/**

 * Copyright (c) 2025 Huawei Technologies Co., Ltd.

 * This program is free software, you can redistribute it and/or modify it under the terms and conditions of

 * CANN Open Software License Agreement Version 2.0 (the "License").

 * Please refer to the License for details. You may not use this file except in compliance with the License.

 * THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,

 * INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.

 * See LICENSE in the root of the software repository for the full text of the License.

 */



/*

 * test_geir_not_equal.cpp

 */



#include <iostream>

#include <fstream>

#include <string.h>

#include <stdint.h>

#include <vector>

#include <string>

#include <map>

#include "assert.h"



#include "graph.h"

#include "types.h"

#include "tensor.h"

#include "ge_error_codes.h"

#include "ge_api_types.h"

#include "ge_api.h"

#include "array_ops.h"

#include "ge_ir_build.h"



#include "experiment_ops.h"

#include "nn_other.h"

#include "../op_graph/not_equal_proto.h"



#define FAILED -1

#define SUCCESS 0



using namespace ge;

using std::map;

using std::string;

using std::vector;

#define ADD_INPUT(intputIndex, intputName, intputDtype, inputShape)                          \

    vector<int64_t> placeholder##intputIndex##_shape = inputShape;                           \

    auto placeholder##intputIndex = op::Data("placeholder" + intputIndex).set_attr_index(0); \

    TensorDesc placeholder##intputIndex##_desc =                                             \

        TensorDesc(ge::Shape(placeholder##intputIndex##_shape), FORMAT_ND, intputDtype);     \

    placeholder##intputIndex##_desc.SetPlacement(ge::kPlacementHost);                        \

    placeholder##intputIndex##_desc.SetFormat(FORMAT_ND);                                    \

    Tensor tensor_placeholder##intputIndex;                                                  \

    ret = GenOnesDataFloat32(placeholder##intputIndex##_shape,                               \

        tensor_placeholder##intputIndex,                                                     \

        placeholder##intputIndex##_desc,                                                     \

        2);                                                                                  \

    if (ret != SUCCESS) {                                                                    \

        printf("%s - ERROR - [XIR]: Generate input data failed\n", GetTime().c_str());       \

        return FAILED;                                                                       \

    }                                                                                        \

    placeholder##intputIndex.update_input_desc_x(placeholder##intputIndex##_desc);           \

    input.push_back(tensor_placeholder##intputIndex);                                        \

    graph.AddOp(placeholder##intputIndex);                                                   \

    notequalop.set_input_##intputName(placeholder##intputIndex);                                   \

    inputs.push_back(placeholder##intputIndex)



#define ADD_CONST_INPUT(intputIndex, intputName, intputDtype, inputShape)                    \

    vector<int64_t> placeholder##intputIndex##_shape = inputShape;                           \

    auto placeholder##intputIndex = op::Const("placeholder" + intputIndex);                  \

    TensorDesc placeholder##intputIndex##_desc =                                             \

        TensorDesc(ge::Shape(placeholder##intputIndex##_shape), FORMAT_ND, intputDtype);     \

    placeholder##intputIndex##_desc.SetPlacement(ge::kPlacementHost);                        \

    placeholder##intputIndex##_desc.SetFormat(FORMAT_ND);                                    \

    Tensor tensor_placeholder##intputIndex;                                                  \

    ret = GenOnesData(placeholder##intputIndex##_shape,                                      \

        tensor_placeholder##intputIndex,                                                     \

        placeholder##intputIndex##_desc,                                                     \

        intputDtype,                                                                         \

        2);                                                                                  \

    if (ret != SUCCESS) {                                                                    \

        printf("%s - ERROR - [XIR]: Generate input data failed\n", GetTime().c_str());       \

        return FAILED;                                                                       \

    }                                                                                        \

    placeholder##intputIndex.SetAttr("value", tensor_placeholder##intputIndex);              \

    placeholder##intputIndex.update_output_desc_y(placeholder##intputIndex##_desc);          \

    graph.AddOp(placeholder##intputIndex);                                                   \

    notequalop.set_input_##intputName(placeholder##intputIndex);                                   \

    notequalop.update_input_desc_##intputName(placeholder##intputIndex##_desc);                    \

    inputs.push_back(placeholder##intputIndex)



#define ADD_OUTPUT(outputIndex, outputName, outputDtype, outputShape)                        \

    TensorDesc outputName##outputIndex##_desc =                                              \

        TensorDesc(ge::Shape(outputShape), FORMAT_ND, outputDtype);                          \

    notequalop.update_output_desc_##outputName(outputName##outputIndex##_desc)



