// 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.

//! Defines physical expressions which specify ordering requirement
//! that can evaluated at runtime during query execution

use std::any::Any;
use std::collections::VecDeque;
use std::fmt::Debug;
use std::sync::Arc;

use crate::aggregate::utils::{down_cast_any_ref, ordering_fields};
use crate::expressions::format_state_name;
use crate::{
    reverse_order_bys, AggregateExpr, LexOrdering, PhysicalExpr, PhysicalSortExpr,
};

use arrow::datatypes::{DataType, Field};
use arrow_array::cast::AsArray;
use arrow_array::{new_empty_array, Array, ArrayRef, StructArray};
use arrow_schema::Fields;
use datafusion_common::utils::{array_into_list_array, get_row_at_idx};
use datafusion_common::{exec_err, Result, ScalarValue};
use datafusion_expr::utils::AggregateOrderSensitivity;
use datafusion_expr::Accumulator;
use datafusion_physical_expr_common::aggregate::merge_arrays::merge_ordered_arrays;

/// Expression for a `ARRAY_AGG(... ORDER BY ..., ...)` aggregation. In a multi
/// partition setting, partial aggregations are computed for every partition,
/// and then their results are merged.
#[derive(Debug)]
pub struct OrderSensitiveArrayAgg {
    /// Column name
    name: String,
    /// The `DataType` for the input expression
    input_data_type: DataType,
    /// The input expression
    expr: Arc<dyn PhysicalExpr>,
    /// If the input expression can have `NULL`s
    nullable: bool,
    /// Ordering data types
    order_by_data_types: Vec<DataType>,
    /// Ordering requirement
    ordering_req: LexOrdering,
    /// Whether the aggregation is running in reverse
    reverse: bool,
}

impl OrderSensitiveArrayAgg {
    /// Create a new `OrderSensitiveArrayAgg` aggregate function
    pub fn new(
        expr: Arc<dyn PhysicalExpr>,
        name: impl Into<String>,
        input_data_type: DataType,
        nullable: bool,
        order_by_data_types: Vec<DataType>,
        ordering_req: LexOrdering,
    ) -> Self {
        Self {
            name: name.into(),
            input_data_type,
            expr,
            nullable,
            order_by_data_types,
            ordering_req,
            reverse: false,
        }
    }
}

impl AggregateExpr for OrderSensitiveArrayAgg {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn field(&self) -> Result<Field> {
        Ok(Field::new_list(
            &self.name,
            // This should be the same as return type of AggregateFunction::ArrayAgg
            Field::new("item", self.input_data_type.clone(), self.nullable),
            false,
        ))
    }

    fn create_accumulator(&self) -> Result<Box<dyn Accumulator>> {
        OrderSensitiveArrayAggAccumulator::try_new(
            &self.input_data_type,
            &self.order_by_data_types,
            self.ordering_req.clone(),
            self.reverse,
            self.nullable,
        )
        .map(|acc| Box::new(acc) as _)
    }

    fn state_fields(&self) -> Result<Vec<Field>> {
        let mut fields = vec![Field::new_list(
            format_state_name(&self.name, "array_agg"),
            Field::new("item", self.input_data_type.clone(), self.nullable),
            false, // This should be the same as field()
        )];
        let orderings = ordering_fields(&self.ordering_req, &self.order_by_data_types);
        fields.push(Field::new_list(
            format_state_name(&self.name, "array_agg_orderings"),
            Field::new(
                "item",
                DataType::Struct(Fields::from(orderings)),
                self.nullable,
            ),
            false,
        ));
        Ok(fields)
    }

    fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
        vec![Arc::clone(&self.expr)]
    }

    fn order_bys(&self) -> Option<&[PhysicalSortExpr]> {
        (!self.ordering_req.is_empty()).then_some(&self.ordering_req)
    }

    fn order_sensitivity(&self) -> AggregateOrderSensitivity {
        AggregateOrderSensitivity::HardRequirement
    }

    fn name(&self) -> &str {
        &self.name
    }

    fn reverse_expr(&self) -> Option<Arc<dyn AggregateExpr>> {
        Some(Arc::new(Self {
            name: self.name.to_string(),
            input_data_type: self.input_data_type.clone(),
            expr: Arc::clone(&self.expr),
            nullable: self.nullable,
            order_by_data_types: self.order_by_data_types.clone(),
            // Reverse requirement:
            ordering_req: reverse_order_bys(&self.ordering_req),
            reverse: !self.reverse,
        }))
    }
}

impl PartialEq<dyn Any> for OrderSensitiveArrayAgg {
    fn eq(&self, other: &dyn Any) -> bool {
        down_cast_any_ref(other)
            .downcast_ref::<Self>()
            .map(|x| {
                self.name == x.name
                    && self.input_data_type == x.input_data_type
                    && self.order_by_data_types == x.order_by_data_types
                    && self.expr.eq(&x.expr)
            })
            .unwrap_or(false)
    }
}

