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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.

//! [`ScalarUDFImpl`] definitions for flatten function.

use crate::utils::make_scalar_function;
use arrow_array::{ArrayRef, GenericListArray, OffsetSizeTrait};
use arrow_buffer::OffsetBuffer;
use arrow_schema::DataType;
use arrow_schema::DataType::{FixedSizeList, LargeList, List, Null};
use datafusion_common::cast::{
    as_generic_list_array, as_large_list_array, as_list_array,
};
use datafusion_common::{exec_err, Result};
use datafusion_expr::{ColumnarValue, ScalarUDFImpl, Signature, Volatility};
use std::any::Any;
use std::sync::Arc;

make_udf_expr_and_func!(
    Flatten,
    flatten,
    array,
    "flattens an array of arrays into a single array.",
    flatten_udf
);

#[derive(Debug)]
pub(super) struct Flatten {
    signature: Signature,
    aliases: Vec<String>,
}
impl Flatten {
    pub fn new() -> Self {
        Self {
            signature: Signature::array(Volatility::Immutable),
            aliases: vec![],
        }
    }
}

impl ScalarUDFImpl for Flatten {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn name(&self) -> &str {
        "flatten"
    }

    fn signature(&self) -> &Signature {
        &self.signature
    }

    fn return_type(&self, arg_types: &[DataType]) -> Result<DataType> {
        fn get_base_type(data_type: &DataType) -> Result<DataType> {
            match data_type {
                List(field) | FixedSizeList(field, _)
                    if matches!(field.data_type(), List(_) | FixedSizeList(_, _)) =>
                {
                    get_base_type(field.data_type())
                }
                LargeList(field) if matches!(field.data_type(), LargeList(_)) => {
                    get_base_type(field.data_type())
                }
                Null | List(_) | LargeList(_) => Ok(data_type.to_owned()),
                FixedSizeList(field, _) => Ok(List(Arc::clone(field))),
                _ => exec_err!(
                    "Not reachable, data_type should be List, LargeList or FixedSizeList"
                ),
            }
        }

        let data_type = get_base_type(&arg_types[0])?;
        Ok(data_type)
    }

    fn invoke(&self, args: &[ColumnarValue]) -> Result<ColumnarValue> {
        make_scalar_function(flatten_inner)(args)
    }

    fn aliases(&self) -> &[String] {
        &self.aliases
    }
}

/// Flatten SQL function
pub fn flatten_inner(args: &[ArrayRef]) -> Result<ArrayRef> {
    if args.len() != 1 {
        return exec_err!("flatten expects one argument");
    }

    let array_type = args[0].data_type();
    match array_type {
        List(_) => {
            let list_arr = as_list_array(&args[0])?;
            let flattened_array = flatten_internal::<i32>(list_arr.clone(), None)?;
            Ok(Arc::new(flattened_array) as ArrayRef)
        }
        LargeList(_) => {
            let list_arr = as_large_list_array(&args[0])?;
            let flattened_array = flatten_internal::<i64>(list_arr.clone(), None)?;
            Ok(Arc::new(flattened_array) as ArrayRef)
        }
        Null => Ok(Arc::clone(&args[0])),
        _ => {
            exec_err!("flatten does not support type '{array_type:?}'")
        }
    }
}

fn flatten_internal<O: OffsetSizeTrait>(
    list_arr: GenericListArray<O>,
    indexes: Option<OffsetBuffer<O>>,
) -> Result<GenericListArray<O>> {
    let (field, offsets, values, _) = list_arr.clone().into_parts();
    let data_type = field.data_type();

    match data_type {
        // Recursively get the base offsets for flattened array
        List(_) | LargeList(_) => {
            let sub_list = as_generic_list_array::<O>(&values)?;
            if let Some(indexes) = indexes {
                let offsets = get_offsets_for_flatten(offsets, indexes);
                flatten_internal::<O>(sub_list.clone(), Some(offsets))
            } else {
                flatten_internal::<O>(sub_list.clone(), Some(offsets))
            }
        }
        // Reach the base level, create a new list array
        _ => {
            if let Some(indexes) = indexes {
                let offsets = get_offsets_for_flatten(offsets, indexes);
                let list_arr = GenericListArray::<O>::new(field, offsets, values, None);
                Ok(list_arr)
            } else {
                Ok(list_arr.clone())
            }
        }
    }
}

// Create new offsets that are equivalent to `flatten` the array.
fn get_offsets_for_flatten<O: OffsetSizeTrait>(
    offsets: OffsetBuffer<O>,
    indexes: OffsetBuffer<O>,
) -> OffsetBuffer<O> {
    let buffer = offsets.into_inner();
    let offsets: Vec<O> = indexes
        .iter()
        .map(|i| buffer[i.to_usize().unwrap()])
        .collect();
    OffsetBuffer::new(offsets.into())
}