#define LOG_PRINT(message, ...)     \

  do {                              \

    printf(message, ##__VA_ARGS__); \

  } while (0)



string GetTime()

{

    time_t timep;

    time(&timep);

    char tmp[64];

    strftime(tmp, sizeof(tmp), "%Y-%m-%d %H:%M:%S,000", localtime(&timep));

    return tmp;

}



uint32_t GetDataTypeSize(DataType dt)

{

    uint32_t dilation = 1;

    uint32_t oneByte = 1;

    uint32_t twoByte = 2;

    uint32_t fourByte = 4;

    uint32_t eightByte = 8;



    if (dt == ge::DT_FLOAT) {

        dilation = fourByte;

    } else if (dt == ge::DT_FLOAT16) {

        dilation = twoByte;

    } else if (dt == ge::DT_BF16) {

        dilation = twoByte;

    } else if (dt == ge::DT_INT16) {

        dilation = twoByte;

    } else if (dt == ge::DT_UINT16) {

        dilation = twoByte;

    } else if (dt == ge::DT_INT32) {

        dilation = fourByte;

    } else if (dt == ge::DT_UINT32) {

        dilation = fourByte;

    } else if (dt == ge::DT_INT64) {

        dilation = eightByte;

    } else if (dt == ge::DT_UINT64) {

        dilation = eightByte;

    } else if (dt == ge::DT_INT8) {

        dilation = oneByte;

    }

    return dilation;

}



int32_t GenOnesDataFloat32(vector<int64_t> shapes, Tensor &input_tensor, TensorDesc &input_tensor_desc, float value)

{

    input_tensor_desc.SetRealDimCnt(shapes.size());

    size_t size = 1;

    for (uint32_t i = 0; i < shapes.size(); i++) {

        size *= shapes[i];

    }

    uint32_t byteSizeFloat32 = 4;

    uint32_t data_len = size * byteSizeFloat32;

    float *pData = new (std::nothrow) float[size];



    for (size_t i = 0; i < size; ++i) {

        *(pData + i) = value;

    }

    input_tensor = Tensor(input_tensor_desc, (uint8_t *)pData, data_len);

    return SUCCESS;

}



int32_t GenOnesData(

    vector<int64_t> shapes, Tensor &input_tensor, TensorDesc &input_tensor_desc, DataType data_type, int value)

{

    input_tensor_desc.SetRealDimCnt(shapes.size());

    size_t size = 1;

    for (uint32_t i = 0; i < shapes.size(); i++) {

        size *= shapes[i];

    }

    uint32_t data_len = size * GetDataTypeSize(data_type);

    int32_t *pData = new (std::nothrow) int32_t[data_len];

    for (uint32_t i = 0; i < size; ++i) {

        *(pData + i) = value;

    }

    input_tensor = Tensor(input_tensor_desc, reinterpret_cast<uint8_t *>(pData), data_len);

    return SUCCESS;

}



int32_t WriteDataToFile(string bin_file, uint64_t data_size, uint8_t *inputData)

{

    FILE *fp = fopen(bin_file.c_str(), "w");

    fwrite(inputData, sizeof(uint8_t), data_size, fp);

    fclose(fp);

    return SUCCESS;

}



int CreateOppInGraph(DataType inDtype, std::vector<ge::Tensor> &input, std::vector<Operator> &inputs,

    std::vector<Operator> &outputs, Graph &graph)

{

    Status ret = SUCCESS;

    // 自定义代码:添加单算子定义到图中

    auto notequalop = op::NotEqual("notequalop");

    std::vector<int64_t> xShape = {4, 4};

    ADD_INPUT(1, x1, inDtype, xShape);

    ADD_INPUT(2, x2, inDtype, xShape);

    ADD_OUTPUT(1, y, DT_BOOL, xShape);



    outputs.push_back(notequalop);

    // 添加完毕

    return SUCCESS;

}



int main(int argc, char *argv[])

{

    const char *graph_name = "tc_ge_irrun_test";

    Graph graph(graph_name);

    std::vector<ge::Tensor> input;



    printf("%s - INFO - [XIR]: Start to initialize ge using ge global options\n", GetTime().c_str());

    std::map<AscendString, AscendString> global_options = {{"ge.exec.deviceId", "0"}, {"ge.graphRunMode", "1"}};

    Status ret = ge::GEInitialize(global_options);

    if (ret != SUCCESS) {

        printf("%s - INFO - [XIR]: Initialize ge using ge global options failed\n", GetTime().c_str());

        return FAILED;

