#include "../tensor.hpp"
#include "../utils.hpp"
#include <algorithm>
#include <fstream>
#include <iostream>
#include <mutex>
#include <numeric>
#include <sstream>

std::shared_ptr<TensorDesc>
TensorDesc::create(infiniDtype_t dtype, const std::vector<size_t> &shape,
                   const std::vector<ptrdiff_t> &strides) {
    return std::shared_ptr<TensorDesc>(new TensorDesc(dtype, shape, strides));
}

std::shared_ptr<TensorDesc>
TensorDesc::create(infiniDtype_t dtype, const std::vector<size_t> &shape) {
    auto ndim = shape.size();
    auto strides = std::vector<ptrdiff_t>(ndim);
    if (ndim > 0) {
        strides[ndim - 1] = 1;
        for (int i = ndim - 2; i >= 0; i--) {
            strides[i] = strides[i + 1] * shape[i + 1];
        }
    }
    return create(dtype, shape, strides);
}

std::shared_ptr<TensorDesc>
TensorDesc::createWithOrder(infiniDtype_t dtype, const std::vector<size_t> &shape,
                            const std::vector<size_t> &order) {
    ASSERT_EQ(shape.size(), order.size());
    auto ndim = shape.size();
    if (ndim == 0) {
        return create(dtype, shape);
    }
    auto strides = std::vector<ptrdiff_t>(order.size());
    auto idx = std::find(order.begin(), order.end(), size_t(ndim - 1));
    strides[std::distance(order.begin(), idx)] = 1;
    for (int i = ndim - 2; i >= 0; i--) {
        auto prev_dim = shape[std::distance(order.begin(), idx)];
        auto prev_stride = strides[std::distance(order.begin(), idx)];
        idx = std::find(order.begin(), order.end(), size_t(i));
        strides[std::distance(order.begin(), idx)] = prev_stride * prev_dim;
    }
    return create(dtype, shape, strides);
}

infiniopTensorDescriptor_t TensorDesc::desc() const {
    if (_desc == nullptr) {
        RUN_INFINI(infiniopCreateTensorDescriptor(
            (infiniopTensorDescriptor_t *)(&_desc), _shape.size(), _shape.data(),
            _strides.data(), _dtype));
    }
    return _desc;
};

void TensorDesc::resetDesc() {
    if (this->_desc != nullptr) {
        infiniopDestroyTensorDescriptor(this->_desc);
        this->_desc = nullptr;
    }
}

void TensorDesc::computeTensorDesHash() {
    _seed = 0;
    for (auto dim : this->shape()) {
        hash_combine(_seed, dim);
    }
    for (auto stride : this->strides()) {
        hash_combine(_seed, static_cast<size_t>(stride));
    }
}

bool TensorDesc::isContigous() const {
    auto ndim = this->ndim();
    auto shape = this->shape();
    auto strides = std::vector<ptrdiff_t>(ndim);
    strides[ndim - 1] = 1;
    for (int i = ndim - 2; i >= 0; i--) {
        strides[i] = strides[i + 1] * shape[i + 1];
    }
    ASSERT_EQ(strides.size(), this->_strides.size());
    return std::equal(strides.begin(), strides.end(), this->_strides.begin());
}

std::string TensorDesc::info() const {
    std::stringstream ss;

    ss << "Tensor: "
       << "shape[ ";
    for (auto s : this->shape()) {
        ss << s << " ";
    }
    ss << "] strides[ ";
    for (auto s : this->strides()) {
        ss << s << " ";
    }
    ss << "] dtype=" << this->dtype();

    return ss.str();
}

TensorDesc::~TensorDesc() {
    this->resetDesc();
}

const std::vector<size_t> &Tensor::shape() const { return this->_desc->shape(); }
const std::vector<ptrdiff_t> &Tensor::strides() const { return this->_desc->strides(); }
size_t Tensor::ndim() const { return this->_desc->ndim(); }
infiniDtype_t Tensor::dtype() const { return this->_desc->dtype(); }
infiniDevice_t Tensor::deviceType() const { return this->_storage->deviceType(); }
int Tensor::deviceId() const { return this->_storage->deviceId(); }
Tensor::~Tensor() {}

