diff --git a/examples/flax/question-answering/run_qa.py b/examples/flax/question-answering/run_qa.py
index e072f3f75b..220400c300 100644
--- a/examples/flax/question-answering/run_qa.py
+++ b/examples/flax/question-answering/run_qa.py
@@ -545,7 +545,7 @@ def main():
 
     # region Tokenizer check: this script requires a fast tokenizer.
     if not isinstance(tokenizer, PreTrainedTokenizerFast):
-        raise ValueError(
+        raise TypeError(
             "This example script only works for models that have a fast tokenizer. Check out the big table of models at"
             " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
             " this requirement"
diff --git a/examples/pytorch/question-answering/run_qa.py b/examples/pytorch/question-answering/run_qa.py
index 1a617d6a87..0a1985ce8a 100755
--- a/examples/pytorch/question-answering/run_qa.py
+++ b/examples/pytorch/question-answering/run_qa.py
@@ -356,7 +356,7 @@ def main():
 
     # Tokenizer check: this script requires a fast tokenizer.
     if not isinstance(tokenizer, PreTrainedTokenizerFast):
-        raise ValueError(
+        raise TypeError(
             "This example script only works for models that have a fast tokenizer. Check out the big table of models at"
             " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
             " this requirement"
diff --git a/examples/pytorch/token-classification/run_ner.py b/examples/pytorch/token-classification/run_ner.py
index ce89fcef5d..e6b9ee8bbe 100755
--- a/examples/pytorch/token-classification/run_ner.py
+++ b/examples/pytorch/token-classification/run_ner.py
@@ -398,7 +398,7 @@ def main():
 
     # Tokenizer check: this script requires a fast tokenizer.
     if not isinstance(tokenizer, PreTrainedTokenizerFast):
-        raise ValueError(
+        raise TypeError(
             "This example script only works for models that have a fast tokenizer. Check out the big table of models at"
             " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
             " this requirement"
diff --git a/examples/tensorflow/question-answering/run_qa.py b/examples/tensorflow/question-answering/run_qa.py
index aaf8cadca9..2acf7cb062 100755
--- a/examples/tensorflow/question-answering/run_qa.py
+++ b/examples/tensorflow/question-answering/run_qa.py
@@ -377,7 +377,7 @@ def main():
 
     # region Tokenizer check: this script requires a fast tokenizer.
     if not isinstance(tokenizer, PreTrainedTokenizerFast):
-        raise ValueError(
+        raise TypeError(
             "This example script only works for models that have a fast tokenizer. Check out the big table of models at"
             " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
             " this requirement"
diff --git a/src/transformers/image_transforms.py b/src/transformers/image_transforms.py
index 38acfc250a..deb988171a 100644
--- a/src/transformers/image_transforms.py
+++ b/src/transformers/image_transforms.py
@@ -416,7 +416,7 @@ def normalize(
             The channel dimension format of the input image. If unset, will use the inferred format from the input.
     """
     if not isinstance(image, np.ndarray):
-        raise ValueError("image must be a numpy array")
+        raise TypeError("image must be a numpy array")
 
     if input_data_format is None:
         input_data_format = infer_channel_dimension_format(image)
diff --git a/src/transformers/integrations/integration_utils.py b/src/transformers/integrations/integration_utils.py
index fa5f2da11e..2b7fd9e756 100755
--- a/src/transformers/integrations/integration_utils.py
+++ b/src/transformers/integrations/integration_utils.py
@@ -768,7 +768,7 @@ class WandbLogModel(str, Enum):
     @classmethod
     def _missing_(cls, value: Any) -> "WandbLogModel":
         if not isinstance(value, str):
-            raise ValueError(f"Expecting to have a string `WANDB_LOG_MODEL` setting, but got {type(value)}")
+            raise TypeError(f"Expecting to have a string `WANDB_LOG_MODEL` setting, but got {type(value)}")
         if value.upper() in ENV_VARS_TRUE_VALUES:
             raise DeprecationWarning(
                 f"Setting `WANDB_LOG_MODEL` as {os.getenv('WANDB_LOG_MODEL')} is deprecated and will be removed in "
diff --git a/src/transformers/modeling_tf_utils.py b/src/transformers/modeling_tf_utils.py
index 5f70e4e2b9..4669971098 100644
--- a/src/transformers/modeling_tf_utils.py
+++ b/src/transformers/modeling_tf_utils.py
@@ -185,7 +185,7 @@ def keras_serializable(cls):
             else:
                 initializer(self, config, *args, **kwargs)
         else:
-            raise ValueError("Must pass either `config` (PretrainedConfig) or `config` (dict)")
+            raise TypeError("Must pass either `config` (PretrainedConfig) or `config` (dict)")
 
