text: /*
   * Copyright (c) 2018-2021 Arm Limited.
   *
   * SPDX-License-Identifier: MIT
   *
   * Permission is hereby granted, free of charge, to any person obtaining a copy
   * of this software and associated documentation files (the "Software"), to
   * deal in the Software without restriction, including without limitation the
   * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
   * sell copies of the Software, and to permit persons to whom the Software is
   * furnished to do so, subject to the following conditions:
   *
   * The above copyright notice and this permission notice shall be included in all
   * copies or substantial portions of the Software.
   *
   * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
   * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
   * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
   * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
   * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
   * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
   * SOFTWARE.
   */
  #include "src/core/NEON/kernels/NERangeKernel.h"
  
  #include "arm_compute/core/Error.h"
  #include "arm_compute/core/Helpers.h"
  #include "arm_compute/core/ITensor.h"
  #include "arm_compute/core/TensorInfo.h"
  #include "arm_compute/core/Validate.h"
  #include "src/core/NEON/NEAsymm.h"
  #include "src/core/NEON/wrapper/wrapper.h"
  #include "src/core/common/Registrars.h"
  #include "src/core/helpers/AutoConfiguration.h"
  #include "src/core/helpers/WindowHelpers.h"
  #include "src/cpu/kernels/range/list.h"
  
  namespace arm_compute
  {
  namespace
  {
  struct RangeSelectorData
  {
      DataType dt;
  };
  
  using RangeSelectorPtr = std::add_pointer<bool(const RangeSelectorData &data)>::type;
  using RangeUKernelPtr  = std::add_pointer<void(ITensor *, float, float, const Window &)>::type;
  
  struct RangeUKernel
  {
      const char            *name;
      const RangeSelectorPtr is_selected;
      RangeUKernelPtr        ukernel;
  };
  
  static const RangeUKernel available_kernels[] =
  {
      {
          "fp16_neon_range",
          [](const RangeSelectorData & data) { return data.dt == DataType::F16; },
          REGISTER_FP16_NEON(arm_compute::cpu::fp16_neon_range_function)
      },
      {
          "f32_neon_range",
          [](const RangeSelectorData & data) { return data.dt == DataType::F32; },
          REGISTER_FP32_NEON(arm_compute::cpu::fp32_neon_range_function)
      },
      {
          "u8_neon_range",
          [](const RangeSelectorData & data) { return data.dt == DataType::U8; },
          REGISTER_INTEGER_NEON(arm_compute::cpu::u8_neon_range_function)
      },
      {
          "u16_neon_range",
          [](const RangeSelectorData & data) { return data.dt == DataType::U16; },
          REGISTER_INTEGER_NEON(arm_compute::cpu::u16_neon_range_function)
      },
      {
          "u32_neon_range",
          [](const RangeSelectorData & data) { return data.dt == DataType::U32; },
          REGISTER_INTEGER_NEON(arm_compute::cpu::u32_neon_range_function)
      },
      {
          "s8_neon_range",
          [](const RangeSelectorData & data) { return data.dt == DataType::S8; },
          REGISTER_INTEGER_NEON(arm_compute::cpu::s8_neon_range_function)
      },
      {
          "s16_neon_range",
          [](const RangeSelectorData & data) { return data.dt == DataType::S16; },
          REGISTER_INTEGER_NEON(arm_compute::cpu::s16_neon_range_function)
      },
      {
          "s32_neon_range",
          [](const RangeSelectorData & data) { return data.dt == DataType::S32; },
          REGISTER_INTEGER_NEON(arm_compute::cpu::s32_neon_range_function)
      },
  };
  
  /** Micro-kernel selector
   *
   * @param[in] data Selection data passed to help pick the appropriate micro-kernel
   *
   * @return A matching micro-kernel else nullptr
   */
  const RangeUKernel *get_implementation(const RangeSelectorData &data)
  {
      for(const auto &uk : available_kernels)
      {
          if(uk.is_selected(data))
          {
              return &uk;
          }
      }
      return nullptr;
  }
  
  Status validate_arguments(const ITensorInfo &output, const float start, const float end, const float step)
  {
      const auto *uk = get_implementation(RangeSelectorData{ output.data_type() });
      ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr);
  
      ARM_COMPUTE_RETURN_ERROR_ON_MSG((start == end), "start of the requested sequence must not be equal to the end");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(((start < end) && (step <= 0)), "step must be greater than 0 when start < end");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(((start > end) && (step >= 0)), "step must be less than 0 when start > end");
  
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!check_value_range(start, output.data_type(), output.quantization_info()), "start value is outside the range of the data type");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!check_value_range(end, output.data_type(), output.quantization_info()), "end value is outside the range of the data type");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!check_value_range(step, output.data_type(), output.quantization_info()), "step value is outside the range of the data type");
  
      ARM_COMPUTE_RETURN_ERROR_ON_MSG((start == end), "start of the requested sequence must not be equal to the end");
  
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(output.num_dimensions() != 1, "Output has to be a 1-D tensor");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(output.tensor_shape().total_size() < num_of_elements_in_range(start, end, step), "Output tensor size is incorrect");
  
      return Status{};
  }
  } // namespace
  
  NERangeKernel::NERangeKernel()
      : _start(0), _end(1), _step(1), _output(nullptr)
  {
  }
  
  void NERangeKernel::configure(ITensor *output, float start, float end, float step)
  {
      ARM_COMPUTE_ERROR_ON_NULLPTR(output);
  
      ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*(output->info()), start, end, step));
  
      // Auto initialize output if not initialized
      auto_init_if_empty(*output->info(), TensorShape(num_of_elements_in_range(start, end, step)), 1, output->info()->data_type(), output->info()->quantization_info());
  
      // Configure kernel window
      Window win = calculate_max_window(*output->info(), Steps());
  
      _start  = start;
      _end    = end;
      _step   = step;
      _output = output;
  
      INEKernel::configure(win);
  }
  
  Status NERangeKernel::validate(const ITensorInfo *output, float start, float end, float step)
  {
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
  
      ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*output, start, end, step));
  
      return Status{};
  }
  
  void NERangeKernel::run(const Window &window, const ThreadInfo &info)
  {
      ARM_COMPUTE_UNUSED(info);
      ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
      ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
      const auto *uk = get_implementation(RangeSelectorData{ _output->info()->data_type() });
  
      uk->ukernel(_output, _start, _step, window);
  }
  } // namespace arm_compute
  