/*
 * Copyright (c) 2017-2018 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * 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.
 */

#include "utils/GraphUtils.h"

#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/SubTensor.h"
#include "utils/Utils.h"

#include <iomanip>

using namespace arm_compute::graph_utils;

namespace
{
std::pair<arm_compute::TensorShape, arm_compute::PermutationVector> compute_permutation_paramaters(const arm_compute::TensorShape &shape,
 arm_compute::DataLayout data_layout)
{
 // Set permutation parameters if needed
 arm_compute::TensorShape permuted_shape = shape;
 arm_compute::PermutationVector perm;
 // Permute only if num_dimensions greater than 2
 if(shape.num_dimensions() > 2)
 {
 perm = (data_layout == arm_compute::DataLayout::NHWC) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);

 arm_compute::PermutationVector perm_shape = (data_layout == arm_compute::DataLayout::NCHW) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U);
 arm_compute::permute(permuted_shape, perm_shape);
 }

 return std::make_pair(permuted_shape, perm);
}
} // namespace

void TFPreproccessor::preprocess(ITensor &tensor)
{
 Window window;
 window.use_tensor_dimensions(tensor.info()->tensor_shape());

 execute_window_loop(window, [&](const Coordinates & id)
 {
 const float value = *reinterpret_cast<float *>(tensor.ptr_to_element(id));
 float res = value / 255.f; // Normalize to [0, 1]
 res = (res - 0.5f) * 2.f; // Map to [-1, 1]
 *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = res;
 });
}

CaffePreproccessor::CaffePreproccessor(std::array<float, 3> mean, bool bgr)
 : _mean(mean), _bgr(bgr)
{
 if(_bgr)
 {
 std::swap(_mean[0], _mean[2]);
 }
}

void CaffePreproccessor::preprocess(ITensor &tensor)
{
 Window window;
 window.use_tensor_dimensions(tensor.info()->tensor_shape());

 execute_window_loop(window, [&](const Coordinates & id)
 {
 const float value = *reinterpret_cast<float *>(tensor.ptr_to_element(id)) - _mean[id.z()];
 *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = value;
 });
}

PPMWriter::PPMWriter(std::string name, unsigned int maximum)
 : _name(std::move(name)), _iterator(0), _maximum(maximum)
{
}

bool PPMWriter::access_tensor(ITensor &tensor)
{
 std::stringstream ss;
 ss << _name << _iterator << ".ppm";

 arm_compute::utils::save_to_ppm(tensor, ss.str());

 _iterator++;
 if(_maximum == 0)
 {
 return true;
 }
 return _iterator < _maximum;
}

DummyAccessor::DummyAccessor(unsigned int maximum)
 : _iterator(0), _maximum(maximum)
{
}

bool DummyAccessor::access_tensor(ITensor &tensor)
{
 ARM_COMPUTE_UNUSED(tensor);
 bool ret = _maximum == 0 || _iterator < _maximum;
 if(_iterator == _maximum)
 {
 _iterator = 0;
 }
 else
 {
 _iterator++;
 }
 return ret;
}

NumPyAccessor::NumPyAccessor(std::string npy_path, TensorShape shape, DataType data_type, std::ostream &output_stream)
 : _npy_tensor(), _filename(std::move(npy_path)), _output_stream(output_stream)
{
 NumPyBinLoader loader(_filename);

 TensorInfo info(shape, 1, data_type);
 _npy_tensor.allocator()->init(info);
 _npy_tensor.allocator()->allocate();

 loader.access_tensor(_npy_tensor);
}

template <typename T>
void NumPyAccessor::access_numpy_tensor(ITensor &tensor)
{
 const int num_elements = tensor.info()->total_size();
 int num_mismatches = utils::compare_tensor<T>(tensor, _npy_tensor);
 float percentage_mismatches = static_cast<float>(num_mismatches) / num_elements;