         self._config = config
         self._kwargs = kwargs
diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
index dc6926d43f..61110e926d 100644
--- a/src/transformers/modeling_utils.py
+++ b/src/transformers/modeling_utils.py
@@ -2088,7 +2088,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, PushToHubMixin, PeftAdapterMi
     def __init__(self, config: PretrainedConfig, *inputs, **kwargs):
         super().__init__()
         if not isinstance(config, PretrainedConfig):
-            raise ValueError(
+            raise TypeError(
                 f"Parameter config in `{self.__class__.__name__}(config)` should be an instance of class "
                 "`PretrainedConfig`. To create a model from a pretrained model use "
                 f"`model = {self.__class__.__name__}.from_pretrained(PRETRAINED_MODEL_NAME)`"
diff --git a/src/transformers/models/aria/modular_aria.py b/src/transformers/models/aria/modular_aria.py
index 95e3bad057..f6303b4d38 100644
--- a/src/transformers/models/aria/modular_aria.py
+++ b/src/transformers/models/aria/modular_aria.py
@@ -999,7 +999,7 @@ class AriaProcessor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         if images is not None:
             image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
diff --git a/src/transformers/models/aria/processing_aria.py b/src/transformers/models/aria/processing_aria.py
index 07a684cd76..d694e59242 100644
--- a/src/transformers/models/aria/processing_aria.py
+++ b/src/transformers/models/aria/processing_aria.py
@@ -120,7 +120,7 @@ class AriaProcessor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         if images is not None:
             image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
diff --git a/src/transformers/models/clip/tokenization_clip_fast.py b/src/transformers/models/clip/tokenization_clip_fast.py
index 4de1504a80..c859d4572d 100644
--- a/src/transformers/models/clip/tokenization_clip_fast.py
+++ b/src/transformers/models/clip/tokenization_clip_fast.py
@@ -81,7 +81,7 @@ class CLIPTokenizerFast(PreTrainedTokenizerFast):
         )
 
         if not isinstance(self.backend_tokenizer.pre_tokenizer, pre_tokenizers.Sequence):
-            raise ValueError(
+            raise TypeError(
                 "The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"
                 " heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"
                 " to be compatible with this version.The easiest way to do so is"
diff --git a/src/transformers/models/gemma3/processing_gemma3.py b/src/transformers/models/gemma3/processing_gemma3.py
index eaa0ab3b76..0cfd7a466e 100644
--- a/src/transformers/models/gemma3/processing_gemma3.py
+++ b/src/transformers/models/gemma3/processing_gemma3.py
@@ -97,7 +97,7 @@ class Gemma3Processor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         image_inputs = {}
         if images is not None:
diff --git a/src/transformers/models/grounding_dino/image_processing_grounding_dino.py b/src/transformers/models/grounding_dino/image_processing_grounding_dino.py
index cf18dd43a1..d56a24cb57 100644
--- a/src/transformers/models/grounding_dino/image_processing_grounding_dino.py
+++ b/src/transformers/models/grounding_dino/image_processing_grounding_dino.py
@@ -831,7 +831,7 @@ def _scale_boxes(boxes, target_sizes):
     elif isinstance(target_sizes, torch.Tensor):
         image_height, image_width = target_sizes.unbind(1)
     else:
-        raise ValueError("`target_sizes` must be a list, tuple or torch.Tensor")
+        raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor")
 
     scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1)
     scale_factor = scale_factor.unsqueeze(1).to(boxes.device)
diff --git a/src/transformers/models/grounding_dino/image_processing_grounding_dino_fast.py b/src/transformers/models/grounding_dino/image_processing_grounding_dino_fast.py
index 93f68f415e..695b65629a 100644
--- a/src/transformers/models/grounding_dino/image_processing_grounding_dino_fast.py
+++ b/src/transformers/models/grounding_dino/image_processing_grounding_dino_fast.py
@@ -307,7 +307,7 @@ def _scale_boxes(boxes, target_sizes):
     elif isinstance(target_sizes, torch.Tensor):
         image_height, image_width = target_sizes.unbind(1)
     else:
-        raise ValueError("`target_sizes` must be a list, tuple or torch.Tensor")
+        raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor")
 
     scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1)
     scale_factor = scale_factor.unsqueeze(1).to(boxes.device)
diff --git a/src/transformers/models/grounding_dino/modular_grounding_dino.py b/src/transformers/models/grounding_dino/modular_grounding_dino.py
index 7ebd170d42..e49a79fdde 100644
--- a/src/transformers/models/grounding_dino/modular_grounding_dino.py
+++ b/src/transformers/models/grounding_dino/modular_grounding_dino.py
@@ -40,7 +40,7 @@ def _scale_boxes(boxes, target_sizes):
     elif isinstance(target_sizes, torch.Tensor):
         image_height, image_width = target_sizes.unbind(1)
     else:
-        raise ValueError("`target_sizes` must be a list, tuple or torch.Tensor")
+        raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor")
 
     scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1)
     scale_factor = scale_factor.unsqueeze(1).to(boxes.device)
diff --git a/src/transformers/models/llava/processing_llava.py b/src/transformers/models/llava/processing_llava.py
index 3d1cfc61e6..5bac76cb44 100644
--- a/src/transformers/models/llava/processing_llava.py
+++ b/src/transformers/models/llava/processing_llava.py
@@ -145,7 +145,7 @@ class LlavaProcessor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         # try to expand inputs in processing if we have the necessary parts
         prompt_strings = text
diff --git a/src/transformers/models/llava_next/processing_llava_next.py b/src/transformers/models/llava_next/processing_llava_next.py
index 05efc60fcd..c5760aa169 100644
--- a/src/transformers/models/llava_next/processing_llava_next.py
+++ b/src/transformers/models/llava_next/processing_llava_next.py
@@ -149,7 +149,7 @@ class LlavaNextProcessor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         prompt_strings = text
         if image_inputs:
diff --git a/src/transformers/models/llava_next_video/processing_llava_next_video.py b/src/transformers/models/llava_next_video/processing_llava_next_video.py
index dd039ff024..b50aee5af2 100644
--- a/src/transformers/models/llava_next_video/processing_llava_next_video.py
+++ b/src/transformers/models/llava_next_video/processing_llava_next_video.py
@@ -176,7 +176,7 @@ class LlavaNextVideoProcessor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         if image_inputs:
             image_sizes = iter(image_inputs["image_sizes"])
diff --git a/src/transformers/models/llava_onevision/processing_llava_onevision.py b/src/transformers/models/llava_onevision/processing_llava_onevision.py
index a3f2e28359..5a17455a52 100644
--- a/src/transformers/models/llava_onevision/processing_llava_onevision.py
+++ b/src/transformers/models/llava_onevision/processing_llava_onevision.py
@@ -157,7 +157,7 @@ class LlavaOnevisionProcessor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         image_inputs = video_inputs = {}
 
diff --git a/src/transformers/models/mllama/image_processing_mllama.py b/src/transformers/models/mllama/image_processing_mllama.py
index f918908c3d..01ae14c9f5 100644
--- a/src/transformers/models/mllama/image_processing_mllama.py
+++ b/src/transformers/models/mllama/image_processing_mllama.py
@@ -473,7 +473,7 @@ def to_channel_dimension_format(
             The image with the channel dimension set to `channel_dim`.
     """
     if not isinstance(image, np.ndarray):
-        raise ValueError(f"Input image must be of type np.ndarray, got {type(image)}")
+        raise TypeError(f"Input image must be of type np.ndarray, got {type(image)}")
 
     if input_channel_dim is None:
         input_channel_dim = infer_channel_dimension_format(image)
diff --git a/src/transformers/models/myt5/tokenization_myt5.py b/src/transformers/models/myt5/tokenization_myt5.py
index e262a70fca..251e3d602b 100644
--- a/src/transformers/models/myt5/tokenization_myt5.py
+++ b/src/transformers/models/myt5/tokenization_myt5.py
@@ -48,7 +48,7 @@ class ByteRewriter:
             with open(rewriting_rules, "r") as f:
                 rewriting_rules = json.load(f)
         elif not isinstance(rewriting_rules, dict):
-            raise ValueError(
+            raise TypeError(
                 f"rewriting_rules should be either a path to json file or a dict, got {type(rewriting_rules)}"
             )
 