#[derive(Debug)]
pub(crate) struct OrderSensitiveArrayAggAccumulator {
    /// Stores entries in the `ARRAY_AGG` result.
    values: Vec<ScalarValue>,
    /// Stores values of ordering requirement expressions corresponding to each
    /// entry in `values`. This information is used when merging results from
    /// different partitions. For detailed information how merging is done, see
    /// [`merge_ordered_arrays`].
    ordering_values: Vec<Vec<ScalarValue>>,
    /// Stores datatypes of expressions inside values and ordering requirement
    /// expressions.
    datatypes: Vec<DataType>,
    /// Stores the ordering requirement of the `Accumulator`.
    ordering_req: LexOrdering,
    /// Whether the aggregation is running in reverse.
    reverse: bool,
    /// Whether the input expr is nullable
    nullable: bool,
}

impl OrderSensitiveArrayAggAccumulator {
    /// Create a new order-sensitive ARRAY_AGG accumulator based on the given
    /// item data type.
    pub fn try_new(
        datatype: &DataType,
        ordering_dtypes: &[DataType],
        ordering_req: LexOrdering,
        reverse: bool,
        nullable: bool,
    ) -> Result<Self> {
        let mut datatypes = vec![datatype.clone()];
        datatypes.extend(ordering_dtypes.iter().cloned());
        Ok(Self {
            values: vec![],
            ordering_values: vec![],
            datatypes,
            ordering_req,
            reverse,
            nullable,
        })
    }
}

impl Accumulator for OrderSensitiveArrayAggAccumulator {
    fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
        if values.is_empty() {
            return Ok(());
        }

        let n_row = values[0].len();
        for index in 0..n_row {
            let row = get_row_at_idx(values, index)?;
            self.values.push(row[0].clone());
            self.ordering_values.push(row[1..].to_vec());
        }

        Ok(())
    }

    fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
        if states.is_empty() {
            return Ok(());
        }

        // First entry in the state is the aggregation result. Second entry
        // stores values received for ordering requirement columns for each
        // aggregation value inside `ARRAY_AGG` list. For each `StructArray`
        // inside `ARRAY_AGG` list, we will receive an `Array` that stores values
        // received from its ordering requirement expression. (This information
        // is necessary for during merging).
        let [array_agg_values, agg_orderings, ..] = &states else {
            return exec_err!("State should have two elements");
        };
        let Some(agg_orderings) = agg_orderings.as_list_opt::<i32>() else {
            return exec_err!("Expects to receive a list array");
        };

        // Stores ARRAY_AGG results coming from each partition
        let mut partition_values = vec![];
        // Stores ordering requirement expression results coming from each partition
        let mut partition_ordering_values = vec![];

        // Existing values should be merged also.
        partition_values.push(self.values.clone().into());
        partition_ordering_values.push(self.ordering_values.clone().into());

        // Convert array to Scalars to sort them easily. Convert back to array at evaluation.
        let array_agg_res = ScalarValue::convert_array_to_scalar_vec(array_agg_values)?;
        for v in array_agg_res.into_iter() {
            partition_values.push(v.into());
        }

        let orderings = ScalarValue::convert_array_to_scalar_vec(agg_orderings)?;

        for partition_ordering_rows in orderings.into_iter() {
            // Extract value from struct to ordering_rows for each group/partition
            let ordering_value = partition_ordering_rows.into_iter().map(|ordering_row| {
                    if let ScalarValue::Struct(s) = ordering_row {
                        let mut ordering_columns_per_row = vec![];

                        for column in s.columns() {
                            let sv = ScalarValue::try_from_array(column, 0)?;
                            ordering_columns_per_row.push(sv);
                        }

                        Ok(ordering_columns_per_row)
                    } else {
                        exec_err!(
                            "Expects to receive ScalarValue::Struct(Arc<StructArray>) but got:{:?}",
                            ordering_row.data_type()
                        )
                    }
                }).collect::<Result<VecDeque<_>>>()?;

            partition_ordering_values.push(ordering_value);
        }

        let sort_options = self
            .ordering_req
            .iter()
            .map(|sort_expr| sort_expr.options)
            .collect::<Vec<_>>();

