// Copyright 2024 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.

#include <algorithm>  // For std::generate, std::shuffle.
#include <array>      // For std::array.
#include <cmath>
#include <cstddef>  // For size_t.
#include <cstdint>
#include <functional>  // For std::multiplies.
#include <memory>      // For std::unique_ptr.
#include <numeric>     // For std::accumulate.
#include <random>      // For std::uniform_real_distribution.
#include <vector>      // For std::vector.

#include <gtest/gtest.h>
#include "xnnpack.h"
#include "xnnpack/math.h"
#include "xnnpack/node-type.h"
#include "xnnpack/operator.h"
#include "xnnpack/subgraph.h"
#include "replicable_random_device.h"
#include "subgraph-unary-tester.h"

template <typename InputType, typename OutputType = InputType,
          size_t min_dim = 1, size_t max_dim = XNN_MAX_TENSOR_DIMS,
          bool pad_output = false>
class StaticExpandDimsTest
    : public UnaryTest<InputType, OutputType, min_dim, max_dim, pad_output> {
 protected:
  std::vector<size_t> GetNewAxes() {
    size_t min_new_axes_size = this->dims.size();
    size_t max_new_axes_size = XNN_MAX_TENSOR_DIMS - min_new_axes_size;
    auto num_new_axis_dist =
        std::uniform_int_distribution<size_t>(std::min(min_new_axes_size, max_new_axes_size), max_new_axes_size);
    size_t num_new_axes = num_new_axis_dist(this->rng);
    auto new_axes_dist =
        std::uniform_int_distribution<size_t>(0, this->dims.size());
    new_axes.resize(num_new_axes);
    for (int i = 0; i < num_new_axes; ++i) {
      new_axes[i] = new_axes_dist(this->rng);
    }
    std::sort(new_axes.begin(), new_axes.end());
    auto new_end = std::unique(new_axes.begin(), new_axes.end());
    new_axes.erase(new_end, new_axes.end());
    return new_axes;
  }
  void CalculateExpectedShape() {
    this->expected_shape = this->dims;
    for (size_t it : new_axes) {
      this->expected_shape.insert(this->expected_shape.begin() + it, 1);
    }
  }
  std::vector<size_t> new_axes;
  std::vector<size_t> expected_shape;
};

using StaticExpandDimsTestInt8 = StaticExpandDimsTest<int8_t>;
using StaticExpandDimsTestF16 = StaticExpandDimsTest<xnn_float16>;
using StaticExpandDimsTestF32 = StaticExpandDimsTest<float>;

TEST_F(StaticExpandDimsTestInt8, define)
{
  const int32_t zero_point = i8dist(rng);
  const float scale = scale_dist(rng);

  ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));

  xnn_subgraph_t subgraph = nullptr;
  ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/2, /*flags=*/0, &subgraph));
  std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);

  input_id = XNN_INVALID_NODE_ID;
  ASSERT_EQ(
    xnn_status_success, xnn_define_quantized_tensor_value(
                          subgraph, xnn_datatype_qint8, zero_point, scale, dims.size(), dims.data(),
                          nullptr, 0, /*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
  ASSERT_NE(input_id, XNN_INVALID_NODE_ID);

  CalculateExpectedShape();
  output_id = XNN_INVALID_NODE_ID;
  ASSERT_EQ(
    xnn_status_success, xnn_define_quantized_tensor_value(
                          subgraph, xnn_datatype_qint8, zero_point, scale, expected_shape.size(), expected_shape.data(),
                          nullptr, 1, /*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
  ASSERT_NE(output_id, XNN_INVALID_NODE_ID);

  new_axes = GetNewAxes();
  ASSERT_EQ(xnn_status_success, xnn_define_static_expand_dims(subgraph, new_axes.size(), new_axes.data(), input_id, output_id, /*flags=*/0));

  ASSERT_EQ(subgraph->num_nodes, 1);
  const struct xnn_node* node = &subgraph->nodes[0];
  ASSERT_EQ(node->type, xnn_node_type_static_expand_dims);
  ASSERT_EQ(node->num_inputs, 1);
  ASSERT_EQ(node->inputs[0], input_id);
  ASSERT_EQ(node->num_outputs, 1);
  ASSERT_EQ(node->outputs[0], output_id);
  ASSERT_EQ(node->flags, 0);
}