    }

    printf("%s - INFO - [XIR]: Initialize ge using ge global options success\n", GetTime().c_str());



    std::vector<Operator> inputs{};

    std::vector<Operator> outputs{};



    std::cout << argv[1] << std::endl;



    DataType inDtype = DT_FLOAT;

    std::cout << inDtype << std::endl;



    ret = CreateOppInGraph(inDtype, input, inputs, outputs, graph);

    if (ret != SUCCESS) {

        printf("%s - ERROR - [XIR]: Create ir session using build options failed\n", GetTime().c_str());

        return FAILED;

    }



    if (!inputs.empty() && !outputs.empty()) {

        graph.SetInputs(inputs).SetOutputs(outputs);

    }



    std::map<AscendString, AscendString> build_options = {



    };

    printf("%s - INFO - [XIR]: Start to create ir session using build options\n", GetTime().c_str());

    ge::Session *session = new Session(build_options);



    if (session == nullptr) {

        printf("%s - ERROR - [XIR]: Create ir session using build options failed\n", GetTime().c_str());

        return FAILED;

    }

    printf("%s - INFO - [XIR]: Create ir session using build options success\n", GetTime().c_str());

    printf("%s - INFO - [XIR]: Start to add compute graph to ir session\n", GetTime().c_str());



    std::map<AscendString, AscendString> graph_options = {



    };

    uint32_t graph_id = 0;

    ret = session->AddGraph(graph_id, graph, graph_options);



    printf("%s - INFO - [XIR]: Session add ir compute graph to ir session success\n", GetTime().c_str());

    printf("%s - INFO - [XIR]: dump graph to txt\n", GetTime().c_str());

    std::string file_path = "./dump";

    aclgrphDumpGraph(graph, file_path.c_str(), file_path.length());

    printf("%s - INFO - [XIR]: Start to run ir compute graph\n", GetTime().c_str());

    std::vector<ge::Tensor> output;

    ret = session->RunGraph(graph_id, input, output);

    if (ret != SUCCESS) {

        printf("%s - INFO - [XIR]: Run graph failed\n", GetTime().c_str());

        delete session;

        GEFinalize();

        return FAILED;

    }

    printf("%s - INFO - [XIR]: Session run ir compute graph success\n", GetTime().c_str());



    int input_num = input.size();

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

        std::cout << "input " << i << " dtype :  " << input[i].GetTensorDesc().GetDataType() << std::endl;

        string input_file = "./tc_ge_irrun_test_0008_npu_input_" + std::to_string(i) + ".bin";

        uint8_t *input_data_i = input[i].GetData();

        int64_t input_shape = input[i].GetTensorDesc().GetShape().GetShapeSize();

        std::cout << "this is " << i << "th input, input shape size =" << input_shape << std::endl;

        uint32_t data_size = input_shape * GetDataTypeSize(input[i].GetTensorDesc().GetDataType());

        WriteDataToFile((const char *)input_file.c_str(), data_size, input_data_i);

    }



    int output_num = output.size();

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

        std::cout << "output " << i << " dtype :  " << output[i].GetTensorDesc().GetDataType() << std::endl;

        string output_file = "./tc_ge_irrun_test_0008_npu_output_" + std::to_string(i) + ".bin";

        uint8_t *output_data_i = output[i].GetData();

        int64_t output_shape = output[i].GetTensorDesc().GetShape().GetShapeSize();

        std::cout << "this is " << i << "th output, output shape size =" << output_shape << std::endl;

        uint32_t data_size = output_shape * GetDataTypeSize(output[i].GetTensorDesc().GetDataType());

        WriteDataToFile((const char *)output_file.c_str(), data_size, output_data_i);

        uint8_t *resultData = (uint8_t*)output_data_i;

        for (int64_t j = 0; j < output_shape; j++) {

            LOG_PRINT("result[%ld] is: %u\n", j, resultData[j]);

        }

    }



    ge::AscendString error_msg = ge::GEGetErrorMsgV2();

    std::string error_str(error_msg.GetString());

    std::cout << "Error message: " << error_str << std::endl;

    ge::AscendString warning_msg = ge::GEGetWarningMsgV2();

    std::string warning_str(warning_msg.GetString());

    std::cout << "Warning message: " << warning_str << std::endl;

    printf("%s - INFO - [XIR]: Start to finalize ir graph session\n", GetTime().c_str());

    ret = ge::GEFinalize();

    if (ret != SUCCESS) {

        printf("%s - INFO - [XIR]: Finalize ir graph session failed\n", GetTime().c_str());

        return FAILED;

    }

    printf("%s - INFO - [XIR]: Finalize ir graph session success\n", GetTime().c_str());

    return SUCCESS;

}