ptrdiff_t Tensor::dataOffset() const {
    return _offset;
}

infiniopTensorDescriptor_t Tensor::desc() const { return _desc->desc(); }

std::shared_ptr<Tensor> Tensor::buffer(infiniDtype_t dtype,
                                       const std::vector<size_t> &shape,
                                       std::shared_ptr<MemoryPool> pool) {
    std::shared_ptr<Tensor> tensor = std::make_shared<Tensor>();
    auto ndim = shape.size();

    size_t size = std::accumulate(shape.begin(), shape.end(), dsize(dtype), std::multiplies<size_t>());
    auto strides = std::vector<ptrdiff_t>(ndim);
    if (ndim > 0) {
        strides[ndim - 1] = 1;
        for (int i = ndim - 2; i >= 0; i--) {
            strides[i] = strides[i + 1] * shape[i + 1];
        }
    }
    tensor->_storage = Storage::createFromPool(size, pool);
    tensor->_desc = TensorDesc::create(dtype, shape, strides);
    tensor->_offset = 0;
    return tensor;
}

std::shared_ptr<Tensor> Tensor::weight(void *data, infiniDtype_t dtype,
                                       const std::vector<size_t> &shape) {
    std::shared_ptr<Tensor> tensor = std::make_shared<Tensor>();
    auto ndim = shape.size();
    size_t size = std::accumulate(shape.begin(), shape.end(), dsize(dtype), std::multiplies<size_t>());
    auto strides = std::vector<ptrdiff_t>(ndim);
    if (ndim > 0) {
        strides[ndim - 1] = 1;
        for (int i = ndim - 2; i >= 0; i--) {
            strides[i] = strides[i + 1] * shape[i + 1];
        }
    }

    tensor->_storage = Storage::create(size);
    tensor->_desc = TensorDesc::create(dtype, shape, strides);
    // NOTE: 为兼容部分平台（沐曦）多线程并发对同一host数据执行memcpy卡死问题
    static std::mutex mutex;
    {
        std::lock_guard<std::mutex> lock(mutex);
        RUN_INFINI(infinirtMemcpy(tensor->_storage->memory(),
                                  data, size, INFINIRT_MEMCPY_H2D));
    }

    tensor->_offset = 0;
    return tensor;
}

std::shared_ptr<Tensor> Tensor::memShare(const std::vector<size_t> &shape, infiniDtype_t dtype_) const {
    auto dtype = dtype_ == INFINI_DTYPE_INVALID ? this->dtype() : dtype_;
    size_t size = std::accumulate(shape.begin(), shape.end(), dsize(dtype), std::multiplies<size_t>());
    ASSERT(size <= this->_storage->size());

    std::shared_ptr<Tensor> tensor = std::make_shared<Tensor>();
    auto ndim = shape.size();
    auto strides = std::vector<ptrdiff_t>(ndim);
    if (ndim > 0) {
        strides[ndim - 1] = 1;
        for (int i = ndim - 2; i >= 0; i--) {
            strides[i] = strides[i + 1] * shape[i + 1];
        }
    }
    tensor->_storage = this->_storage;
    tensor->_offset = 0;
    tensor->_desc = TensorDesc::create(dtype, shape, strides);
    return tensor;
}

void *Tensor::dataImpl(ptrdiff_t offset) const {
    return (char *)(this->_storage->memory()) + this->_offset + offset * dsize(this->dtype());
}

void *Tensor::data(ptrdiff_t offset) {
    return this->dataImpl(offset);
}

const void *Tensor::data(ptrdiff_t offset) const {
    return this->dataImpl(offset);
}

