/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.

Licensed 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 <string.h>
#include "tensorflow/contrib/lite/c/builtin_op_data.h"
#include "tensorflow/contrib/lite/c/c_api_internal.h"
#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
#include "tensorflow/contrib/lite/kernels/internal/tensor.h"
#include "tensorflow/contrib/lite/kernels/kernel_util.h"
#include "tensorflow/contrib/lite/kernels/op_macros.h"
#include "tensorflow/contrib/lite/string_util.h"

namespace tflite {
namespace ops {
namespace builtin {
namespace gather {
constexpr int kInputTensor = 0;
constexpr int kInputPositions = 1;
constexpr int kOutputTensor = 0;

TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);

  const auto* params =
      reinterpret_cast<const TfLiteGatherParams*>(node->builtin_data);
  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  const TfLiteTensor* positions = GetInput(context, node, kInputPositions);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  // Only INT32 positions are supported.
  TF_LITE_ENSURE_EQ(context, positions->type, kTfLiteInt32);
  // Assign to output the input type.
  output->type = input->type;
  // TODO(mgubin): Only default axis == 0 is supported.
  TF_LITE_ENSURE_EQ(context, params->axis, 0);
  // Check conditions for different types.
  switch (input->type) {
    case kTfLiteFloat32:
    case kTfLiteUInt8:
    case kTfLiteInt32: {
      // Fully supported by reference_ops::Gather.
    } break;

    case kTfLiteString: {
      // Only 1D input is supported.
      TF_LITE_ENSURE_EQ(context, NumDimensions(input), 1);
    } break;
    default:
      context->ReportError(
          context, "Only float32 and string types are supported, got %d",
          input->type);
      return kTfLiteError;
  }
  const int num_dimensions =
      NumDimensions(input) + NumDimensions(positions) - 1;
  TF_LITE_ENSURE(context, params->axis <= num_dimensions);
  TfLiteIntArray* output_shape = TfLiteIntArrayCreate(num_dimensions);
  int output_index = 0;
  for (int i = 0; i < params->axis; ++i) {
    output_shape->data[output_index++] = input->dims->data[i];
  }
  for (int i = 0; i < positions->dims->size; ++i) {
    output_shape->data[output_index++] = positions->dims->data[i];
  }
  for (int i = params->axis + 1; i < input->dims->size; ++i) {
    output_shape->data[output_index++] = input->dims->data[i];
  }
  return context->ResizeTensor(context, output, output_shape);
}

TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
  const TfLiteTensor* positions = GetInput(context, node, kInputPositions);
  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  const int input_rank = NumDimensions(input);
#define TF_LITE_GATHER(data_type, index_type)                              \
  {                                                                        \
    tflite::GatherParams op_params;                                        \
    op_params.input_rank = input_rank;                                     \
    optimized_ops::Gather(                                                 \
        op_params, GetTensorShape(input), GetTensorData<data_type>(input), \
        GetTensorShape(positions), GetTensorData<index_type>(positions),   \
        GetTensorShape(output), GetTensorData<data_type>(output));         \
  }
  switch (input->type) {
    case kTfLiteFloat32:
      TF_LITE_GATHER(float, int32_t);
      break;
    case kTfLiteUInt8:
      TF_LITE_GATHER(uint8_t, int32_t);
      break;
    case kTfLiteInt32:
      TF_LITE_GATHER(int32_t, int32_t);
      break;
    case kTfLiteString: {
      // TODO(mgubin): Currently support only for 1D output tensors.
      DynamicBuffer buffer;
      const int32* indexes = positions->data.i32;
      const int num_strings = GetStringCount(input);
      for (int i = 0; i < positions->dims->data[0]; ++i) {
        const int pos = indexes[i];
        TF_LITE_ENSURE(context, pos < num_strings);
        const auto string_ref = GetString(input, pos);
        buffer.AddString(string_ref.str, string_ref.len);
      }
      buffer.WriteToTensor(output);
    } break;
    default:
      return kTfLiteError;
  }
#undef TF_LITE_GATHER
  return kTfLiteOk;
}
}  // namespace gather

TfLiteRegistration* Register_GATHER() {
  static TfLiteRegistration r = {nullptr, nullptr, gather::Prepare,
                                 gather::Eval};
  return &r;
}

}  // namespace builtin
}  // namespace ops
}  // namespace tflite