/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License. */

#include "tensor_util.h"
#include <algorithm>
#include <limits>
#include <vector>

namespace paddle_mobile {
    namespace framework {

        void TensorCopy(const Tensor &src, Tensor *dst) {
            //  VLOG(3) << "TensorCopy " << src.dims() << " from " <<
            //  src.place() << " to
            //  "
            //          << dst_place;
            src.check_memory_size();

            dst->Resize(src.dims());
            dst->set_layout(src.layout());
            auto src_ptr = src.data<void>();

            auto dst_ptr = dst->mutable_data(src.type());

            auto size = src.numel() * SizeOfType(src.type());

            memory::Copy(dst_ptr, src_ptr, size);
        }

        void TensorCopySync(const Tensor &src, Tensor *dst) {
            //  VLOG(3) << "TensorCopySync " << src.dims() << " from " <<
            //  src.place()
            //          << " to " << dst_place;
            src.check_memory_size();
            dst->Resize(src.dims());
            dst->set_layout(src.layout());
            auto src_ptr = src.data<void>();
            auto dst_ptr = dst->mutable_data(src.type());
            auto size = src.numel() * SizeOfType(src.type());
            memory::Copy(dst_ptr, src_ptr, size);
        }

        template <typename Predicate> struct AnyDTypeVisitor {
            Predicate predicate_;
            const Tensor &tensor_;
            Tensor *out_;

            AnyDTypeVisitor(Predicate predicate, const Tensor &tensor,
                            Tensor *out)
                : predicate_(predicate), tensor_(tensor), out_(out) {}

            template <typename T> void operator()() const {
                //    auto t = EigenVector<T>::Flatten(tensor_);
                //    auto o = EigenScalar<bool>::From(*out_);
                // return any of predicate_(t) is true.
                //    o.device(*ctx_.eigen_device()) = predicate_(t).any();
            }
        };

        template <typename Predicate>
        inline void AnyImpl(Predicate predicate, const Tensor &tensor,
                            framework::Tensor *out) {
            VisitDataType(ToDataType(tensor.type()),
                          AnyDTypeVisitor<Predicate>(predicate, tensor, out));
        }

        template <typename Predicate> struct AnyVisitor {
            const framework::Tensor &tensor_;
            Predicate predicate_;

            AnyVisitor(const framework::Tensor &tensor, Predicate predicate)
                : tensor_(tensor), predicate_(std::move(predicate)) {}

            bool operator()(void) const {
                framework::Tensor out;
                out.Resize({1});
                out.mutable_data<bool>();
                AnyImpl(predicate_, tensor_, &out);
                return this->GetResult(out);
            }

            bool GetResult(const framework::Tensor &out) const {
                return *out.data<bool>();
            }
        };

        template <typename Predicate>
        inline bool Any(const framework::Tensor &tensor, Predicate predicate) {
            AnyVisitor<Predicate> visitor(tensor, predicate);
            //  return platform::VisitPlace(visitor);
            return visitor();
        }

        struct ContainsNANPredicate {
            template <typename T>
            auto operator()(const T &eigen_vec) const
                -> decltype(std::declval<T>().isnan()) {
                // Cast eigen_vector to vector of bool. true if is inf.
                return eigen_vec.isnan();
            }
        };

        bool TensorContainsNAN(const framework::Tensor &tensor) {
            ContainsNANPredicate predicate;
            return Any(tensor, predicate);
        }

        struct ContainsInfPredicate {
            template <typename T>
            auto operator()(const T &eigen_vec) const
                -> decltype(std::declval<T>().isinf()) {
                // Cast eigen_vector to vector of bool. true if is inf.
                return eigen_vec.isinf();
            }
        };

        bool TensorContainsInf(const framework::Tensor &tensor) {
            ContainsInfPredicate predicate;
            return Any(tensor, predicate);
        }

        void TensorToStream(std::ostream &os, const Tensor &tensor) {
            { // the 1st field, uint32_t version
                constexpr uint32_t version = 0;
                os.write(reinterpret_cast<const char *>(&version),
                         sizeof(version));
            }
            { // the 2nd field, tensor description
                // int32_t  size
                // void*    protobuf message
                proto::VarType::TensorDesc desc;
                desc.set_data_type(framework::ToDataType(tensor.type()));
                auto dims = framework::vectorize(tensor.dims());
                auto *pb_dims = desc.mutable_dims();
                pb_dims->Resize(static_cast<int>(dims.size()), 0);
                std::copy(dims.begin(), dims.end(), pb_dims->begin());
                int32_t size = desc.ByteSize();
                os.write(reinterpret_cast<const char *>(&size), sizeof(size));
                auto out = desc.SerializeAsString();
                os.write(out.data(), size);
            }
            { // the 3rd field, tensor data
                uint64_t size = tensor.memory_size();
                auto *data_ptr = tensor.data<void>();
                //    PADDLE_ENFORCE(size <
                //    std::numeric_limits<std::streamsize>::max(),
                //                   "Index overflow when writing tensor");

                os.write(static_cast<const char *>(data_ptr),
                         static_cast<std::streamsize>(size));
            }
        }

        struct DeserializedDataFunctor {
            DeserializedDataFunctor(void **buf, Tensor *tensor)
                : buf_(buf), tensor_(tensor) {}

            template <typename T> void operator()() {
                *buf_ = tensor_->mutable_data<T>();
            }

            void **buf_;
            Tensor *tensor_;
        };

        void TensorFromStream(std::istream &is, framework::Tensor *tensor) {
            uint32_t version;
            is.read(reinterpret_cast<char *>(&version), sizeof(version));
            //  PADDLE_ENFORCE_EQ(version, 0U, "Only version 0 is supported");
            proto::VarType::TensorDesc desc;
            { // int32_t size
                // proto buffer
                int32_t size;
                is.read(reinterpret_cast<char *>(&size), sizeof(size));
                std::unique_ptr<char[]> buf(new char[size]);
                is.read(reinterpret_cast<char *>(buf.get()), size);
                //    PADDLE_ENFORCE(desc.ParseFromArray(buf.get(), size),
                //                   "Cannot parse tensor desc");
            }
            { // read tensor
                std::vector<int64_t> dims;
                dims.reserve(static_cast<size_t>(desc.dims().size()));
                std::copy(desc.dims().begin(), desc.dims().end(),
                          std::back_inserter(dims));
                tensor->Resize(framework::make_ddim(dims));
                void *buf;

                framework::VisitDataType(desc.data_type(),
                                         DeserializedDataFunctor(&buf, tensor));
                is.read(static_cast<char *>(buf), tensor->memory_size());
            }
        }

    } // namespace framework
} // namespace paddle_mobile