 _output_stream << "Results: " << 100.f - (percentage_mismatches * 100) << " % matches with the provided output[" << _filename << "]." << std::endl;
}

bool NumPyAccessor::access_tensor(ITensor &tensor)
{
 ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32);
 ARM_COMPUTE_ERROR_ON(_npy_tensor.info()->dimension(0) != tensor.info()->dimension(0));

 switch(tensor.info()->data_type())
 {
 case DataType::F32:
 access_numpy_tensor<float>(tensor);
 break;
 default:
 ARM_COMPUTE_ERROR("NOT SUPPORTED!");
 }

 return false;
}

PPMAccessor::PPMAccessor(std::string ppm_path, bool bgr, std::unique_ptr<IPreprocessor> preprocessor)
 : _ppm_path(std::move(ppm_path)), _bgr(bgr), _preprocessor(std::move(preprocessor))
{
}

bool PPMAccessor::access_tensor(ITensor &tensor)
{
 utils::PPMLoader ppm;

 // Open PPM file
 ppm.open(_ppm_path);

 // Get permutated shape and permutation parameters
 TensorShape permuted_shape = tensor.info()->tensor_shape();
 arm_compute::PermutationVector perm;
 if(tensor.info()->data_layout() != DataLayout::NCHW)
 {
 std::tie(permuted_shape, perm) = compute_permutation_paramaters(tensor.info()->tensor_shape(), tensor.info()->data_layout());
 }
 ARM_COMPUTE_ERROR_ON_MSG(ppm.width() != permuted_shape.x() || ppm.height() != permuted_shape.y(),
 "Failed to load image file: dimensions [%d,%d] not correct, expected [%d,%d].", ppm.width(), ppm.height(), permuted_shape.x(), permuted_shape.y());

 // Fill the tensor with the PPM content (BGR)
 ppm.fill_planar_tensor(tensor, _bgr);

 // Preprocess tensor
 if(_preprocessor)
 {
 _preprocessor->preprocess(tensor);
 }

 return true;
}

TopNPredictionsAccessor::TopNPredictionsAccessor(const std::string &labels_path, size_t top_n, std::ostream &output_stream)
 : _labels(), _output_stream(output_stream), _top_n(top_n)
{
 _labels.clear();

 std::ifstream ifs;

 try
 {
 ifs.exceptions(std::ifstream::badbit);
 ifs.open(labels_path, std::ios::in | std::ios::binary);

 for(std::string line; !std::getline(ifs, line).fail();)
 {
 _labels.emplace_back(line);
 }
 }
 catch(const std::ifstream::failure &e)
 {
 ARM_COMPUTE_ERROR("Accessing %s: %s", labels_path.c_str(), e.what());
 }
}

template <typename T>
void TopNPredictionsAccessor::access_predictions_tensor(ITensor &tensor)
{
 // Get the predicted class
 std::vector<T> classes_prob;
 std::vector<size_t> index;

 const auto output_net = reinterpret_cast<T *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes());
 const size_t num_classes = tensor.info()->dimension(0);

 classes_prob.resize(num_classes);
 index.resize(num_classes);

 std::copy(output_net, output_net + num_classes, classes_prob.begin());

 // Sort results
 std::iota(std::begin(index), std::end(index), static_cast<size_t>(0));
 std::sort(std::begin(index), std::end(index),
 [&](size_t a, size_t b)
 {
 return classes_prob[a] > classes_prob[b];
 });

