcon_mamba.py

@@ -650,9 +650,7 @@ class FalconMambaModel(FalconMambaPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):  # ^ is python for xor
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if inputs_embeds is None:
             inputs_embeds = self.embeddings(input_ids)

src/transformers/models/gemma/modeling_gemma.py

@@ -754,9 +754,7 @@ class GemmaModel(GemmaPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/gemma/modular_gemma.py

@@ -667,9 +667,7 @@ class GemmaModel(LlamaModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/gemma2/modeling_gemma2.py

@@ -768,9 +768,7 @@ class Gemma2Model(Gemma2PreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/gemma2/modular_gemma2.py

@@ -605,9 +605,7 @@ class Gemma2Model(GemmaModel, Gemma2PreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/gpt_neo/modeling_gpt_neo.py

@@ -674,9 +674,7 @@ class GPTNeoModel(GPTNeoPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/gpt_neox/modeling_gpt_neox.py

@@ -876,9 +876,7 @@ class GPTNeoXModel(GPTNeoXPreTrainedModel):
         use_cache = use_cache if use_cache is not None else self.config.use_cache
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py

@@ -603,9 +603,7 @@ class GPTNeoXJapaneseModel(GPTNeoXJapanesePreTrainedModel):
         use_cache = use_cache if use_cache is not None else self.config.use_cache
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if inputs_embeds is None:
             inputs_embeds = self.embed_in(input_ids)

src/transformers/models/gptj/modeling_gptj.py

@@ -746,9 +746,7 @@ class GPTJModel(GPTJPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/granite/modeling_granite.py

@@ -766,9 +766,7 @@ class GraniteModel(GranitePreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/granitemoe/modeling_granitemoe.py

@@ -997,9 +997,7 @@ class GraniteMoeModel(GraniteMoePreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/idefics/modeling_idefics.py

@@ -1172,9 +1172,7 @@ class IdeficsModel(IdeficsPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/jamba/modeling_jamba.py

@@ -1282,9 +1282,7 @@ class JambaModel(JambaPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/jetmoe/modeling_jetmoe.py

@@ -972,9 +972,7 @@ class JetMoeModel(JetMoePreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/llama/modeling_llama.py

@@ -878,9 +878,7 @@ class LlamaModel(LlamaPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/llava/modeling_llava.py

@@ -444,9 +444,7 @@ class LlavaForConditionalGeneration(LlavaPreTrainedModel, GenerationMixin):
         )
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if pixel_values is not None and inputs_embeds is not None:
             raise ValueError(

src/transformers/models/llava_next/modeling_llava_next.py

@@ -786,9 +786,7 @@ class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel, GenerationMixi
         )
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if pixel_values is not None and inputs_embeds is not None:
             raise ValueError(

src/transformers/models/llava_next_video/modeling_llava_next_video.py

@@ -875,9 +875,7 @@ class LlavaNextVideoForConditionalGeneration(LlavaNextVideoPreTrainedModel, Gene
         )
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None:
             raise ValueError(

src/transformers/models/llava_next_video/modular_llava_next_video.py

@@ -380,9 +380,7 @@ class LlavaNextVideoForConditionalGeneration(LlavaNextForConditionalGeneration):
         )
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None:
             raise ValueError(

src/transformers/models/llava_onevision/modeling_llava_onevision.py

@@ -572,9 +572,7 @@ class LlavaOnevisionForConditionalGeneration(LlavaOnevisionPreTrainedModel, Gene
         )
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None:
             raise ValueError(

src/transformers/models/mamba/modeling_mamba.py

@@ -590,9 +590,7 @@ class MambaModel(MambaPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):  # ^ is python for xor
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if inputs_embeds is None:
             inputs_embeds = self.embeddings(input_ids)

src/transformers/models/mamba2/modeling_mamba2.py

@@ -862,9 +862,7 @@ class Mamba2Model(Mamba2PreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):  # ^ is python for xor
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if inputs_embeds is None:
             inputs_embeds = self.embeddings(input_ids)

src/transformers/models/mistral/modeling_mistral.py

@@ -749,9 +749,7 @@ class MistralModel(MistralPreTrainedModel):
 
         # retrieve input_ids and inputs_embeds
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/mixtral/modeling_mixtral.py

@@ -959,9 +959,7 @@ class MixtralModel(MixtralPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/mllama/modeling_mllama.py

@@ -1606,9 +1606,7 @@ class MllamaTextModel(MllamaPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(
@@ -2145,9 +2143,7 @@ class MllamaForConditionalGeneration(MllamaPreTrainedModel, GenerationMixin):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if pixel_values is not None and inputs_embeds is not None:
             raise ValueError(

src/transformers/models/nemotron/modeling_nemotron.py

@@ -771,9 +771,7 @@ class NemotronModel(NemotronPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/olmo/modeling_olmo.py

@@ -799,9 +799,7 @@ class OlmoModel(OlmoPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/olmoe/modeling_olmoe.py

@@ -947,9 +947,7 @@ class OlmoeModel(OlmoePreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/paligemma/modeling_paligemma.py

@@ -453,9 +453,7 @@ class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixi
         ```"""
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if pixel_values is not None and inputs_embeds is not None:
             raise ValueError(

src/transformers/models/persimmon/modeling_persimmon.py

@@ -621,9 +621,7 @@ class PersimmonModel(PersimmonPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/phi/modeling_phi.py

@@ -911,9 +911,7 @@ class PhiModel(PhiPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/phi3/modeling_phi3.py

@@ -938,9 +938,7 @@ class Phi3Model(Phi3PreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/qwen2/modeling_qwen2.py

@@ -851,9 +851,7 @@ class Qwen2Model(Qwen2PreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/qwen2_moe/modeling_qwen2_moe.py

@@ -1021,9 +1021,7 @@ class Qwen2MoeModel(Qwen2MoePreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/qwen2_vl/modeling_qwen2_vl.py

@@ -1126,9 +1126,7 @@ class Qwen2VLModel(Qwen2VLPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py

@@ -695,9 +695,7 @@ class RecurrentGemmaModel(RecurrentGemmaPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training and use_cache:
             logger.warning_once(

src/transformers/models/stablelm/modeling_stablelm.py

@@ -896,9 +896,7 @@ class StableLmModel(StableLmPreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/starcoder2/modeling_starcoder2.py

@@ -824,9 +824,7 @@ class Starcoder2Model(Starcoder2PreTrainedModel):
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if self.gradient_checkpointing and self.training:
             if use_cache:

src/transformers/models/video_llava/modeling_video_llava.py

@@ -516,9 +516,7 @@ class VideoLlavaForConditionalGeneration(VideoLlavaPreTrainedModel, GenerationMi
         )
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if (pixel_values_images is not None or pixel_values_videos is not None) and inputs_embeds is not None:
             raise ValueError(

src/transformers/models/vipllava/modeling_vipllava.py

@@ -441,9 +441,7 @@ class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel, GenerationMixin)
         )
 
         if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
 
         if pixel_values is not None and inputs_embeds is not None:
             raise ValueError(