        (self.values, self.ordering_values) = merge_ordered_arrays(
            &mut partition_values,
            &mut partition_ordering_values,
            &sort_options,
        )?;

        Ok(())
    }

    fn state(&mut self) -> Result<Vec<ScalarValue>> {
        let mut result = vec![self.evaluate()?];
        result.push(self.evaluate_orderings()?);
        Ok(result)
    }

    fn evaluate(&mut self) -> Result<ScalarValue> {
        let values = self.values.clone();
        let array = if self.reverse {
            ScalarValue::new_list_from_iter(
                values.into_iter().rev(),
                &self.datatypes[0],
                self.nullable,
            )
        } else {
            ScalarValue::new_list_from_iter(
                values.into_iter(),
                &self.datatypes[0],
                self.nullable,
            )
        };
        Ok(ScalarValue::List(array))
    }

    fn size(&self) -> usize {
        let mut total = std::mem::size_of_val(self)
            + ScalarValue::size_of_vec(&self.values)
            - std::mem::size_of_val(&self.values);

        // Add size of the `self.ordering_values`
        total +=
            std::mem::size_of::<Vec<ScalarValue>>() * self.ordering_values.capacity();
        for row in &self.ordering_values {
            total += ScalarValue::size_of_vec(row) - std::mem::size_of_val(row);
        }

        // Add size of the `self.datatypes`
        total += std::mem::size_of::<DataType>() * self.datatypes.capacity();
        for dtype in &self.datatypes {
            total += dtype.size() - std::mem::size_of_val(dtype);
        }

        // Add size of the `self.ordering_req`
        total += std::mem::size_of::<PhysicalSortExpr>() * self.ordering_req.capacity();
        // TODO: Calculate size of each `PhysicalSortExpr` more accurately.
        total
    }
}

impl OrderSensitiveArrayAggAccumulator {
    fn evaluate_orderings(&self) -> Result<ScalarValue> {
        let fields = ordering_fields(&self.ordering_req, &self.datatypes[1..]);
        let num_columns = fields.len();
        let struct_field = Fields::from(fields.clone());

        let mut column_wise_ordering_values = vec![];
        for i in 0..num_columns {
            let column_values = self
                .ordering_values
                .iter()
                .map(|x| x[i].clone())
                .collect::<Vec<_>>();
            let array = if column_values.is_empty() {
                new_empty_array(fields[i].data_type())
            } else {
                ScalarValue::iter_to_array(column_values.into_iter())?
            };
            column_wise_ordering_values.push(array);
        }

        let ordering_array = StructArray::try_new(
            struct_field.clone(),
            column_wise_ordering_values,
            None,
        )?;
        Ok(ScalarValue::List(Arc::new(array_into_list_array(
            Arc::new(ordering_array),
            self.nullable,
        ))))
    }
}

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

    use crate::aggregate::array_agg_ordered::merge_ordered_arrays;

    use arrow_array::{Array, ArrayRef, Int64Array};
    use arrow_schema::SortOptions;
    use datafusion_common::utils::get_row_at_idx;
    use datafusion_common::{Result, ScalarValue};

    #[test]
    fn test_merge_asc() -> Result<()> {
        let lhs_arrays: Vec<ArrayRef> = vec![
            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2])),
            Arc::new(Int64Array::from(vec![0, 1, 2, 3, 4])),
        ];
        let n_row = lhs_arrays[0].len();
        let lhs_orderings = (0..n_row)
            .map(|idx| get_row_at_idx(&lhs_arrays, idx))
            .collect::<Result<VecDeque<_>>>()?;

        let rhs_arrays: Vec<ArrayRef> = vec![
            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2])),
            Arc::new(Int64Array::from(vec![0, 1, 2, 3, 4])),
        ];
        let n_row = rhs_arrays[0].len();
        let rhs_orderings = (0..n_row)
            .map(|idx| get_row_at_idx(&rhs_arrays, idx))
            .collect::<Result<VecDeque<_>>>()?;
        let sort_options = vec![
            SortOptions {
                descending: false,
                nulls_first: false,
            },
            SortOptions {
                descending: false,
                nulls_first: false,
            },
        ];

        let lhs_vals_arr = Arc::new(Int64Array::from(vec![0, 1, 2, 3, 4])) as ArrayRef;
        let lhs_vals = (0..lhs_vals_arr.len())
            .map(|idx| ScalarValue::try_from_array(&lhs_vals_arr, idx))
            .collect::<Result<VecDeque<_>>>()?;