TEST_F(StaticExpandDimsTestInt8, matches_operator_api)
{
  const int32_t zero_point = i8dist(rng);
  const float scale = scale_dist(rng);
  std::generate(input.begin(), input.end(), [&]() { return i8dist(rng); });

  new_axes = GetNewAxes();

  ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));

  // Call operator API.
  xnn_operator_t op = nullptr;
  const xnn_status status =
    xnn_create_copy_nc_x8(/*flags=*/0, &op);
  if (status == xnn_status_unsupported_hardware) {
    GTEST_SKIP();
  }
  ASSERT_EQ(xnn_status_success, status);
  ASSERT_NE(nullptr, op);
  std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_op(op, xnn_delete_operator);

  size_t batch_size = NumElements(dims);
  ASSERT_EQ(xnn_status_success, xnn_reshape_copy_nc_x8(op, batch_size, 1, 1, 1, /*threadpool=*/nullptr));
  ASSERT_EQ(xnn_status_success, xnn_setup_copy_nc_x8(op, input.data(), operator_output.data()));
  ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));

  // Call subgraph API.
  xnn_subgraph_t subgraph = nullptr;
  ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/2, /*flags=*/0, &subgraph));
  std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
  input_id = XNN_INVALID_NODE_ID;
  ASSERT_EQ(
    xnn_status_success, xnn_define_quantized_tensor_value(
                          subgraph, xnn_datatype_qint8, zero_point, scale, dims.size(), dims.data(),
                          nullptr, /*external_id=*/0, /*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
  ASSERT_NE(input_id, XNN_INVALID_NODE_ID);

  CalculateExpectedShape();
  output_id = XNN_INVALID_NODE_ID;
  ASSERT_EQ(
    xnn_status_success, xnn_define_quantized_tensor_value(
                          subgraph, xnn_datatype_qint8, zero_point, scale, expected_shape.size(), expected_shape.data(),
                          nullptr, /*external_id=*/1, /*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
  ASSERT_NE(output_id, XNN_INVALID_NODE_ID);

  ASSERT_EQ(xnn_status_success, xnn_define_static_expand_dims(subgraph, new_axes.size(), new_axes.data(), input_id, output_id, /*flags=*/0));

  xnn_runtime_t runtime = nullptr;
  ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
  ASSERT_NE(nullptr, runtime);
  std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);

  std::array<xnn_external_value, 2> external = {
    xnn_external_value{input_id, input.data()}, xnn_external_value{output_id, subgraph_output.data()}};
  ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
  ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));

  ASSERT_EQ(subgraph_output, operator_output);

  size_t num_out_dims;
  std::vector<size_t> out_dims(XNN_MAX_TENSOR_DIMS);
  ASSERT_EQ(xnn_status_success, xnn_get_external_value_shape(runtime, output_id, &num_out_dims, &out_dims[0]));
  out_dims.resize(num_out_dims);
  EXPECT_EQ(expected_shape, out_dims);
}

TEST_F(StaticExpandDimsTestF16, define)
{
  ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));

  xnn_subgraph_t subgraph = nullptr;
  ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/2, /*flags=*/0, &subgraph));
  std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);

  input_id = XNN_INVALID_NODE_ID;
  ASSERT_EQ(
    xnn_status_success, xnn_define_tensor_value(
                          subgraph, xnn_datatype_fp16, dims.size(), dims.data(),
                          nullptr, 0, /*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
  ASSERT_NE(input_id, XNN_INVALID_NODE_ID);

  CalculateExpectedShape();
  output_id = XNN_INVALID_NODE_ID;
  ASSERT_EQ(
    xnn_status_success, xnn_define_tensor_value(
                          subgraph, xnn_datatype_fp16, expected_shape.size(), expected_shape.data(),
                          nullptr, 1, /*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
  ASSERT_NE(output_id, XNN_INVALID_NODE_ID);

  new_axes = GetNewAxes();
  ASSERT_EQ(xnn_status_success, xnn_define_static_expand_dims(subgraph, new_axes.size(), new_axes.data(), input_id, output_id, /*flags=*/0));