diff --git a/src/transformers/models/owlv2/image_processing_owlv2.py b/src/transformers/models/owlv2/image_processing_owlv2.py
index 33a14adfb0..0db195ba16 100644
--- a/src/transformers/models/owlv2/image_processing_owlv2.py
+++ b/src/transformers/models/owlv2/image_processing_owlv2.py
@@ -86,7 +86,7 @@ def _scale_boxes(boxes, target_sizes):
     elif isinstance(target_sizes, torch.Tensor):
         image_height, image_width = target_sizes.unbind(1)
     else:
-        raise ValueError("`target_sizes` must be a list, tuple or torch.Tensor")
+        raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor")
 
     # for owlv2 image is padded to max size unlike owlvit, that's why we have to scale boxes to max size
     max_size = torch.max(image_height, image_width)
diff --git a/src/transformers/models/owlvit/image_processing_owlvit.py b/src/transformers/models/owlvit/image_processing_owlvit.py
index 8d3448e688..4bf9306c56 100644
--- a/src/transformers/models/owlvit/image_processing_owlvit.py
+++ b/src/transformers/models/owlvit/image_processing_owlvit.py
@@ -82,7 +82,7 @@ def _scale_boxes(boxes, target_sizes):
     elif isinstance(target_sizes, torch.Tensor):
         image_height, image_width = target_sizes.unbind(1)
     else:
-        raise ValueError("`target_sizes` must be a list, tuple or torch.Tensor")
+        raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor")
 
     scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1)
     scale_factor = scale_factor.unsqueeze(1).to(boxes.device)
diff --git a/src/transformers/models/phi4_multimodal/processing_phi4_multimodal.py b/src/transformers/models/phi4_multimodal/processing_phi4_multimodal.py
index b9a97391e9..58c6619ba1 100644
--- a/src/transformers/models/phi4_multimodal/processing_phi4_multimodal.py
+++ b/src/transformers/models/phi4_multimodal/processing_phi4_multimodal.py
@@ -128,7 +128,7 @@ class Phi4MultimodalProcessor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         image_token = self.tokenizer.image_token
         audio_token = self.tokenizer.audio_token
diff --git a/src/transformers/models/phimoe/configuration_phimoe.py b/src/transformers/models/phimoe/configuration_phimoe.py
index 5fea626c30..80fefd3dc9 100644
--- a/src/transformers/models/phimoe/configuration_phimoe.py
+++ b/src/transformers/models/phimoe/configuration_phimoe.py
@@ -181,13 +181,11 @@ class PhimoeConfig(PretrainedConfig):
             rope_scaling_short_mscale = self.rope_scaling.get("short_mscale", None)
             rope_scaling_long_mscale = self.rope_scaling.get("long_mscale", None)
             if not isinstance(rope_scaling_short_mscale, (int, float)):
-                raise ValueError(
+                raise TypeError(
                     f"`rope_scaling`'s short_mscale field must be a number, got {rope_scaling_short_mscale}"
                 )
             if not isinstance(rope_scaling_long_mscale, (int, float)):
-                raise ValueError(
-                    f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}"
-                )
+                raise TypeError(f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}")
 
         rope_config_validation(self)
 
