[Preserve dtype of array when converting to torch (#1349)

We have noticing the following error with a recent version of outlines
when used with MLX:
```
TypeError: argument 'token_id': 'float' object cannot be interpreted as an integer

At:
  /.../outlines_core/fsm/guide.py(294): get_next_state
  /.../outlines/processors/structured.py(101): process_logits
  /.../outlines/processors/base_logits_processor.py(90): __call__
```

The issue is that the MLX array of tokens, which are integers, are being
force-converted to floats, even though outlines expects an integer
array. This is because all MLX arrays are being converted to `float32`,
even when it's not necessarily appropriate, like in this case. Looking
at the [commented
link](https://ml-explore.github.io/mlx/build/html/usage/numpy.html#pytorch),
the advice was to convert to `float32` only for `bfloat16`, because
numpy does not support `bfloat16`. Now the MLX `_to_torch`
implementation matches the other array libraries, none of the other
libraries are being force-casted to float

outlines/processors/base_logits_processor.py

@@ -110,9 +110,9 @@ def _to_torch(tensor_like: Array) -> torch.Tensor:
             import mlx.core as mx
 
             # https://ml-explore.github.io/mlx/build/html/usage/numpy.html#pytorch
-            return torch.from_dlpack(
-                np.array(tensor_like.astype(mx.float32), copy=False)
-            )
+            if tensor_like.dtype == mx.bfloat16:
+                tensor_like = tensor_like.astype(mx.float32)
+            return torch.from_dlpack(np.array(tensor_like, copy=False))
 
         elif is_jax_array_type(type(tensor_like)):
             import jax

tests/processors/test_base_processor.py

@@ -18,6 +18,7 @@
     import mlx.core as mx
 
     arrays["mlx"] = mx.array([[1, 2], [3, 4]], dtype=mx.float32)
+    arrays["mlx_bfloat16"] = mx.array([[1, 2], [3, 4]], dtype=mx.bfloat16)
 except ImportError:
     pass
 
@@ -59,7 +60,12 @@ def test_from_torch(array_type, processor):
     torch_tensor = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32)
     data = processor._from_torch(torch_tensor, type(arrays[array_type]))
     assert isinstance(data, type(arrays[array_type]))
-    assert np.allclose(data, arrays[array_type])
+    if array_type == "mlx_bfloat16":
+        # For bfloat16, we expect the output to be float32 due to the conversion
+        assert data.dtype == mx.float32
+        assert np.allclose(np.array(data), np.array([[1, 2], [3, 4]], dtype=np.float32))
+    else:
+        assert np.allclose(data, arrays[array_type])
 
 
 @pytest.mark.parametrize("array_type", arrays.keys())