/* Copyright 2015 The TensorFlow 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 "tensorflow/core/public/tensor_c_api.h"

#include <memory>
#include <vector>

#include "tensorflow/core/framework/log_memory.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/lib/core/coding.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"

// The implementation below is at the top level instead of the
// brain namespace because we are defining 'extern "C"' functions.
using tensorflow::error::Code;
using tensorflow::errors::InvalidArgument;
using tensorflow::gtl::ArraySlice;
using tensorflow::AllocationDescription;
using tensorflow::Status;
using tensorflow::DataType;
using tensorflow::Env;
using tensorflow::GraphDef;
using tensorflow::NewSession;
using tensorflow::Session;
using tensorflow::Tensor;
using tensorflow::TensorBuffer;
using tensorflow::SessionOptions;
using tensorflow::RunOptions;
using tensorflow::RunMetadata;
using tensorflow::TensorShape;

extern "C" {

// --------------------------------------------------------------------------
struct TF_Status {
  Status status;
};

struct TF_Library {
  void* lib_handle;
  TF_Buffer op_list;
};

TF_Status* TF_NewStatus() { return new TF_Status; }

void TF_DeleteStatus(TF_Status* s) { delete s; }

void TF_SetStatus(TF_Status* s, TF_Code code, const char* msg) {
  s->status = Status(static_cast<Code>(code), tensorflow::StringPiece(msg));
}

TF_Code TF_GetCode(const TF_Status* s) {
  return static_cast<TF_Code>(s->status.code());
}

const char* TF_Message(const TF_Status* s) {
  return s->status.error_message().c_str();
}

// --------------------------------------------------------------------------

namespace {
class TF_ManagedBuffer : public TensorBuffer {
 public:
  void* data_;
  size_t len_;
  void (*deallocator_)(void* data, size_t len, void* arg);
  void* deallocator_arg_;

  ~TF_ManagedBuffer() override {
    (*deallocator_)(data_, len_, deallocator_arg_);
  }

  void* data() const override { return data_; }
  size_t size() const override { return len_; }
  TensorBuffer* root_buffer() override { return this; }
  void FillAllocationDescription(AllocationDescription* proto) const override {
    tensorflow::int64 rb = size();
    proto->set_requested_bytes(rb);
    proto->set_allocator_name(tensorflow::cpu_allocator()->Name());
  }
};

void deallocate_realigned_buffer(void* data, size_t len, void* arg) {
  if (tensorflow::LogMemory::IsEnabled()) {
    tensorflow::LogMemory::RecordRawDeallocation(
        "TensorFlow C Api",
        tensorflow::LogMemory::EXTERNAL_TENSOR_ALLOCATION_STEP_ID, data,
        tensorflow::cpu_allocator(), false);
  }
  tensorflow::cpu_allocator()->DeallocateRaw(data);
}
}  // namespace

struct TF_Tensor {
  TF_DataType dtype;
  TensorShape shape;
  TensorBuffer* buffer;
};

TF_Tensor* TF_NewTensor(TF_DataType dtype, tensorflow::int64* dims,
                        int num_dims, void* data, size_t len,
                        void (*deallocator)(void* data, size_t len, void* arg),
                        void* deallocator_arg) {
  std::vector<tensorflow::int64> dimvec(num_dims);
  for (int i = 0; i < num_dims; i++) {
    dimvec[i] = dims[i];
  }

