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 /*
  * Copyright (c) 2025 Advanced Micro Devices, Inc. All rights reserved.
  * 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.
  */
  
 #pragma once
  
 #include <hip/hip_runtime.h>
  
 #include <vector>
  
 #include "core/detail/type_traits.hpp"
 #include "core/tensor.hpp"
  
 namespace roccv {
  
 template <typename T>
 class ImageWrapper {
    public:
     using ValueType = T;
     using BaseType = detail::BaseType<T>;
  
     ImageWrapper(const Tensor& tensor) {
         if (tensor.layout() != eTensorLayout::TENSOR_LAYOUT_NHWC &&
             tensor.layout() != eTensorLayout::TENSOR_LAYOUT_NCHW &&
             tensor.layout() != eTensorLayout::TENSOR_LAYOUT_HWC) {
             throw Exception("The given tensor layout is not supported for ImageWrapper", eStatusType::NOT_IMPLEMENTED);
         }
  
         // Copy tensor data into image tensor descriptor
         TensorDataStrided tdata = tensor.exportData<TensorDataStrided>();
         ImageShape indexes = {tensor.layout().batch_index(), tensor.layout().height_index(),
                               tensor.layout().width_index(), tensor.layout().channels_index()};
  
         // Handle HWC layout, which doesn't have shapes/strides for the batch dimension. We set the batch shape to 1 and
         // the strides to 0.
         int64_t num_batches = indexes.n < 0 ? 1 : tensor.shape(indexes.n);
         int64_t batch_stride = indexes.n < 0 ? 0 : tdata.stride(indexes.n);
  
         shape = {num_batches, tdata.shape(indexes.h), tdata.shape(indexes.w), tdata.shape(indexes.c)};
         stride = {batch_stride, tdata.stride(indexes.h), tdata.stride(indexes.w), tdata.stride(indexes.c)};
         data = static_cast<unsigned char*>(tdata.basePtr());
     }
  
     ImageWrapper(std::vector<BaseType>& input, int32_t batchSize, int32_t width, int32_t height) {
         // Calculate strides based on input (byte-wise strides)
         stride.c = sizeof(BaseType);
         stride.w = stride.c * detail::NumElements<T>;
         stride.h = stride.w * width;
         stride.n = stride.h * height;
  
         // Copy shape information
         shape.c = detail::NumElements<T>;
         shape.w = width;
         shape.h = height;
         shape.n = batchSize;
  
         // Copy data pointer from input vector
         data = reinterpret_cast<unsigned char*>(input.data());
     }
  
     ImageWrapper(void* input, int32_t batchSize, int32_t width, int32_t height) {
         // Calculate strides based on input (byte-wise strides)
         stride.c = sizeof(BaseType);
         stride.w = stride.c * detail::NumElements<T>;
         stride.h = stride.w * width;
         stride.n = stride.h * height;
  
         // Copy shape information
         shape.c = detail::NumElements<T>;
         shape.w = width;
         shape.h = height;
         shape.n = batchSize;
  
         data = reinterpret_cast<unsigned char*>(input);
     }
  
     __device__ __host__ T& at(int64_t n, int64_t h, int64_t w, int64_t c) {
         return *(reinterpret_cast<T*>(data + (stride.n * n) + (stride.h * h) + (stride.w * w) + (stride.c * c)));
     }
  
     __device__ __host__ const T at(int64_t n, int64_t h, int64_t w, int64_t c) const {
         return *(reinterpret_cast<T*>(data + (stride.n * n) + (stride.h * h) + (stride.w * w) + (stride.c * c)));
     }
  
     __device__ __host__ inline int64_t height() const { return shape.h; }
  
     __device__ __host__ inline int64_t width() const { return shape.w; }
  
     __device__ __host__ inline int64_t batches() const { return shape.n; }
  
     __device__ __host__ inline int64_t channels() const { return shape.c; }
  
    private:
     struct ImageShape {
         int64_t n, h, w, c;
     };
  
     ImageShape shape;
  
     ImageShape stride;
  
     unsigned char* data;
 };
 }  // namespace roccv