> For the complete documentation index, see [llms.txt](https://docs.zama.org/concrete-ml/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/concrete-ml/1.3/developer-guide/api/concrete.ml.common.utils.md).

# concrete.ml.common.utils.md

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## module `concrete.ml.common.utils`

Utils that can be re-used by other pieces of code in the module.

### **Global Variables**

* **SUPPORTED\_FLOAT\_TYPES**
* **SUPPORTED\_INT\_TYPES**
* **SUPPORTED\_TYPES**
* **MAX\_BITWIDTH\_BACKWARD\_COMPATIBLE**
* **USE\_OLD\_VL**
* **QUANT\_ROUND\_LIKE\_ROUND\_PBS**

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L95)

### function `replace_invalid_arg_name_chars`

```python
replace_invalid_arg_name_chars(arg_name: str) → str
```

Sanitize arg\_name, replacing invalid chars by \_.

This does not check that the starting character of arg\_name is valid.

**Args:**

* `arg_name` (str): the arg name to sanitize.

**Returns:**

* `str`: the sanitized arg name, with only chars in \_VALID\_ARG\_CHARS.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L114)

### function `generate_proxy_function`

```python
generate_proxy_function(
    function_to_proxy: Callable,
    desired_functions_arg_names: Iterable[str]
) → Tuple[Callable, Dict[str, str]]
```

Generate a proxy function for a function accepting only \*args type arguments.

This returns a runtime compiled function with the sanitized argument names passed in desired\_functions\_arg\_names as the arguments to the function.

**Args:**

* `function_to_proxy` (Callable): the function defined like def f(\*args) for which to return a function like f\_proxy(arg\_1, arg\_2) for any number of arguments.
* `desired_functions_arg_names` (Iterable\[str]): the argument names to use, these names are sanitized and the mapping between the original argument name to the sanitized one is returned in a dictionary. Only the sanitized names will work for a call to the proxy function.

**Returns:**

* `Tuple[Callable, Dict[str, str]]`: the proxy function and the mapping of the original arg name to the new and sanitized arg names.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L155)

### function `get_onnx_opset_version`

```python
get_onnx_opset_version(onnx_model: ModelProto) → int
```

Return the ONNX opset\_version.

**Args:**

* `onnx_model` (onnx.ModelProto): the model.

**Returns:**

* `int`: the version of the model

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L170)

### function `manage_parameters_for_pbs_errors`

```python
manage_parameters_for_pbs_errors(
    p_error: Optional[float] = None,
    global_p_error: Optional[float] = None
)
```

Return (p\_error, global\_p\_error) that we want to give to Concrete.

The returned (p\_error, global\_p\_error) depends on user's parameters and the way we want to manage defaults in Concrete ML, which may be different from the way defaults are managed in Concrete.

Principle: - if none are set, we set global\_p\_error to a default value of our choice - if both are set, we raise an error - if one is set, we use it and forward it to Concrete

Note that global\_p\_error is currently set to 0 in the FHE simulation mode.

**Args:**

* `p_error` (Optional\[float]): probability of error of a single PBS.
* `global_p_error` (Optional\[float]): probability of error of the full circuit.

**Returns:**

* `(p_error, global_p_error)`: parameters to give to the compiler

**Raises:**

* `ValueError`: if the two parameters are set (this is *not* as in Concrete-Python)

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L215)

### function `check_there_is_no_p_error_options_in_configuration`

```python
check_there_is_no_p_error_options_in_configuration(configuration)
```

Check the user did not set p\_error or global\_p\_error in configuration.

It would be dangerous, since we set them in direct arguments in our calls to Concrete-Python.

**Args:**

* `configuration`: Configuration object to use during compilation

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L236)

### function `get_model_class`

```python
get_model_class(model_class)
```

Return the class of the model (instantiated or not), which can be a partial() instance.

**Args:**

* `model_class`: The model, which can be a partial() instance.

**Returns:** The model's class.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L258)

### function `is_model_class_in_a_list`

```python
is_model_class_in_a_list(model_class, a_list)
```

Indicate if a model class, which can be a partial() instance, is an element of a\_list.

**Args:**

* `model_class`: The model, which can be a partial() instance.
* `a_list`: The list in which to look into.

**Returns:** If the model's class is in the list or not.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L272)

### function `get_model_name`

```python
get_model_name(model_class)
```

Return the name of the model, which can be a partial() instance.

**Args:**

* `model_class`: The model, which can be a partial() instance.

**Returns:** the model's name.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L285)

### function `is_classifier_or_partial_classifier`

```python
is_classifier_or_partial_classifier(model_class)
```

Indicate if the model class represents a classifier.

**Args:**

* `model_class`: The model class, which can be a functool's `partial` class.

**Returns:**

* `bool`: If the model class represents a classifier.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L297)

### function `is_regressor_or_partial_regressor`

```python
is_regressor_or_partial_regressor(model_class)
```

Indicate if the model class represents a regressor.

**Args:**

* `model_class`: The model class, which can be a functool's `partial` class.

**Returns:**

* `bool`: If the model class represents a regressor.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L309)

### function `is_pandas_dataframe`

```python
is_pandas_dataframe(input_container: Any) → bool
```

Indicate if the input container is a Pandas DataFrame.

