[CI] Fix ci (#21940)

* fix `get_proposal_pos_embed`

* fix order

* style

* zero shot simplify test

* add approximate values for zero shot audio classification

src/transformers/models/deformable_detr/modeling_deformable_detr.py

@@ -497,7 +497,7 @@ class DeformableDetrSinePositionEmbedding(nn.Module):
             x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale
 
         dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device)
-        dim_t = self.temperature ** (2 * torch_int_div(dim_t, 2 / self.embedding_dim))
+        dim_t = self.temperature ** (2 * torch_int_div(dim_t, 2) / self.embedding_dim)
 
         pos_x = x_embed[:, :, :, None] / dim_t
         pos_y = y_embed[:, :, :, None] / dim_t
@@ -1552,7 +1552,7 @@ class DeformableDetrModel(DeformableDetrPreTrainedModel):
         scale = 2 * math.pi
 
         dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)
-        dim_t = temperature ** (2 * torch.div(dim_t, 2) / num_pos_feats)
+        dim_t = temperature ** (2 * torch_int_div(dim_t, 2) / num_pos_feats)
         # batch_size, num_queries, 4
         proposals = proposals.sigmoid() * scale
         # batch_size, num_queries, 4, 128

src/transformers/models/deta/modeling_deta.py

@@ -399,7 +399,7 @@ class DetaSinePositionEmbedding(nn.Module):
             x_embed = (x_embed - 0.5) / (x_embed[:, :, -1:] + eps) * self.scale
 
         dim_t = torch.arange(self.embedding_dim, dtype=torch.float32, device=pixel_values.device)
-        dim_t = self.temperature ** (2 * torch_int_div(dim_t, 2 / self.embedding_dim))
+        dim_t = self.temperature ** (2 * torch_int_div(dim_t, 2) / self.embedding_dim)
 
         pos_x = x_embed[:, :, :, None] / dim_t
         pos_y = y_embed[:, :, :, None] / dim_t
@@ -1463,7 +1463,7 @@ class DetaModel(DetaPreTrainedModel):
         scale = 2 * math.pi
 
         dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)
-        dim_t = temperature ** (2 * torch.div(dim_t, 2) / num_pos_feats)
+        dim_t = temperature ** (2 * torch_int_div(dim_t, 2) / num_pos_feats)
         # batch_size, num_queries, 4
         proposals = proposals.sigmoid() * scale
         # batch_size, num_queries, 4, 128

src/transformers/pipelines/zero_shot_audio_classification.py

@@ -44,7 +44,7 @@ class ZeroShotAudioClassificationPipeline(Pipeline):
     >>> audio = next(iter(dataset["train"]["audio"]))["array"]
     >>> classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-unfused")
     >>> classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
-    [{'score': 0.9995999932289124, 'label': 'Sound of a dog'}, {'score': 0.00040007088682614267, 'label': 'Sound of vaccum cleaner'}]
+    [{'score': 0.9996, 'label': 'Sound of a dog'}, {'score': 0.0004, 'label': 'Sound of vaccum cleaner'}]
     ```