  ASSERT_EQ(subgraph->num_nodes, 1);
  const struct xnn_node* node = &subgraph->nodes[0];
  ASSERT_EQ(node->type, xnn_node_type_static_expand_dims);
  ASSERT_EQ(node->num_inputs, 1);
  ASSERT_EQ(node->inputs[0], input_id);
  ASSERT_EQ(node->num_outputs, 1);
  ASSERT_EQ(node->outputs[0], output_id);
  ASSERT_EQ(node->flags, 0);
}

TEST_F(StaticExpandDimsTestF16, matches_operator_api)
{
  std::generate(input.begin(), input.end(), [&]() { return xnn_float16_from_float(f32dist(rng)); });

  new_axes = GetNewAxes();

  ASSERT_EQ(xnn_status_success, xnn_initialize(/*allocator=*/nullptr));

  // Call operator API.
  xnn_operator_t op = nullptr;
  const xnn_status status =
    xnn_create_copy_nc_x16(/*flags=*/0, &op);
  if (status == xnn_status_unsupported_hardware) {
    GTEST_SKIP();
  }
  ASSERT_EQ(xnn_status_success, status);
  ASSERT_NE(nullptr, op);
  std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_op(op, xnn_delete_operator);

  size_t batch_size = NumElements(dims);
  ASSERT_EQ(xnn_status_success, xnn_reshape_copy_nc_x16(op, batch_size, 1, 1, 1, /*threadpool=*/nullptr));
  ASSERT_EQ(xnn_status_success, xnn_setup_copy_nc_x16(op, input.data(), operator_output.data()));
  ASSERT_EQ(xnn_status_success, xnn_run_operator(op, /*threadpool=*/nullptr));

  // Call subgraph API.
  xnn_subgraph_t subgraph = nullptr;
  ASSERT_EQ(xnn_status_success, xnn_create_subgraph(/*external_value_ids=*/2, /*flags=*/0, &subgraph));
  std::unique_ptr<xnn_subgraph, decltype(&xnn_delete_subgraph)> auto_subgraph(subgraph, xnn_delete_subgraph);
  input_id = XNN_INVALID_NODE_ID;
  ASSERT_EQ(
    xnn_status_success, xnn_define_tensor_value(
                          subgraph, xnn_datatype_fp16, dims.size(), dims.data(),
                          nullptr, /*external_id=*/0, /*flags=*/XNN_VALUE_FLAG_EXTERNAL_INPUT, &input_id));
  ASSERT_NE(input_id, XNN_INVALID_NODE_ID);

  CalculateExpectedShape();
  output_id = XNN_INVALID_NODE_ID;
  ASSERT_EQ(
    xnn_status_success, xnn_define_tensor_value(
                          subgraph, xnn_datatype_fp16, expected_shape.size(), expected_shape.data(),
                          nullptr, /*external_id=*/1, /*flags=*/XNN_VALUE_FLAG_EXTERNAL_OUTPUT, &output_id));
  ASSERT_NE(output_id, XNN_INVALID_NODE_ID);

  ASSERT_EQ(xnn_status_success, xnn_define_static_expand_dims(subgraph, new_axes.size(), new_axes.data(), input_id, output_id, /*flags=*/0));

  xnn_runtime_t runtime = nullptr;
  ASSERT_EQ(xnn_status_success, xnn_create_runtime_v3(subgraph, nullptr, nullptr, /*flags=*/0, &runtime));
  ASSERT_NE(nullptr, runtime);
  std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)> auto_runtime(runtime, xnn_delete_runtime);

  std::array<xnn_external_value, 2> external = {
    xnn_external_value{input_id, input.data()}, xnn_external_value{output_id, subgraph_output.data()}};
  ASSERT_EQ(xnn_status_success, xnn_setup_runtime(runtime, external.size(), external.data()));
  ASSERT_EQ(xnn_status_success, xnn_invoke_runtime(runtime));

  ASSERT_EQ(subgraph_output, operator_output);

  size_t num_out_dims;
  std::vector<size_t> out_dims(XNN_MAX_TENSOR_DIMS);
  ASSERT_EQ(xnn_status_success, xnn_get_external_value_shape(runtime, output_id, &num_out_dims, &out_dims[0]));
  out_dims.resize(num_out_dims);
  EXPECT_EQ(expected_shape, out_dims);
}