This function is inspired from Scikit-Learn's test validation tools and avoids the need to add and import Pandas as an additional dependency to the project. See <https://github.com/scikit-learn/scikit-learn/blob/98cf537f5/sklearn/utils/validation.py#L629>

**Args:**

* `input_container` (Any): The input container to consider

**Returns:**

* `bool`: If the input container is a DataFrame

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L325)

### function `is_pandas_series`

```python
is_pandas_series(input_container: Any) → bool
```

Indicate if the input container is a Pandas Series.

This function is inspired from Scikit-Learn's test validation tools and avoids the need to add and import Pandas as an additional dependency to the project. See <https://github.com/scikit-learn/scikit-learn/blob/98cf537f5/sklearn/utils/validation.py#L629>

**Args:**

* `input_container` (Any): The input container to consider

**Returns:**

* `bool`: If the input container is a Series

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L341)

### function `is_pandas_type`

```python
is_pandas_type(input_container: Any) → bool
```

Indicate if the input container is a Pandas DataFrame or Series.

**Args:**

* `input_container` (Any): The input container to consider

**Returns:**

* `bool`: If the input container is a DataFrame orSeries

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L436)

### function `check_dtype_and_cast`

```python
check_dtype_and_cast(
    values: Any,
    expected_dtype: str,
    error_information: Optional[str] = ''
)
```

Convert any allowed type into an array and cast it if required.

If values types don't match with any supported type or the expected dtype, raise a ValueError.

**Args:**

* `values` (Any): The values to consider
* `expected_dtype` (str): The expected dtype, either "float32" or "int64"
* `error_information` (str): Additional information to put in front of the error message when raising a ValueError. Default to None.

**Returns:**

* `(Union[numpy.ndarray, torch.utils.data.dataset.Subset])`: The values with proper dtype.

**Raises:**

* `ValueError`: If the values' dtype don't match the expected one or casting is not possible.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L488)

### function `compute_bits_precision`

```python
compute_bits_precision(x: ndarray) → int
```

Compute the number of bits required to represent x.

**Args:**

* `x` (numpy.ndarray): Integer data

**Returns:**

* `int`: the number of bits required to represent x

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L500)

### function `is_brevitas_model`

```python
is_brevitas_model(model: Module) → bool
```

Check if a model is a Brevitas type.

**Args:**

* `model`: PyTorch model.

**Returns:**

* `bool`: True if `model` is a Brevitas network.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L518)

### function `to_tuple`

```python
to_tuple(x: Any) → tuple
```

Make the input a tuple if it is not already the case.

**Args:**

* `x` (Any): The input to consider. It can already be an input.

**Returns:**

* `tuple`: The input as a tuple.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L534)

### function `all_values_are_integers`

```python
all_values_are_integers(*values: Any) → bool
```

Indicate if all unpacked values are of a supported integer dtype.

**Args:**

* `*values (Any)`: The values to consider.

**Returns:**

* `bool`: Whether all values are supported integers or not.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L547)

### function `all_values_are_floats`

```python
all_values_are_floats(*values: Any) → bool
```

Indicate if all unpacked values are of a supported float dtype.

**Args:**

* `*values (Any)`: The values to consider.

**Returns:**

* `bool`: Whether all values are supported floating points or not.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L560)

### function `all_values_are_of_dtype`

```python
all_values_are_of_dtype(*values: Any, dtypes: Union[str, List[str]]) → bool
```

Indicate if all unpacked values are of the specified dtype(s).

**Args:**

* `*values (Any)`: The values to consider.
* `dtypes` (Union\[str, List\[str]]): The dtype(s) to consider.

**Returns:**

* `bool`: Whether all values are of the specified dtype(s) or not.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L588)

### function `array_allclose_and_same_shape`

```python
array_allclose_and_same_shape(
    a,
    b,
    rtol: float = 1e-05,
    atol: float = 1e-08,
    equal_nan: bool = False
) → bool
```

Check if two numpy arrays are equal within a tolerances and have the same shape.

**Args:**

* `a` (numpy.ndarray): The first input array
* `b` (numpy.ndarray): The second input array
* `rtol` (float): The relative tolerance parameter
* `atol` (float): The absolute tolerance parameter
* `equal_nan` (bool): Whether to compare NaN’s as equal. If True, NaN’s in a will be considered equal to NaN’s in b in the output array

**Returns:**

* `bool`: True if the arrays have the same shape and all elements are equal within the specified tolerances, False otherwise.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.3.x/src/concrete/ml/common/utils.py#L52)

### class `FheMode`

Enum representing the execution mode.

This enum inherits from str in order to be able to easily compare a string parameter to its equivalent Enum attribute.

**Examples:** fhe\_disable = FheMode.DISABLE

`fhe_disable == "disable"` True

```
 >>> fhe_disable == "execute"
 False

 >>> FheMode.is_valid("simulate")
 True

 >>> FheMode.is_valid(FheMode.EXECUTE)
 True

 >>> FheMode.is_valid("predict_in_fhe")
 False
```


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