  TF_ManagedBuffer* buf = new TF_ManagedBuffer;
  buf->len_ = len;
  if (reinterpret_cast<intptr_t>(data) % EIGEN_MAX_ALIGN_BYTES != 0) {
    // Copy the data into a buffer that satisfies Eigen's alignment
    // requirements.
    buf->data_ =
        tensorflow::cpu_allocator()->AllocateRaw(EIGEN_MAX_ALIGN_BYTES, len);
    if (tensorflow::LogMemory::IsEnabled()) {
      tensorflow::LogMemory::RecordRawAllocation(
          "TF_NewTensor",
          tensorflow::LogMemory::EXTERNAL_TENSOR_ALLOCATION_STEP_ID, len,
          buf->data_, tensorflow::cpu_allocator());
    }
    std::memcpy(buf->data_, data, len);
    buf->deallocator_ = deallocate_realigned_buffer;
    buf->deallocator_arg_ = nullptr;
    // Free the original buffer.
    deallocator(data, len, deallocator_arg);
  } else {
    buf->data_ = data;
    buf->deallocator_ = deallocator;
    buf->deallocator_arg_ = deallocator_arg;
  }
  return new TF_Tensor{dtype, TensorShape(dimvec), buf};
}

void TF_DeleteTensor(TF_Tensor* t) {
  t->buffer->Unref();
  delete t;
}

TF_DataType TF_TensorType(const TF_Tensor* t) { return t->dtype; }
int TF_NumDims(const TF_Tensor* t) { return t->shape.dims(); }
tensorflow::int64 TF_Dim(const TF_Tensor* t, int dim_index) {
  return t->shape.dim_size(dim_index);
}
size_t TF_TensorByteSize(const TF_Tensor* t) { return t->buffer->size(); }
void* TF_TensorData(const TF_Tensor* t) { return t->buffer->data(); }

// --------------------------------------------------------------------------
struct TF_SessionOptions {
  SessionOptions options;
};
TF_SessionOptions* TF_NewSessionOptions() { return new TF_SessionOptions; }
void TF_DeleteSessionOptions(TF_SessionOptions* opt) { delete opt; }

void TF_SetTarget(TF_SessionOptions* options, const char* target) {
  options->options.target = target;
}

void TF_SetConfig(TF_SessionOptions* options, const void* proto,
                  size_t proto_len, TF_Status* status) {
  if (!options->options.config.ParseFromArray(proto, proto_len)) {
    status->status =
        tensorflow::errors::InvalidArgument("Unparseable ConfigProto");
  }
}
// --------------------------------------------------------------------------
TF_Buffer* TF_NewBuffer() { return new TF_Buffer{nullptr, 0, nullptr}; }

TF_Buffer* TF_NewBufferFromString(const void* proto, size_t proto_len) {
  void* copy = malloc(proto_len);
  memcpy(copy, proto, proto_len);

  TF_Buffer* buf = new TF_Buffer;
  buf->data = copy;
  buf->length = proto_len;
  buf->data_deallocator = [](void* data, size_t length) { free(data); };
  return buf;
}

void TF_DeleteBuffer(TF_Buffer* buffer) {
  if (buffer->data_deallocator != nullptr) {
    (*buffer->data_deallocator)(const_cast<void*>(buffer->data),
                                buffer->length);
  }
  delete buffer;
}

TF_Buffer TF_GetBuffer(TF_Buffer* buffer) { return *buffer; }

// --------------------------------------------------------------------------
struct TF_Session {
  Session* session;
};

TF_Session* TF_NewSession(const TF_SessionOptions* opt, TF_Status* status) {
  Session* session;
  status->status = NewSession(opt->options, &session);
  if (status->status.ok()) {
    return new TF_Session({session});
  } else {
    DCHECK_EQ(nullptr, session);
    return NULL;
  }
}

void TF_CloseSession(TF_Session* s, TF_Status* status) {
  status->status = s->session->Close();
}

void TF_DeleteSession(TF_Session* s, TF_Status* status) {
  status->status = Status::OK();
  delete s->session;
  delete s;
}

void TF_ExtendGraph(TF_Session* s, const void* proto, size_t proto_len,
                    TF_Status* status) {
  GraphDef g;
  if (!tensorflow::ParseProtoUnlimited(&g, proto, proto_len)) {
    status->status = tensorflow::errors::InvalidArgument("Invalid GraphDef");
    return;
  }
  status->status = s->session->Extend(g);
}

static void DeleteArray(void* data, size_t size, void* arg) {
  DCHECK_EQ(data, arg);
  delete[] reinterpret_cast<char*>(arg);
}