void Tensor::copyFrom(std::shared_ptr<Tensor const> src,
                      infiniopHandle_t handle, infinirtStream_t stream) {
    ASSERT_EQ(this->shape(), src->shape());
    ASSERT_EQ(this->dtype(), src->dtype());
    infiniopRearrangeDescriptor_t desc;
    RUN_INFINI(infiniopCreateRearrangeDescriptor(
        handle, &desc, this->desc(), src->desc()));
    RUN_INFINI(infiniopRearrange(desc, this->data(), src->data(),
                                 stream));
    RUN_INFINI(infiniopDestroyRearrangeDescriptor(desc));
}

bool Tensor::isContigous() const {
    return this->_desc->isContigous();
}

template <typename T>
void print_data(T *data, const std::vector<size_t> &shape,
                const std::vector<ptrdiff_t> &strides, size_t dim) {
    if (dim == shape.size() - 1) {
        for (size_t i = 0; i < shape[dim]; i++) {
            std::cout << data[i] << " ";
        }
        std::cout << std::endl;
    } else if (dim < shape.size() - 1) {
        for (size_t i = 0; i < shape[dim]; i++) {
            print_data(data + i * strides[dim], shape, strides, dim + 1);
        }
    }
}

template <>
void print_data(uint16_t const *data, const std::vector<size_t> &shape,
                const std::vector<ptrdiff_t> &strides, size_t dim) {
    if (dim == shape.size() - 1) {
        for (size_t i = 0; i < shape[dim]; i++) {
            std::cout << f16_to_f32(data[i * strides[dim]]) << " ";
        }
        std::cout << std::endl;
    } else if (dim < shape.size() - 1) {
        for (size_t i = 0; i < shape[dim]; i++) {
            print_data(data + i * strides[dim], shape, strides, dim + 1);
        }
    }
}

void print_data_bf16(uint16_t const *data, const std::vector<size_t> &shape,
                     const std::vector<ptrdiff_t> &strides, size_t dim) {
    if (dim == shape.size() - 1) {
        for (size_t i = 0; i < shape[dim]; i++) {
            std::cout << bf16_to_f32(data[i * strides[dim]]) << " ";
        }
        std::cout << std::endl;
    } else if (dim < shape.size() - 1) {
        for (size_t i = 0; i < shape[dim]; i++) {
            print_data(data + i * strides[dim], shape, strides, dim + 1);
        }
    }
}

std::string Tensor::info() const {
    std::stringstream ss;

    ss << "Tensor: "
       << this->_desc->info()
       << " device=" << this->deviceType()
       << " device_id=" << this->deviceId();
    return this->_desc->info();
}

size_t Tensor::seed() const {
    return this->_desc->seed();
}

std::shared_ptr<Tensor> Tensor::view(const std::vector<size_t> &new_shape) const {
    // Step 1: Validate total size
    size_t numel = 1;
    for (size_t dim : this->_desc->shape()) {
        numel *= dim;
    }

    size_t new_numel = 1;
    for (size_t dim : new_shape) {
        new_numel *= dim;
    }

    ASSERT_EQ(numel, new_numel);

    // Step 2: Get current shape and strides
    const std::vector<size_t> &old_shape = this->_desc->shape();
    const std::vector<ptrdiff_t> &old_strides = this->_desc->strides();

    // Step 3: Create merged shape and strides
    std::vector<size_t> merged_shape;
    std::vector<ptrdiff_t> merged_strides;

    if (!old_shape.empty()) {
        merged_shape.push_back(old_shape[0]);
        merged_strides.push_back(old_strides[0]);

        for (size_t i = 1; i < old_shape.size(); ++i) {
            if (old_strides[i] * static_cast<ptrdiff_t>(old_shape[i]) == merged_strides.back()) {
                merged_shape.back() *= old_shape[i];
                merged_strides.back() = old_strides[i];
            } else {
                merged_shape.push_back(old_shape[i]);
                merged_strides.push_back(old_strides[i]);
            }
        }
    }

