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// 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.

#include <stddef.h>
#include <stdint.h>

#include <algorithm>
#include <boost/iterator/iterator_facade.hpp>
#include <memory>
#include <utility>

#include "common/status.h"
#include "vec/aggregate_functions/aggregate_function.h"
#include "vec/columns/column.h"
#include "vec/columns/column_vector.h"
#include "vec/columns/columns_number.h"
#include "vec/core/block.h"
#include "vec/core/column_numbers.h"
#include "vec/core/column_with_type_and_name.h"
#include "vec/core/types.h"
#include "vec/data_types/data_type.h"
#include "vec/data_types/data_type_nullable.h"
#include "vec/data_types/data_type_number.h"
#include "vec/functions/function.h"
#include "vec/functions/simple_function_factory.h"

namespace doris {
class FunctionContext;
} // namespace doris

namespace doris::vectorized {
class FunctionWidthBucket : public IFunction {
public:
    static constexpr auto name = "width_bucket";
    static FunctionPtr create() { return std::make_shared<FunctionWidthBucket>(); }

    /// Get function name.
    String get_name() const override { return name; }

    bool is_variadic() const override { return false; }

    size_t get_number_of_arguments() const override { return 4; }

    DataTypePtr get_return_type_impl(const DataTypes& arguments) const override {
        return std::make_shared<DataTypeInt64>();
    }

    Status execute_impl(FunctionContext* context, Block& block, const ColumnNumbers& arguments,
                        size_t result, size_t input_rows_count) override {
        ColumnPtr expr_ptr =
                block.get_by_position(arguments[0]).column->convert_to_full_column_if_const();
        ColumnPtr min_value_ptr =
                block.get_by_position(arguments[1]).column->convert_to_full_column_if_const();
        ColumnPtr max_value_ptr =
                block.get_by_position(arguments[2]).column->convert_to_full_column_if_const();
        ColumnPtr num_buckets_ptr =
                block.get_by_position(arguments[3]).column->convert_to_full_column_if_const();
        int64_t num_buckets = num_buckets_ptr->get_int(0);

        auto nested_column_ptr = ColumnInt64::create(input_rows_count, 0);
        DataTypePtr expr_type = block.get_by_position(arguments[0]).type;

        _execute_by_type(*expr_ptr, *min_value_ptr, *max_value_ptr, num_buckets, *nested_column_ptr,
                         expr_type);

        block.replace_by_position(result, std::move(nested_column_ptr));
        return Status::OK();
    }

private:
    template <typename ColumnType>
    void _execute(const IColumn& expr_column, const IColumn& min_value_column,
                  const IColumn& max_value_column, const int64_t num_buckets,
                  IColumn& nested_column) {
        const ColumnType& expr_column_concrete = reinterpret_cast<const ColumnType&>(expr_column);
        const ColumnType& min_value_column_concrete =
                reinterpret_cast<const ColumnType&>(min_value_column);
        const ColumnType& max_value_column_concrete =
                reinterpret_cast<const ColumnType&>(max_value_column);
        ColumnInt64& nested_column_concrete = reinterpret_cast<ColumnInt64&>(nested_column);

        size_t input_rows_count = expr_column.size();

        for (size_t i = 0; i < input_rows_count; ++i) {
            auto min_value = min_value_column_concrete.get_data()[i];
            auto max_value = max_value_column_concrete.get_data()[i];
            if (expr_column_concrete.get_data()[i] < min_value) {
                continue;
            } else if (expr_column_concrete.get_data()[i] >= max_value) {
                nested_column_concrete.get_data()[i] = num_buckets + 1;
            } else {
                if ((max_value - min_value) / num_buckets == 0) {
                    continue;
                }
                nested_column_concrete.get_data()[i] =
                        (int64_t)(1 + (expr_column_concrete.get_data()[i] - min_value) /
                                              ((max_value - min_value) / num_buckets));
            }
        }
    }

    void _execute_by_type(const IColumn& expr_column, const IColumn& min_value_column,
                          const IColumn& max_value_column, const int64_t num_buckets,
                          IColumn& nested_column_column, DataTypePtr& expr_type) {
        WhichDataType which(remove_nullable(expr_type));
        if (which.is_int8()) {
            _execute<ColumnInt8>(expr_column, min_value_column, max_value_column, num_buckets,
                                 nested_column_column);
        } else if (which.is_int16()) {
            _execute<ColumnInt16>(expr_column, min_value_column, max_value_column, num_buckets,
                                  nested_column_column);
        } else if (which.is_int32()) {
            _execute<ColumnInt32>(expr_column, min_value_column, max_value_column, num_buckets,
                                  nested_column_column);
        } else if (which.is_int64()) {
            _execute<ColumnInt64>(expr_column, min_value_column, max_value_column, num_buckets,
                                  nested_column_column);
        } else if (which.is_float32()) {
            _execute<ColumnFloat32>(expr_column, min_value_column, max_value_column, num_buckets,
                                    nested_column_column);
        } else if (which.is_float64()) {
            _execute<ColumnFloat64>(expr_column, min_value_column, max_value_column, num_buckets,
                                    nested_column_column);
        } else if (which.is_decimal128()) {
            _execute<ColumnDecimal128>(expr_column, min_value_column, max_value_column, num_buckets,
                                       nested_column_column);
        } else if (which.is_date()) {
            _execute<ColumnDate>(expr_column, min_value_column, max_value_column, num_buckets,
                                 nested_column_column);
        } else if (which.is_date_v2()) {
            _execute<ColumnDateV2>(expr_column, min_value_column, max_value_column, num_buckets,
                                   nested_column_column);
        } else if (which.is_date_time()) {
            _execute<ColumnDateTime>(expr_column, min_value_column, max_value_column, num_buckets,
                                     nested_column_column);
        } else if (which.is_date_time_v2()) {
            _execute<ColumnDateTimeV2>(expr_column, min_value_column, max_value_column, num_buckets,
                                       nested_column_column);
        }
    }
};

void register_function_width_bucket(SimpleFunctionFactory& factory) {
    factory.register_function<FunctionWidthBucket>();
}

} // namespace doris::vectorized