}  // end extern "C"

namespace tensorflow {

// Non-static for testing.
bool TF_Tensor_DecodeStrings(TF_Tensor* src, Tensor* dst, TF_Status* status) {
  const tensorflow::int64 num_elements = src->shape.num_elements();
  const char* input = reinterpret_cast<const char*>(TF_TensorData(src));
  const size_t src_size = TF_TensorByteSize(src);
  if (static_cast<tensorflow::int64>(src_size / sizeof(tensorflow::uint64)) <
      num_elements) {
    status->status = InvalidArgument(
        "Malformed TF_STRING tensor; too short to hold number of elements");
    return false;
  }
  const char* data_start = input + sizeof(tensorflow::uint64) * num_elements;
  const char* limit = input + src_size;

  *dst = Tensor(static_cast<DataType>(src->dtype), src->shape);
  auto dstarray = dst->flat<tensorflow::string>();
  for (tensorflow::int64 i = 0; i < num_elements; i++) {
    tensorflow::uint64 offset =
        reinterpret_cast<const tensorflow::uint64*>(input)[i];
    tensorflow::uint64 len;
    const char* p;
    if (static_cast<ptrdiff_t>(offset) >= (limit - data_start) ||
        !(p = tensorflow::core::GetVarint64Ptr(data_start + offset, limit,
                                               &len)) ||
        (static_cast<ptrdiff_t>(len) > (limit - p))) {
      status->status = InvalidArgument("Malformed TF_STRING tensor; element ",
                                       i, " out of range");
      return false;
    }
    dstarray(i).assign(p, len);
  }
  return true;
}

// Non-static for testing.
TF_Tensor* TF_Tensor_EncodeStrings(const Tensor& src) {
  // Compute bytes needed for encoding.
  size_t size = 0;
  const auto& srcarray = src.flat<tensorflow::string>();
  for (int i = 0; i < srcarray.size(); i++) {
    const tensorflow::string& s = srcarray(i);
    // uint64 starting_offset, varint64 length, string contents
    size += sizeof(tensorflow::uint64) +
            tensorflow::core::VarintLength(s.size()) + s.size();
  }

  // Encode all strings.
  char* base = new char[size];
  char* data_start = base + sizeof(tensorflow::uint64) * srcarray.size();
  char* dst = data_start;  // Where next string is encoded.
  tensorflow::uint64* offsets = reinterpret_cast<tensorflow::uint64*>(base);
  for (int i = 0; i < srcarray.size(); i++) {
    const tensorflow::string& s = srcarray(i);
    *offsets = (dst - data_start);
    offsets++;
    dst = tensorflow::core::EncodeVarint64(dst, s.size());
    memcpy(dst, s.data(), s.size());
    dst += s.size();
  }
  CHECK_EQ(dst, base + size);

  auto dims = src.shape().dim_sizes();
  std::vector<tensorflow::int64> dimvec(dims.size());
  for (size_t i = 0; i < dims.size(); i++) {
    dimvec[i] = dims[i];
  }
  return TF_NewTensor(TF_STRING, dimvec.data(), dimvec.size(), base, size,
                      DeleteArray, base);
}

class TensorCApi {
 public:
  static TensorBuffer* Buffer(const Tensor& tensor) { return tensor.buf_; }
  static Tensor MakeTensor(TF_DataType type, const TensorShape& shape,
                           TensorBuffer* buf) {
    return Tensor(static_cast<DataType>(type), shape, buf);
  }
};