diff --git a/src/transformers/models/pixtral/processing_pixtral.py b/src/transformers/models/pixtral/processing_pixtral.py
index 16ebaa0d55..b38c5917d7 100644
--- a/src/transformers/models/pixtral/processing_pixtral.py
+++ b/src/transformers/models/pixtral/processing_pixtral.py
@@ -188,7 +188,7 @@ class PixtralProcessor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         # try to expand inputs in processing if we have the necessary parts
         prompt_strings = text
diff --git a/src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py b/src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
index c9584dfe3a..8174f22af2 100644
--- a/src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
+++ b/src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
@@ -3247,7 +3247,7 @@ class TorchActivation1d(nn.Module):
     ):
         super().__init__()
         if not callable(activation):
-            raise ValueError("Activation function must be callable")
+            raise TypeError("Activation function must be callable")
         self.act = activation
         self.upsample = UpSample1d(up_ratio, up_kernel_size)
         self.downsample = DownSample1d(down_ratio, down_kernel_size)
diff --git a/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py b/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py
index cfd0e29c97..5b501cf7f9 100644
--- a/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py
+++ b/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py
@@ -3533,7 +3533,7 @@ class TorchActivation1d(nn.Module):
     ):
         super().__init__()
         if not callable(activation):
-            raise ValueError("Activation function must be callable")
+            raise TypeError("Activation function must be callable")
         self.act = activation
         self.upsample = UpSample1d(up_ratio, up_kernel_size)
         self.downsample = DownSample1d(down_ratio, down_kernel_size)
diff --git a/src/transformers/models/sam/image_processing_sam.py b/src/transformers/models/sam/image_processing_sam.py
index c431bb72ca..7591da5af9 100644
--- a/src/transformers/models/sam/image_processing_sam.py
+++ b/src/transformers/models/sam/image_processing_sam.py
@@ -668,7 +668,7 @@ class SamImageProcessor(BaseImageProcessor):
             if isinstance(masks[i], np.ndarray):
                 masks[i] = torch.from_numpy(masks[i])
             elif not isinstance(masks[i], torch.Tensor):
-                raise ValueError("Input masks should be a list of `torch.tensors` or a list of `np.ndarray`")
+                raise TypeError("Input masks should be a list of `torch.tensors` or a list of `np.ndarray`")
             interpolated_mask = F.interpolate(masks[i], target_image_size, mode="bilinear", align_corners=False)
             interpolated_mask = interpolated_mask[..., : reshaped_input_sizes[i][0], : reshaped_input_sizes[i][1]]
             interpolated_mask = F.interpolate(interpolated_mask, original_size, mode="bilinear", align_corners=False)
@@ -1119,7 +1119,7 @@ def _generate_crop_boxes(
     """
 
     if isinstance(image, list):
-        raise ValueError("Only one image is allowed for crop generation.")
+        raise TypeError("Only one image is allowed for crop generation.")
     image = to_numpy_array(image)
     original_size = get_image_size(image, input_data_format)
 
diff --git a/src/transformers/models/video_llava/processing_video_llava.py b/src/transformers/models/video_llava/processing_video_llava.py
index d0b061892d..d7aababe6f 100644
--- a/src/transformers/models/video_llava/processing_video_llava.py
+++ b/src/transformers/models/video_llava/processing_video_llava.py
@@ -152,7 +152,7 @@ class VideoLlavaProcessor(ProcessorMixin):
         if isinstance(text, str):
             text = [text]
         elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
 
         data = {}
         if images is not None:
diff --git a/src/transformers/models/vitpose/image_processing_vitpose.py b/src/transformers/models/vitpose/image_processing_vitpose.py
index 77cd0c92d7..32c048a8a9 100644
--- a/src/transformers/models/vitpose/image_processing_vitpose.py
+++ b/src/transformers/models/vitpose/image_processing_vitpose.py
@@ -133,7 +133,7 @@ def get_keypoint_predictions(heatmaps: np.ndarray) -> tuple[np.ndarray, np.ndarr
             Scores (confidence) of the keypoints.
     """
     if not isinstance(heatmaps, np.ndarray):
-        raise ValueError("Heatmaps should be np.ndarray")
+        raise TypeError("Heatmaps should be np.ndarray")
     if heatmaps.ndim != 4:
         raise ValueError("Heatmaps should be 4-dimensional")
 
diff --git a/src/transformers/pipelines/text2text_generation.py b/src/transformers/pipelines/text2text_generation.py
index c9333b760d..8952b58208 100644
--- a/src/transformers/pipelines/text2text_generation.py
+++ b/src/transformers/pipelines/text2text_generation.py
@@ -150,7 +150,7 @@ class Text2TextGenerationPipeline(Pipeline):
             args = (prefix + args[0],)
             padding = False
         else:
-            raise ValueError(
+            raise TypeError(
                 f" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"
             )
         inputs = self.tokenizer(*args, padding=padding, truncation=truncation, return_tensors=self.framework)
diff --git a/src/transformers/quantizers/auto.py b/src/transformers/quantizers/auto.py
index b332550b42..7cda893e3c 100644
--- a/src/transformers/quantizers/auto.py
+++ b/src/transformers/quantizers/auto.py
@@ -252,7 +252,7 @@ def register_quantization_config(method: str):
             raise ValueError(f"Config '{method}' already registered")
 
         if not issubclass(cls, QuantizationConfigMixin):
-            raise ValueError("Config must extend QuantizationConfigMixin")
+            raise TypeError("Config must extend QuantizationConfigMixin")
 