 _output_stream << "---------- Top " << _top_n << " predictions ----------" << std::endl
 << std::endl;
 for(size_t i = 0; i < _top_n; ++i)
 {
 _output_stream << std::fixed << std::setprecision(4)
 << +classes_prob[index.at(i)]
 << " - [id = " << index.at(i) << "]"
 << ", " << _labels[index.at(i)] << std::endl;
 }
}

bool TopNPredictionsAccessor::access_tensor(ITensor &tensor)
{
 ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32, DataType::QASYMM8);
 ARM_COMPUTE_ERROR_ON(_labels.size() != tensor.info()->dimension(0));

 switch(tensor.info()->data_type())
 {
 case DataType::QASYMM8:
 access_predictions_tensor<uint8_t>(tensor);
 break;
 case DataType::F32:
 access_predictions_tensor<float>(tensor);
 break;
 default:
 ARM_COMPUTE_ERROR("NOT SUPPORTED!");
 }

 return false;
}

RandomAccessor::RandomAccessor(PixelValue lower, PixelValue upper, std::random_device::result_type seed)
 : _lower(lower), _upper(upper), _seed(seed)
{
}

template <typename T, typename D>
void RandomAccessor::fill(ITensor &tensor, D &&distribution)
{
 std::mt19937 gen(_seed);

 if(tensor.info()->padding().empty() && (dynamic_cast<SubTensor *>(&tensor) == nullptr))
 {
 for(size_t offset = 0; offset < tensor.info()->total_size(); offset += tensor.info()->element_size())
 {
 const T value = distribution(gen);
 *reinterpret_cast<T *>(tensor.buffer() + offset) = value;
 }
 }
 else
 {
 // If tensor has padding accessing tensor elements through execution window.
 Window window;
 window.use_tensor_dimensions(tensor.info()->tensor_shape());

 execute_window_loop(window, [&](const Coordinates & id)
 {
 const T value = distribution(gen);
 *reinterpret_cast<T *>(tensor.ptr_to_element(id)) = value;
 });
 }
}

bool RandomAccessor::access_tensor(ITensor &tensor)
{
 switch(tensor.info()->data_type())
 {
 case DataType::U8:
 {
 std::uniform_int_distribution<uint8_t> distribution_u8(_lower.get<uint8_t>(), _upper.get<uint8_t>());
 fill<uint8_t>(tensor, distribution_u8);
 break;
 }
 case DataType::S8:
 case DataType::QS8:
 {
 std::uniform_int_distribution<int8_t> distribution_s8(_lower.get<int8_t>(), _upper.get<int8_t>());
 fill<int8_t>(tensor, distribution_s8);
 break;
 }
 case DataType::U16:
 {
 std::uniform_int_distribution<uint16_t> distribution_u16(_lower.get<uint16_t>(), _upper.get<uint16_t>());
 fill<uint16_t>(tensor, distribution_u16);
 break;
 }
 case DataType::S16:
 case DataType::QS16:
 {
 std::uniform_int_distribution<int16_t> distribution_s16(_lower.get<int16_t>(), _upper.get<int16_t>());
 fill<int16_t>(tensor, distribution_s16);
 break;
 }
 case DataType::U32:
 {
 std::uniform_int_distribution<uint32_t> distribution_u32(_lower.get<uint32_t>(), _upper.get<uint32_t>());
 fill<uint32_t>(tensor, distribution_u32);
 break;
 }
 case DataType::S32:
 {
 std::uniform_int_distribution<int32_t> distribution_s32(_lower.get<int32_t>(), _upper.get<int32_t>());
 fill<int32_t>(tensor, distribution_s32);
 break;
 }
 case DataType::U64:
 {
 std::uniform_int_distribution<uint64_t> distribution_u64(_lower.get<uint64_t>(), _upper.get<uint64_t>());
 fill<uint64_t>(tensor, distribution_u64);
 break;
 }
 case DataType::S64:
 {
 std::uniform_int_distribution<int64_t> distribution_s64(_lower.get<int64_t>(), _upper.get<int64_t>());
 fill<int64_t>(tensor, distribution_s64);
 break;
 }
 case DataType::F16:
 {
 std::uniform_real_distribution<float> distribution_f16(_lower.get<float>(), _upper.get<float>());
 fill<float>(tensor, distribution_f16);
 break;
 }
 case DataType::F32:
 {
 std::uniform_real_distribution<float> distribution_f32(_lower.get<float>(), _upper.get<float>());
 fill<float>(tensor, distribution_f32);
 break;
 }
 case DataType::F64:
 {
 std::uniform_real_distribution<double> distribution_f64(_lower.get<double>(), _upper.get<double>());
 fill<double>(tensor, distribution_f64);
 break;
 }
 default:
 ARM_COMPUTE_ERROR("NOT SUPPORTED!");
 }
 return true;
}