// Create an empty tensor of type 'dtype'. 'shape' can be arbitrary, but has to
// result in a zero-sized tensor.
static TF_Tensor* EmptyTensor(TF_DataType dtype, const TensorShape& shape) {
  static char empty;
  tensorflow::int64 nelems = 1;
  std::vector<tensorflow::int64> dims;
  for (int i = 0; i < shape.dims(); ++i) {
    dims.push_back(shape.dim_size(i));
    nelems *= shape.dim_size(i);
  }
  CHECK_EQ(nelems, 0);
  return TF_NewTensor(dtype, dims.data(), shape.dims(),
                      reinterpret_cast<void*>(&empty), 0,
                      [](void*, size_t, void*) {}, nullptr);
}

// Helpers for loading a TensorFlow plugin (a .so file).
Status LoadLibrary(const char* library_filename, void** result,
                   const void** buf, size_t* len);

}  // namespace tensorflow

void TF_Run_Helper(TF_Session* s, const char* handle,
                   const TF_Buffer* run_options,
                   // Input tensors
                   const char** c_input_names, TF_Tensor** c_inputs,
                   int ninputs,
                   // Output tensors
                   const char** c_output_tensor_names, TF_Tensor** c_outputs,
                   int noutputs,
                   // Target nodes
                   const char** c_target_node_names, int ntargets,
                   TF_Buffer* run_metadata, TF_Status* status) {
  status->status = Status::OK();
  for (int i = 0; i < noutputs; i++) {
    c_outputs[i] = NULL;
  }

  // Initialize inputs.
  std::vector<std::pair<tensorflow::string, Tensor>> inputs(ninputs);
  bool ok = true;
  for (int i = 0; i < ninputs; i++) {
    TF_Tensor* src = c_inputs[i];
    if (ok) {
      inputs[i].first = c_input_names[i];
      if (c_inputs[i]->dtype != TF_STRING) {
        inputs[i].second = tensorflow::TensorCApi::MakeTensor(
            src->dtype, src->shape, src->buffer);
      } else {
        // TF_STRING tensors require copying since Tensor class expects
        // a sequence of string objects.
        ok =
            tensorflow::TF_Tensor_DecodeStrings(src, &inputs[i].second, status);
        // Must keep looping through all inputs even if there is an error
        // so that TF_DeleteTensor() is called unconditionally on all inputs.
      }
    }
    TF_DeleteTensor(src);
  }
  if (!ok) {
    return;
  }

  std::vector<tensorflow::string> output_tensor_names(noutputs);
  std::vector<Tensor> outputs(noutputs);
  std::vector<tensorflow::string> target_node_names(ntargets);
  for (int i = 0; i < noutputs; i++) {
    output_tensor_names[i] = c_output_tensor_names[i];
  }
  for (int i = 0; i < ntargets; i++) {
    target_node_names[i] = c_target_node_names[i];
  }
  Status result;

  if (handle == nullptr) {
    RunOptions run_options_proto;
    if (run_options != nullptr &&
        !run_options_proto.ParseFromArray(run_options->data,
                                          run_options->length)) {
      status->status =
          tensorflow::errors::InvalidArgument("Unparseable RunOptions proto");
      return;
    }
    if (run_metadata != nullptr && run_metadata->data != nullptr) {
      status->status = tensorflow::errors::InvalidArgument(
          "Passing non-empty run_metadata is invalid.");
      return;
    }

    RunMetadata run_metadata_proto;
    result = s->session->Run(run_options_proto, inputs, output_tensor_names,
                             target_node_names, &outputs, &run_metadata_proto);

    // Serialize back to upstream client, who now owns the new buffer
    if (run_metadata != nullptr) {
      int proto_size = run_metadata_proto.ByteSize();
      void* str_buf = reinterpret_cast<void*>(operator new(proto_size));
      run_metadata_proto.SerializeToArray(str_buf, proto_size);
      run_metadata->data = str_buf;
      run_metadata->length = proto_size;
    }
  } else {
    // NOTE(zongheng): PRun does not support RunOptions yet.
    result = s->session->PRun(handle, inputs, output_tensor_names, &outputs);
  }
  if (!result.ok()) {
    status->status = result;
    return;
  }