         AUTO_QUANTIZATION_CONFIG_MAPPING[method] = cls
         return cls
diff --git a/src/transformers/quantizers/quantizer_bnb_8bit.py b/src/transformers/quantizers/quantizer_bnb_8bit.py
index e1391426ec..e7d914170e 100644
--- a/src/transformers/quantizers/quantizer_bnb_8bit.py
+++ b/src/transformers/quantizers/quantizer_bnb_8bit.py
@@ -214,7 +214,7 @@ class Bnb8BitHfQuantizer(HfQuantizer):
         old_value = getattr(module, tensor_name)
 
         if not isinstance(module._parameters[tensor_name], bnb.nn.Int8Params):
-            raise ValueError(f"Parameter `{tensor_name}` should only be a `bnb.nn.Int8Params` instance.")
+            raise TypeError(f"Parameter `{tensor_name}` should only be a `bnb.nn.Int8Params` instance.")
         if (
             old_value.device == torch.device("meta")
             and target_device not in ["meta", torch.device("meta")]
diff --git a/src/transformers/tokenization_utils_base.py b/src/transformers/tokenization_utils_base.py
index 200bf478ed..51bcc9a321 100644
--- a/src/transformers/tokenization_utils_base.py
+++ b/src/transformers/tokenization_utils_base.py
@@ -2184,7 +2184,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
                     added_tokens_decoder[int(idx)] = token
                     added_tokens_map[str(token)] = token
                 else:
-                    raise ValueError(
+                    raise TypeError(
                         f"Found a {token.__class__} in the saved `added_tokens_decoder`, should be a dictionary or an AddedToken instance"
                     )
         else:
diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index cd0cc7d7da..6dc2a11436 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -701,7 +701,7 @@ class Trainer:
             os.makedirs(self.args.output_dir, exist_ok=True)
 
         if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
-            raise ValueError("The `data_collator` should be a simple callable (function, class with `__call__`).")
+            raise TypeError("The `data_collator` should be a simple callable (function, class with `__call__`).")
 
         if args.max_steps > 0 and args.num_train_epochs > 0:
             logger.info("max_steps is given, it will override any value given in num_train_epochs")
@@ -1346,7 +1346,7 @@ class Trainer:
                 raise ValueError(f"You need to define `optim_target_modules` to use {optimizer_name} optimizers")
 
             if not isinstance(args.optim_target_modules, (list, str)):
-                raise ValueError(
+                raise TypeError(
                     f"`optim_target_modules` must be a list of strings, a regex string, or 'all-linear'. Got: {args.optim_target_modules}"
                 )
 
diff --git a/src/transformers/utils/import_utils.py b/src/transformers/utils/import_utils.py
index 93c4d971ac..f56182d2a5 100644
--- a/src/transformers/utils/import_utils.py
+++ b/src/transformers/utils/import_utils.py
@@ -2383,7 +2383,7 @@ def requires(*, backends=()):
     """
 
     if not isinstance(backends, tuple):
-        raise ValueError("Backends should be a tuple.")
+        raise TypeError("Backends should be a tuple.")
 
     applied_backends = []
     for backend in backends:
diff --git a/src/transformers/utils/quantization_config.py b/src/transformers/utils/quantization_config.py
index 70c0b034a5..d6f4dd4663 100644
--- a/src/transformers/utils/quantization_config.py
+++ b/src/transformers/utils/quantization_config.py
@@ -1649,7 +1649,7 @@ class TorchAoConfig(QuantizationConfigMixin):
             from torchao.quantization.quant_api import AOBaseConfig
 
             if not isinstance(self.quant_type, AOBaseConfig):
-                raise ValueError(
+                raise TypeError(
                     f"quant_type must be either a string or an AOBaseConfig instance, got {type(self.quant_type)}"
                 )
         else:
diff --git a/src/transformers/video_utils.py b/src/transformers/video_utils.py
index 8282a25d08..6ac415eb35 100644
--- a/src/transformers/video_utils.py
+++ b/src/transformers/video_utils.py
@@ -629,7 +629,7 @@ def convert_to_rgb(
             The channel dimension format of the input video. If unset, will use the inferred format from the input.
     """
     if not isinstance(video, np.ndarray):
-        raise ValueError(f"Video has to be a numpy array to convert to RGB format, but found {type(video)}")
+        raise TypeError(f"Video has to be a numpy array to convert to RGB format, but found {type(video)}")
 