NumPyBinLoader::NumPyBinLoader(std::string filename, DataLayout file_layout)
 : _filename(std::move(filename)), _file_layout(file_layout)
{
}

bool NumPyBinLoader::access_tensor(ITensor &tensor)
{
 const TensorShape tensor_shape = tensor.info()->tensor_shape();
 std::vector<unsigned long> shape;

 // Open file
 std::ifstream stream(_filename, std::ios::in | std::ios::binary);
 ARM_COMPUTE_ERROR_ON_MSG(!stream.good(), "Failed to load binary data");
 std::string header = npy::read_header(stream);

 // Parse header
 bool fortran_order = false;
 std::string typestr;
 npy::parse_header(header, typestr, fortran_order, shape);

 // Check if the typestring matches the given one
 std::string expect_typestr = arm_compute::utils::get_typestring(tensor.info()->data_type());
 ARM_COMPUTE_ERROR_ON_MSG(typestr != expect_typestr, "Typestrings mismatch");

 // Reverse vector in case of non fortran order
 if(!fortran_order)
 {
 std::reverse(shape.begin(), shape.end());
 }

 // Correct dimensions (Needs to match TensorShape dimension corrections)
 if(shape.size() != tensor_shape.num_dimensions())
 {
 for(int i = static_cast<int>(shape.size()) - 1; i > 0; --i)
 {
 if(shape[i] == 1)
 {
 shape.pop_back();
 }
 else
 {
 break;
 }
 }
 }

 bool are_layouts_different = (_file_layout != tensor.info()->data_layout());

 // Validate tensor ranks
 ARM_COMPUTE_ERROR_ON_MSG(shape.size() != tensor_shape.num_dimensions(), "Tensor ranks mismatch");

 // Set permutation parameters if needed
 TensorShape permuted_shape = tensor_shape;
 arm_compute::PermutationVector perm;
 if(are_layouts_different)
 {
 std::tie(permuted_shape, perm) = compute_permutation_paramaters(tensor_shape, tensor.info()->data_layout());
 }

 // Validate shapes
 for(size_t i = 0; i < shape.size(); ++i)
 {
 ARM_COMPUTE_ERROR_ON_MSG(permuted_shape[i] != shape[i], "Tensor dimensions mismatch");
 }

 // Validate shapes and copy tensor
 if(!are_layouts_different || perm.num_dimensions() <= 2)
 {
 // Read data
 if(tensor.info()->padding().empty() && (dynamic_cast<SubTensor *>(&tensor) == nullptr))
 {
 // If tensor has no padding read directly from stream.
 stream.read(reinterpret_cast<char *>(tensor.buffer()), tensor.info()->total_size());
 }
 else
 {
 // If tensor has padding accessing tensor elements through execution window.
 Window window;
 window.use_tensor_dimensions(tensor_shape);

 execute_window_loop(window, [&](const Coordinates & id)
 {
 stream.read(reinterpret_cast<char *>(tensor.ptr_to_element(id)), tensor.info()->element_size());
 });
 }
 }
 else
 {
 // If tensor has padding accessing tensor elements through execution window.
 Window window;
 window.use_tensor_dimensions(permuted_shape);

 execute_window_loop(window, [&](const Coordinates & id)
 {
 Coordinates coords(id);
 arm_compute::permute(coords, perm);
 stream.read(reinterpret_cast<char *>(tensor.ptr_to_element(coords)), tensor.info()->element_size());
 });
 }
 return true;
}