Fix issue probably-meant-fstring found at https://codereview.doctor (#16913)

examples/research_projects/onnx/summarization/bart_onnx/generation_onnx.py

@@ -639,7 +639,7 @@ class BARTBeamSearchGenerator(BARTGenerator):
 
         assert (
             num_beams * batch_size == batch_beam_size
-        ), "Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
+        ), 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

src/transformers/configuration_utils.py

@@ -634,7 +634,7 @@ class PretrainedConfig(PushToHubMixin):
             raise EnvironmentError(
                 f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it in the cached "
                 f"files and it looks like {pretrained_model_name_or_path} is not the path to a directory containing a "
-                "{configuration_file} file.\nCheckout your internet connection or see how to run the library in "
+                f"{configuration_file} file.\nCheckout your internet connection or see how to run the library in "
                 "offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'."
             )
         except EnvironmentError:

src/transformers/models/auto/feature_extraction_auto.py

@@ -311,7 +311,7 @@ class AutoFeatureExtractor:
         raise ValueError(
             f"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
             f"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
-            "`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}"
+            f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}"
         )
 
     @staticmethod

src/transformers/models/bart/modeling_bart.py

@@ -1050,7 +1050,7 @@ class BartDecoder(BartPretrainedModel):
             if attn_mask is not None:
                 if attn_mask.size()[0] != (len(self.layers)):
                     raise ValueError(
-                        "The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
+                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
                     )
 
         for idx, decoder_layer in enumerate(self.layers):

src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py

@@ -129,7 +129,7 @@ def load_tf2_weights_in_bert(model, tf_checkpoint_path, config):
                     trace.append("token_type_embeddings")
                     pointer = getattr(pointer, "token_type_embeddings")
                 else:
-                    raise ValueError("Unknown embedding layer with name {full_name}")
+                    raise ValueError(f"Unknown embedding layer with name {full_name}")
                 trace.append("weight")
                 pointer = getattr(pointer, "weight")
             elif m_name == "_attention_layer":

src/transformers/models/plbart/modeling_plbart.py

@@ -1022,7 +1022,7 @@ class PLBartDecoder(PLBartPreTrainedModel):
             if attn_mask is not None:
                 if attn_mask.size()[0] != (len(self.layers)):
                     raise ValueError(
-                        "The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
+                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
                     )
 
         for idx, decoder_layer in enumerate(self.layers):

src/transformers/models/prophetnet/modeling_prophetnet.py

@@ -757,7 +757,7 @@ class ProphetNetAttention(nn.Module):
             batch_size * self.num_attn_heads,
             tgt_len,
             self.head_dim,
-        ), "`attn_output` should be of shape {batch_size * self.num_attn_heads, tgt_len, self.head_dim}, but is of shape {attn_output.size()}"
+        ), f"`attn_output` should be of shape {batch_size * self.num_attn_heads, tgt_len, self.head_dim}, but is of shape {attn_output.size()}"
 
         attn_output = (
             attn_output.view(batch_size, self.num_attn_heads, tgt_len, self.head_dim)

src/transformers/models/xglm/modeling_flax_xglm.py

@@ -153,7 +153,7 @@ class FlaxXGLMAttention(nn.Module):
         if self.head_dim * self.num_heads != self.embed_dim:
             raise ValueError(
                 f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} "
-                "and `num_heads`: {self.num_heads})."
+                f"and `num_heads`: {self.num_heads})."
             )
 
         dense = partial(

src/transformers/pipelines/audio_utils.py

@@ -55,7 +55,7 @@ def ffmpeg_microphone(
     elif format_for_conversion == "f32le":
         size_of_sample = 4
     else:
-        raise ValueError("Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`")
+        raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`")
 
     system = platform.system()
     if system == "Linux":
@@ -144,7 +144,7 @@ def ffmpeg_microphone_live(
         dtype = np.float32
         size_of_sample = 4
     else:
-        raise ValueError("Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`")
+        raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`")
 
     if stride_length_s is None:
         stride_length_s = chunk_length_s / 6

src/transformers/utils/hub.py

@@ -748,7 +748,7 @@ def has_file(
         logger.error(e)
         raise EnvironmentError(
             f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for this "
-            "model name. Check the model page at 'https://huggingface.co/{path_or_repo}' for available revisions."
+            f"model name. Check the model page at 'https://huggingface.co/{path_or_repo}' for available revisions."
         )
     except requests.HTTPError:
         # We return false for EntryNotFoundError (logical) as well as any connection error.

tests/extended/test_trainer_ext.py

@@ -266,7 +266,7 @@ class TestTrainerExt(TestCasePlus):
         )
 
         self.assertEqual(
-            loss_orig, loss_bnb, "loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"
+            loss_orig, loss_bnb, f"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"
         )
 
         # Additionally let's test that the absolute gpu memory difference is larger or about the