  // Store results in c_outputs[]
  for (int i = 0; i < noutputs; i++) {
    const Tensor& src = outputs[i];
    if (!src.IsInitialized()) {
      c_outputs[i] = tensorflow::EmptyTensor(
          static_cast<TF_DataType>(src.dtype()), src.shape());
      continue;
    }
    if (src.dtype() != tensorflow::DT_STRING) {
      // Share the underlying buffer.
      TensorBuffer* buf = tensorflow::TensorCApi::Buffer(src);
      buf->Ref();
      c_outputs[i] = new TF_Tensor{static_cast<TF_DataType>(src.dtype()),
                                   src.shape(), buf};
    } else {
      c_outputs[i] = tensorflow::TF_Tensor_EncodeStrings(src);
    }
  }
}

extern "C" {

void TF_Run(TF_Session* s, const TF_Buffer* run_options,
            // Input tensors
            const char** c_input_names, TF_Tensor** c_inputs, int ninputs,
            // Output tensors
            const char** c_output_tensor_names, TF_Tensor** c_outputs,
            int noutputs,
            // Target nodes
            const char** c_target_node_names, int ntargets,
            TF_Buffer* run_metadata, TF_Status* status) {
  TF_Run_Helper(s, nullptr, run_options, c_input_names, c_inputs, ninputs,
                c_output_tensor_names, c_outputs, noutputs, c_target_node_names,
                ntargets, run_metadata, status);
}

void TF_PRunSetup(TF_Session* s,
                  // Input names
                  const char** c_input_names, int ninputs,
                  // Output names
                  const char** c_output_tensor_names, int noutputs,
                  // Target nodes
                  const char** c_target_node_names, int ntargets, char** handle,
                  TF_Status* status) {
  status->status = Status::OK();

  std::vector<tensorflow::string> input_names(ninputs);
  std::vector<tensorflow::string> output_tensor_names(noutputs);
  std::vector<tensorflow::string> target_node_names(ntargets);
  for (int i = 0; i < ninputs; i++) {
    input_names[i] = c_input_names[i];
  }
  for (int i = 0; i < noutputs; i++) {
    output_tensor_names[i] = c_output_tensor_names[i];
  }
  for (int i = 0; i < ntargets; i++) {
    target_node_names[i] = c_target_node_names[i];
  }
  tensorflow::string new_handle;
  Status result;
  result = s->session->PRunSetup(input_names, output_tensor_names,
                                 target_node_names, &new_handle);
  if (result.ok()) {
    *handle = new char[new_handle.size() + 1];
    memcpy(*handle, new_handle.c_str(), new_handle.size() + 1);
  } else {
    status->status = result;
  }
}

void TF_PRun(TF_Session* s, const char* handle,
             // Input tensors
             const char** c_input_names, TF_Tensor** c_inputs, int ninputs,
             // Output tensors
             const char** c_output_tensor_names, TF_Tensor** c_outputs,
             int noutputs,
             // Target nodes
             const char** c_target_node_names, int ntargets,
             TF_Status* status) {
  TF_Run_Helper(s, handle, nullptr, c_input_names, c_inputs, ninputs,
                c_output_tensor_names, c_outputs, noutputs, c_target_node_names,
                ntargets, nullptr, status);
}

TF_Library* TF_LoadLibrary(const char* library_filename, TF_Status* status) {
  TF_Library* lib_handle = new TF_Library;
  status->status = tensorflow::LoadLibrary(
      library_filename, &lib_handle->lib_handle, &lib_handle->op_list.data,
      &lib_handle->op_list.length);
  if (!status->status.ok()) {
    delete lib_handle;
    return nullptr;
  }
  return lib_handle;
}

TF_Buffer TF_GetOpList(TF_Library* lib_handle) { return lib_handle->op_list; }

}  // end extern "C"
