// clang-format off
// Generated file (from: mean_implicit.mod.py). Do not edit
// clang-format off
// Generated file (from: mean_implicit.mod.py). Do not edit
// Generated from: mean_implicit.mod.py.
namespace mean_implicit {
// Generated mean_implicit test
#include "-"
// Generated model constructor
#include "-"
} // namespace mean_implicit

// Create the model
Model createTestModel() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_FLOAT32,
            .dimensions = {2, 2},
            .numberOfConsumers = 3,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_INT32,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 4},
        },
        {
            .type = OperandType::TENSOR_FLOAT32,
            .dimensions = {1, 2},
            .numberOfConsumers = 0,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::MEAN,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

std::vector<MixedTypedExample> examples = {
// Begin of an example
{
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {1.0f, 2.0f, 3.0f, 4.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {4.0f, 6.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
}
}, // End of an example
// Begin of an example
{
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {-1.0f, -2.0f, -3.0f, -4.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {-4.0f, -6.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
}
}, // End of an example
};

TEST_F(NeuralnetworksHidlTest, mean_implicit) {
  generated_tests::Execute(device,
                           mean_implicit::createTestModel,
                           mean_implicit::is_ignored,
                           mean_implicit::examples);
}

// Create the model
Model createTestModel_2() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_FLOAT32,
            .dimensions = {2, 2},
            .numberOfConsumers = 3,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_INT32,
            .dimensions = {1},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 4},
        },
        {
            .type = OperandType::TENSOR_FLOAT32,
            .dimensions = {2, 1},
            .numberOfConsumers = 0,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::MEAN,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      1, 0, 0, 0, 1, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_2(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

std::vector<MixedTypedExample> examples_2 = {
// Begin of an example
{
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {1.0f, 2.0f, 3.0f, 4.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {3.0f, 7.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
}
}, // End of an example
// Begin of an example
{
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {-1.0f, -2.0f, -3.0f, -4.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {-3.0f, -7.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
}
}, // End of an example
};

TEST_F(NeuralnetworksHidlTest, mean_implicit_2) {
  generated_tests::Execute(device,
                           mean_implicit::createTestModel_2,
                           mean_implicit::is_ignored_2,
                           mean_implicit::examples_2);
}

// Create the model
Model createTestModel_3() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_FLOAT32,
            .dimensions = {2, 2},
            .numberOfConsumers = 3,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::TENSOR_INT32,
            .dimensions = {2},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 8},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 8, .length = 4},
        },
        {
            .type = OperandType::TENSOR_FLOAT32,
            .dimensions = {1},
            .numberOfConsumers = 0,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::MEAN,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_3(int i) {
  static std::set<int> ignore = {};
  return ignore.find(i) != ignore.end();
}

std::vector<MixedTypedExample> examples_3 = {
// Begin of an example
{
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {1.0f, 2.0f, 3.0f, 4.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {10.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
}
}, // End of an example
// Begin of an example
{
//Input(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {-1.0f, -2.0f, -3.0f, -4.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
},
//Output(s)
{ // See tools/test_generator/include/TestHarness.h:MixedTyped
  // int -> FLOAT32 map
  {{0, {-10.0f}}},
  // int -> INT32 map
  {},
  // int -> QUANT8_ASYMM map
  {}
}
}, // End of an example
};

TEST_F(NeuralnetworksHidlTest, mean_implicit_3) {
  generated_tests::Execute(device,
                           mean_implicit::createTestModel_3,
                           mean_implicit::is_ignored_3,
                           mean_implicit::examples_3);
}