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// Licensed to the Apache Software Foundation (ASF) under one

// or more contributor license agreements. See the NOTICE file

// distributed with this work for additional information

// regarding copyright ownership. The ASF licenses this file

// to you under the Apache License, Version 2.0 (the

// "License"); you may not use this file except in compliance

// with the License. You may obtain a copy of the License at

//

// http://www.apache.org/licenses/LICENSE-2.0

//

// Unless required by applicable law or agreed to in writing,

// software distributed under the License is distributed on an

// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY

// KIND, either express or implied. See the License for the

// specific language governing permissions and limitations

// under the License.

use arrow::datatypes::FieldRef;

use pyo3::prelude::*;

use crate::common::data_type::PyDataType;

#[pyclass(frozen, name = "ScalarVariable", module = "datafusion.expr", subclass)]

#[derive(Clone)]

pub struct PyScalarVariable {

field: FieldRef,

variables: Vec<String>,

}

impl PyScalarVariable {

pub fn new(field: &FieldRef, variables: &[String]) -> Self {

Self {

field: field.to_owned(),

variables: variables.to_vec(),

}

}

}

#[pymethods]

impl PyScalarVariable {

/// Get the data type

fn data_type(&self) -> PyResult<PyDataType> {

Ok(self.field.data_type().clone().into())

}

fn variables(&self) -> PyResult<Vec<String>> {

Ok(self.variables.clone())

}

fn __repr__(&self) -> PyResult<String> {

Ok(format!("{}{:?}", self.field.data_type(), self.variables))

}

}