    // Step 4: Compute new strides by splitting merged dimensions
    std::vector<ptrdiff_t> new_strides(new_shape.size());
    size_t merged_idx = 0;
    ptrdiff_t current_stride = merged_strides[0];
    size_t remaining_size = merged_shape[0];

    for (size_t i = 0; i < new_shape.size(); ++i) {
        // Find which merged dimension contains this new dimension
        while (new_shape[i] > remaining_size) {
            ASSERT(++merged_idx < merged_shape.size());
            current_stride = merged_strides[merged_idx];
            remaining_size = merged_shape[merged_idx];
        }

        ASSERT_EQ(remaining_size % new_shape[i], 0);

        new_strides[i] = current_stride * (remaining_size / new_shape[i]);
        remaining_size /= new_shape[i];
    }

    return this->view_as(new_shape, new_strides);
}

std::shared_ptr<Tensor> Tensor::view_as(const std::vector<size_t> &new_shape) const {
    std::shared_ptr<Tensor> tensor = std::make_shared<Tensor>();
    tensor->_storage = this->_storage;
    tensor->_desc = TensorDesc::create(this->dtype(), new_shape);
    tensor->_offset = this->_offset;
    return tensor;
}

std::shared_ptr<Tensor> Tensor::view_as(const std::vector<size_t> &new_shape, const std::vector<ptrdiff_t> &new_strides) const {
    std::shared_ptr<Tensor> tensor = std::make_shared<Tensor>();
    tensor->_storage = this->_storage;
    tensor->_desc = TensorDesc::create(this->dtype(), new_shape, new_strides);
    tensor->_offset = this->_offset;
    return tensor;
}

void Tensor::debug(const std::string &filename) const {
    RUN_INFINI(infinirtDeviceSynchronize());

    std::cout << info() << std::endl;

    void const *cpu_data;
    if (this->deviceType() != INFINI_DEVICE_CPU) {
        void *cpu_memory = std::malloc(this->_storage->size());
        RUN_INFINI(infinirtMemcpy(cpu_memory, this->_storage->memory(),
                                  this->_storage->size(), INFINIRT_MEMCPY_D2H));
        cpu_data = cpu_memory;
    } else {
        cpu_data = this->_storage->memory();
    }

    if (!filename.empty()) {
        std::ofstream outFile(filename, std::ios::binary);
        if (!outFile) {
            std::cerr << "Error opening file for writing: " << filename << "\n";
            return;
        }
        outFile.write(reinterpret_cast<const char *>(cpu_data), this->_storage->size());
        outFile.close();
        std::cout << "Data written to file: " << filename << "\n";
        return;
    }

    switch (this->dtype()) {
    case INFINI_DTYPE_F16:
        print_data((uint16_t const *)((char const *)cpu_data + dataOffset()),
                   this->shape(), this->strides(), 0);
        break;
    case INFINI_DTYPE_F32:
        print_data((float const *)((char const *)cpu_data + dataOffset()),
                   this->shape(), this->strides(), 0);
        break;
    case INFINI_DTYPE_U64:
        print_data((uint64_t const *)((char const *)cpu_data + dataOffset()),
                   this->shape(), this->strides(), 0);
        break;
    case INFINI_DTYPE_I64:
        print_data((int64_t const *)((char const *)cpu_data + dataOffset()),
                   this->shape(), this->strides(), 0);
        break;
    case INFINI_DTYPE_U32:
        print_data((uint32_t const *)((char const *)cpu_data + dataOffset()),
                   this->shape(), this->strides(), 0);
        break;
    case INFINI_DTYPE_I32:
        print_data((int32_t const *)((char const *)cpu_data + dataOffset()),
                   this->shape(), this->strides(), 0);
        break;
    case INFINI_DTYPE_BF16:
        print_data_bf16((uint16_t const *)((char const *)cpu_data + dataOffset()),
                        this->shape(), this->strides(), 0);
        break;
    default:
        PANIC("Unsupported data type");
    }
}

void Tensor::debug() const { this->debug(""); }