        let rhs_vals_arr = Arc::new(Int64Array::from(vec![0, 1, 2, 3, 4])) as ArrayRef;
        let rhs_vals = (0..rhs_vals_arr.len())
            .map(|idx| ScalarValue::try_from_array(&rhs_vals_arr, idx))
            .collect::<Result<VecDeque<_>>>()?;
        let expected =
            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2, 2, 3, 3, 4, 4])) as ArrayRef;
        let expected_ts = vec![
            Arc::new(Int64Array::from(vec![0, 0, 0, 0, 1, 1, 1, 1, 2, 2])) as ArrayRef,
            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2, 2, 3, 3, 4, 4])) as ArrayRef,
        ];

        let (merged_vals, merged_ts) = merge_ordered_arrays(
            &mut [lhs_vals, rhs_vals],
            &mut [lhs_orderings, rhs_orderings],
            &sort_options,
        )?;
        let merged_vals = ScalarValue::iter_to_array(merged_vals.into_iter())?;
        let merged_ts = (0..merged_ts[0].len())
            .map(|col_idx| {
                ScalarValue::iter_to_array(
                    (0..merged_ts.len())
                        .map(|row_idx| merged_ts[row_idx][col_idx].clone()),
                )
            })
            .collect::<Result<Vec<_>>>()?;

        assert_eq!(&merged_vals, &expected);
        assert_eq!(&merged_ts, &expected_ts);

        Ok(())
    }

    #[test]
    fn test_merge_desc() -> Result<()> {
        let lhs_arrays: Vec<ArrayRef> = vec![
            Arc::new(Int64Array::from(vec![2, 1, 1, 0, 0])),
            Arc::new(Int64Array::from(vec![4, 3, 2, 1, 0])),
        ];
        let n_row = lhs_arrays[0].len();
        let lhs_orderings = (0..n_row)
            .map(|idx| get_row_at_idx(&lhs_arrays, idx))
            .collect::<Result<VecDeque<_>>>()?;

        let rhs_arrays: Vec<ArrayRef> = vec![
            Arc::new(Int64Array::from(vec![2, 1, 1, 0, 0])),
            Arc::new(Int64Array::from(vec![4, 3, 2, 1, 0])),
        ];
        let n_row = rhs_arrays[0].len();
        let rhs_orderings = (0..n_row)
            .map(|idx| get_row_at_idx(&rhs_arrays, idx))
            .collect::<Result<VecDeque<_>>>()?;
        let sort_options = vec![
            SortOptions {
                descending: true,
                nulls_first: false,
            },
            SortOptions {
                descending: true,
                nulls_first: false,
            },
        ];

        // Values (which will be merged) doesn't have to be ordered.
        let lhs_vals_arr = Arc::new(Int64Array::from(vec![0, 1, 2, 1, 2])) as ArrayRef;
        let lhs_vals = (0..lhs_vals_arr.len())
            .map(|idx| ScalarValue::try_from_array(&lhs_vals_arr, idx))
            .collect::<Result<VecDeque<_>>>()?;

        let rhs_vals_arr = Arc::new(Int64Array::from(vec![0, 1, 2, 1, 2])) as ArrayRef;
        let rhs_vals = (0..rhs_vals_arr.len())
            .map(|idx| ScalarValue::try_from_array(&rhs_vals_arr, idx))
            .collect::<Result<VecDeque<_>>>()?;
        let expected =
            Arc::new(Int64Array::from(vec![0, 0, 1, 1, 2, 2, 1, 1, 2, 2])) as ArrayRef;
        let expected_ts = vec![
            Arc::new(Int64Array::from(vec![2, 2, 1, 1, 1, 1, 0, 0, 0, 0])) as ArrayRef,
            Arc::new(Int64Array::from(vec![4, 4, 3, 3, 2, 2, 1, 1, 0, 0])) as ArrayRef,
        ];
        let (merged_vals, merged_ts) = merge_ordered_arrays(
            &mut [lhs_vals, rhs_vals],
            &mut [lhs_orderings, rhs_orderings],
            &sort_options,
        )?;
        let merged_vals = ScalarValue::iter_to_array(merged_vals.into_iter())?;
        let merged_ts = (0..merged_ts[0].len())
            .map(|col_idx| {
                ScalarValue::iter_to_array(
                    (0..merged_ts.len())
                        .map(|row_idx| merged_ts[row_idx][col_idx].clone()),
                )
            })
            .collect::<Result<Vec<_>>>()?;

        assert_eq!(&merged_vals, &expected);
        assert_eq!(&merged_ts, &expected_ts);
        Ok(())
    }
}