     # np.array usually comes with ChannelDimension.LAST so leet's convert it
     if input_data_format is None:
diff --git a/tests/models/clip/test_tokenization_clip.py b/tests/models/clip/test_tokenization_clip.py
index fca53f07cd..f1bd8dd353 100644
--- a/tests/models/clip/test_tokenization_clip.py
+++ b/tests/models/clip/test_tokenization_clip.py
@@ -174,7 +174,7 @@ class CLIPTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     def test_log_warning(self):
         # Test related to the breaking change introduced in transformers v4.17.0
         # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
-        with self.assertRaises(ValueError) as context:
+        with self.assertRaises(TypeError) as context:
             self.get_rust_tokenizer("robot-test/old-clip-tokenizer")
 
         self.assertTrue(
diff --git a/tests/models/grounding_dino/test_modeling_grounding_dino.py b/tests/models/grounding_dino/test_modeling_grounding_dino.py
index 953255797b..8d94a42100 100644
--- a/tests/models/grounding_dino/test_modeling_grounding_dino.py
+++ b/tests/models/grounding_dino/test_modeling_grounding_dino.py
@@ -61,7 +61,7 @@ def generate_fake_bounding_boxes(n_boxes):
     """Generate bounding boxes in the format (center_x, center_y, width, height)"""
     # Validate the input
     if not isinstance(n_boxes, int):
-        raise ValueError("n_boxes must be an integer")
+        raise TypeError("n_boxes must be an integer")
     if n_boxes <= 0:
         raise ValueError("n_boxes must be a positive integer")
 
diff --git a/tests/models/sam/test_processor_sam.py b/tests/models/sam/test_processor_sam.py
index 15cd2b0297..852df56f99 100644
--- a/tests/models/sam/test_processor_sam.py
+++ b/tests/models/sam/test_processor_sam.py
@@ -154,7 +154,7 @@ class SamProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
 
         dummy_masks = [[1, 0], [0, 1]]
-        with self.assertRaises(ValueError):
+        with self.assertRaises(TypeError):
             masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
 
     def test_rle_encoding(self):
diff --git a/tests/models/sam_hq/test_processor_samhq.py b/tests/models/sam_hq/test_processor_samhq.py
index 9f24cc7ab4..89a642ba60 100644
--- a/tests/models/sam_hq/test_processor_samhq.py
+++ b/tests/models/sam_hq/test_processor_samhq.py
@@ -163,5 +163,5 @@ class SamHQProcessorTest(ProcessorTesterMixin, unittest.TestCase):
         self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
 
         dummy_masks = [[1, 0], [0, 1]]
-        with self.assertRaises(ValueError):
+        with self.assertRaises(TypeError):
             masks = processor.post_process_masks(dummy_masks, np.array(original_sizes), np.array(reshaped_input_size))
diff --git a/tests/pipelines/test_pipelines_text2text_generation.py b/tests/pipelines/test_pipelines_text2text_generation.py
index 6a664e9aab..31a91ea848 100644
--- a/tests/pipelines/test_pipelines_text2text_generation.py
+++ b/tests/pipelines/test_pipelines_text2text_generation.py
@@ -81,7 +81,7 @@ class Text2TextGenerationPipelineTests(unittest.TestCase):
             ],
         )
 
-        with self.assertRaises(ValueError):
+        with self.assertRaises(TypeError):
             generator(4)
 
     @require_torch
diff --git a/tests/test_image_transforms.py b/tests/test_image_transforms.py
index c2f44120e2..313dc099f1 100644
--- a/tests/test_image_transforms.py
+++ b/tests/test_image_transforms.py
@@ -246,7 +246,7 @@ class ImageTransformsTester(unittest.TestCase):
 
         # Test that exception is raised if inputs are incorrect
         # Not a numpy array image
-        with self.assertRaises(ValueError):
+        with self.assertRaises(TypeError):
             normalize(5, 5, 5)
 
         # Number of mean values != number of channels