// 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::{DataType, Schema};

use arrow::record_batch::RecordBatch;

use datafusion_common::Result;

use datafusion_expr::ColumnarValue;
use std::fmt::{Debug, Display};

use arrow::array::{make_array, Array, ArrayRef, BooleanArray, MutableArrayData};
use arrow::compute::{and_kleene, filter_record_batch, is_not_null, SlicesIterator};
use std::any::Any;

/// Expression that can be evaluated against a RecordBatch
/// A Physical expression knows its type, nullability and how to evaluate itself.
pub trait PhysicalExpr: Send + Sync + Display + Debug {
    /// Returns the physical expression as [`Any`](std::any::Any) so that it can be
    /// downcast to a specific implementation.
    fn as_any(&self) -> &dyn Any;
    /// Get the data type of this expression, given the schema of the input
    fn data_type(&self, input_schema: &Schema) -> Result<DataType>;
    /// Determine whether this expression is nullable, given the schema of the input
    fn nullable(&self, input_schema: &Schema) -> Result<bool>;
    /// Evaluate an expression against a RecordBatch
    fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue>;
    /// Evaluate an expression against a RecordBatch after first applying a
    /// validity array
    fn evaluate_selection(
        &self,
        batch: &RecordBatch,
        selection: &BooleanArray,
    ) -> Result<ColumnarValue> {
        let tmp_batch = filter_record_batch(batch, selection)?;

        let tmp_result = self.evaluate(&tmp_batch)?;
        // All values from the `selection` filter are true.
        if batch.num_rows() == tmp_batch.num_rows() {
            return Ok(tmp_result);
        }
        if let ColumnarValue::Array(a) = tmp_result {
            let result = scatter(selection, a.as_ref())?;
            Ok(ColumnarValue::Array(result))
        } else {
            Ok(tmp_result)
        }
    }
}

/// Scatter `truthy` array by boolean mask. When the mask evaluates `true`, next values of `truthy`
/// are taken, when the mask evaluates `false` values null values are filled.
///
/// # Arguments
/// * `mask` - Boolean values used to determine where to put the `truthy` values
/// * `truthy` - All values of this array are to scatter according to `mask` into final result.
fn scatter(mask: &BooleanArray, truthy: &dyn Array) -> Result<ArrayRef> {
    let truthy = truthy.data();

    // update the mask so that any null values become false
    // (SlicesIterator doesn't respect nulls)
    let mask = and_kleene(mask, &is_not_null(mask)?)?;

    let mut mutable = MutableArrayData::new(vec![truthy], true, mask.len());

    // the SlicesIterator slices only the true values. So the gaps left by this iterator we need to
    // fill with falsy values

    // keep track of how much is filled
    let mut filled = 0;
    // keep track of current position we have in truthy array
    let mut true_pos = 0;

    SlicesIterator::new(&mask).for_each(|(start, end)| {
        // the gap needs to be filled with nulls
        if start > filled {
            mutable.extend_nulls(start - filled);
        }
        // fill with truthy values
        let len = end - start;
        mutable.extend(0, true_pos, true_pos + len);
        true_pos += len;
        filled = end;
    });
    // the remaining part is falsy
    if filled < mask.len() {
        mutable.extend_nulls(mask.len() - filled);
    }

    let data = mutable.freeze();
    Ok(make_array(data))
}

#[cfg(test)]
mod tests {
    use std::sync::Arc;

    use super::*;
    use arrow::array::Int32Array;
    use datafusion_common::Result;

    #[test]
    fn scatter_int() -> Result<()> {
        let truthy = Arc::new(Int32Array::from(vec![1, 10, 11, 100]));
        let mask = BooleanArray::from(vec![true, true, false, false, true]);

        // the output array is expected to be the same length as the mask array
        let expected =
            Int32Array::from_iter(vec![Some(1), Some(10), None, None, Some(11)]);
        let result = scatter(&mask, truthy.as_ref())?;
        let result = result.as_any().downcast_ref::<Int32Array>().unwrap();

        assert_eq!(&expected, result);
        Ok(())
    }

    #[test]
    fn scatter_int_end_with_false() -> Result<()> {
        let truthy = Arc::new(Int32Array::from(vec![1, 10, 11, 100]));
        let mask = BooleanArray::from(vec![true, false, true, false, false, false]);

        // output should be same length as mask
        let expected =
            Int32Array::from_iter(vec![Some(1), None, Some(10), None, None, None]);
        let result = scatter(&mask, truthy.as_ref())?;
        let result = result.as_any().downcast_ref::<Int32Array>().unwrap();

        assert_eq!(&expected, result);
        Ok(())
    }

    #[test]
    fn scatter_with_null_mask() -> Result<()> {
        let truthy = Arc::new(Int32Array::from(vec![1, 10, 11]));
        let mask: BooleanArray = vec![Some(false), None, Some(true), Some(true), None]
            .into_iter()
            .collect();

        // output should treat nulls as though they are false
        let expected = Int32Array::from_iter(vec![None, None, Some(1), Some(10), None]);
        let result = scatter(&mask, truthy.as_ref())?;
        let result = result.as_any().downcast_ref::<Int32Array>().unwrap();

        assert_eq!(&expected, result);
        Ok(())
    }

    #[test]
    fn scatter_boolean() -> Result<()> {
        let truthy = Arc::new(BooleanArray::from(vec![false, false, false, true]));
        let mask = BooleanArray::from(vec![true, true, false, false, true]);

        // the output array is expected to be the same length as the mask array
        let expected = BooleanArray::from_iter(vec![
            Some(false),
            Some(false),
            None,
            None,
            Some(false),
        ]);
        let result = scatter(&mask, truthy.as_ref())?;
        let result = result.as_any().downcast_ref::<BooleanArray>().unwrap();

        assert_eq!(&expected, result);
        Ok(())
    }
}