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

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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 <fmt/format.h>

#include <glog/logging.h>

#include <algorithm>

#include <boost/iterator/iterator_facade.hpp>

#include <cstdint>

#include <cstdlib>

#include <memory>

#include <random>

#include <utility>

#include "common/status.h"

#include "udf/udf.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/common/assert_cast.h"

#include "vec/core/block.h"

#include "vec/core/column_numbers.h"

#include "vec/core/types.h"

#include "vec/data_types/data_type_number.h"

#include "vec/functions/function.h"

#include "vec/functions/simple_function_factory.h"

namespace doris::vectorized {

class Random : public IFunction {

public:

    static constexpr auto name = "random";

    static FunctionPtr create() { return std::make_shared<Random>(); }

    String get_name() const override { return name; }

    bool use_default_implementation_for_constants() const override { return false; }

    size_t get_number_of_arguments() const override { return 0; }

    bool is_variadic() const override { return true; }

    DataTypePtr get_return_type_impl(const DataTypes& arguments) const override {

        if (arguments.size() == 2) {

            return std::make_shared<DataTypeInt64>();

        }

        return std::make_shared<DataTypeFloat64>();

    }

    Status open(FunctionContext* context, FunctionContext::FunctionStateScope scope) override {

        std::shared_ptr<std::mt19937_64> generator(new std::mt19937_64());

        context->set_function_state(scope, generator);

        if (scope == FunctionContext::THREAD_LOCAL) {

            if (context->get_num_args() == 1) {

                // This is a call to RandSeed, initialize the seed

                if (!context->is_col_constant(0)) {

                    return Status::InvalidArgument("The param of rand function must be literal");

                }

                uint32_t seed = 0;

                if (!context->get_constant_col(0)->column_ptr->is_null_at(0)) {

                    seed = (*context->get_constant_col(0)->column_ptr)[0].get<int64_t>();

                }

                generator->seed(seed);

            } else if (context->get_num_args() == 2) {

                if (!context->is_col_constant(0) || !context->is_col_constant(1)) {

                    return Status::InvalidArgument("The param of rand function must be literal");

                }

                generator->seed(std::random_device()());

            } else { // zero args

                generator->seed(std::random_device()());

            }

        }

        return Status::OK();

    }

    Status execute_impl(FunctionContext* context, Block& block, const ColumnNumbers& arguments,

                        uint32_t result, size_t input_rows_count) const override {

        if (arguments.size() == 2) {

            return _execute_int_range(context, block, arguments, result, input_rows_count);

        }

        return _execute_float(context, block, arguments, result, input_rows_count);

    }

    Status close(FunctionContext* context, FunctionContext::FunctionStateScope scope) override {

        return Status::OK();

    }

private:

    static Status _execute_int_range(FunctionContext* context, Block& block,

                                     const ColumnNumbers& arguments, uint32_t result,

                                     size_t input_rows_count) {

        auto res_column = ColumnInt64::create(input_rows_count);

        auto& res_data = static_cast<ColumnInt64&>(*res_column).get_data();

        auto* generator = reinterpret_cast<std::mt19937_64*>(

                context->get_function_state(FunctionContext::THREAD_LOCAL));

        DCHECK(generator != nullptr);

        // checked in open()

        Int64 min = assert_cast<const ColumnInt64*>(

                            assert_cast<const ColumnConst*>(

                                    block.get_by_position(arguments[0]).column.get())

                                    ->get_data_column_ptr()

                                    .get())

                            ->get_element(0);

        Int64 max = assert_cast<const ColumnInt64*>(

                            assert_cast<const ColumnConst*>(

                                    block.get_by_position(arguments[1]).column.get())

                                    ->get_data_column_ptr()

                                    .get())

                            ->get_element(0);

        if (min >= max) {

            return Status::InvalidArgument(fmt::format(

                    "random's lower bound should less than upper bound, but got [{}, {})", min,

                    max));

        }

        std::uniform_int_distribution<int64_t> distribution(min, max);

        for (int i = 0; i < input_rows_count; i++) {

            res_data[i] = distribution(*generator);

        }

        block.replace_by_position(result, std::move(res_column));

        return Status::OK();

    }

    static Status _execute_float(FunctionContext* context, Block& block,

                                 const ColumnNumbers& arguments, uint32_t result,

                                 size_t input_rows_count) {

        static const double min = 0.0;

        static const double max = 1.0;

        auto res_column = ColumnFloat64::create(input_rows_count);

        auto& res_data = static_cast<ColumnFloat64&>(*res_column).get_data();

        auto* generator = reinterpret_cast<std::mt19937_64*>(

                context->get_function_state(FunctionContext::THREAD_LOCAL));

        DCHECK(generator != nullptr);

        std::uniform_real_distribution<double> distribution(min, max);

        for (int i = 0; i < input_rows_count; i++) {

            res_data[i] = distribution(*generator);

        }

        block.replace_by_position(result, std::move(res_column));

        return Status::OK();

    }

};

void register_function_random(SimpleFunctionFactory& factory) {

    factory.register_function<Random>();

    factory.register_alias(Random::name, "rand");

}

} // namespace doris::vectorized