/* Copyright 2017 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.
==============================================================================*/
// Main abstraction controlling the tflite interpreter.
// See context.h for the API for defining operations (TfLiteRegistration).
#ifndef TENSORFLOW_CONTRIB_LITE_INTERPRETER_H_
#define TENSORFLOW_CONTRIB_LITE_INTERPRETER_H_

#include <cstdio>
#include <cstdlib>
#include <vector>
#include "tensorflow/contrib/lite/allocation.h"
#include "tensorflow/contrib/lite/context.h"
#include "tensorflow/contrib/lite/error_reporter.h"
#include "tensorflow/contrib/lite/memory_planner.h"

namespace tflite {

// Map statically from a c++ type to a TfLiteType (used below for safe casts).
template <class T>
constexpr TfLiteType typeToTfLiteType() {
  return kTfLiteNoType;
}
template <>
constexpr TfLiteType typeToTfLiteType<int>() {
  return kTfLiteInt32;
}
template <>
constexpr TfLiteType typeToTfLiteType<int64_t>() {
  return kTfLiteInt64;
}
template <>
constexpr TfLiteType typeToTfLiteType<float>() {
  return kTfLiteFloat32;
}
template <>
constexpr TfLiteType typeToTfLiteType<unsigned char>() {
  return kTfLiteUInt8;
}

// Forward declare since NNAPIDelegate uses Interpreter.
class NNAPIDelegate;

// An interpreter for a graph of nodes that input and output from tensors.
// Each node of the graph processes a set of input tensors and produces a
// set of output Tensors. All inputs/output tensors are referenced by index.
//
// Usage:
//
// -- Create basic model
// Interpreter foo(2, 1);
// foo.SetTensorParametersReadWrite(0, ...);
// foo.SetTensorParametersReadOnly(1, ...);
// foo.SetNodeParameters(0, ...)
//
// -- Resize input array to 1 length.
// foo.ResizeInputTensor(0, 1);
// foo.AllocateTensors();
// -- Install array data
// foo.typed_tensor<float>(0)[0] = 3;
// foo.Invoke();
// foo.typed_tensor<float>(0)[0] = 4;
// foo.Invoke();
// -- Resize input array and set data.
// foo.ResizeInputTensor(0, 2);
// foo.AllocateTensors();
// foo.typed_tensor<float>(0)[0] = 4;
// foo.typed_tensor<float>(0)[1] = 8;
// foo.Invoke();
//

class Interpreter {
 public:
  // Instantiate an interpreter. All errors associated with reading and
  // processing this model will be forwarded to the error_reporter object.
  //
  // Note, if error_reporter is nullptr, then a default StderrReporter is
  // used.
  explicit Interpreter(ErrorReporter* error_reporter = DefaultErrorReporter());

  ~Interpreter();

  Interpreter(const Interpreter&) = delete;
  Interpreter& operator=(const Interpreter&) = delete;

  // Functions to build interpreter

  // Provide a list of tensor indexes that are inputs to the model.
  // Each index is bound check and this modifies the consistent_ flag of the
  // interpreter.
  TfLiteStatus SetInputs(std::vector<int> inputs);

  // Provide a list of tensor indexes that are outputs to the model
  // Each index is bound check and this modifies the consistent_ flag of the
  // interpreter.
  TfLiteStatus SetOutputs(std::vector<int> outputs);

  // Adds a node with the given parameters and returns the index of the new
  // node in `node_index` (optionally). Interpreter will take ownership of
  // `builtin_data` and destroy it with `free`. Ownership of 'init_data'
  // remains with the caller.
  TfLiteStatus AddNodeWithParameters(const std::vector<int>& inputs,
                                     const std::vector<int>& outputs,
                                     const char* init_data,
                                     size_t init_data_size, void* builtin_data,
                                     const TfLiteRegistration* registration,
                                     int* node_index = nullptr);

  // Adds `tensors_to_add` tensors, preserving pre-existing Tensor entries.
  // The value pointed to by `first_new_tensor_index` will be set to the
  // index of the first new tensor if `first_new_tensor_index` is non-null.
  TfLiteStatus AddTensors(int tensors_to_add,
                          int* first_new_tensor_index = nullptr);

  // Set description of inputs/outputs/data/fptrs for node `node_index`.
  // This variant assumes an external buffer has been allocated of size
  // bytes. The lifetime of buffer must be ensured to be greater or equal
  // to Interpreter.
  TfLiteStatus SetTensorParametersReadOnly(
      int tensor_index, TfLiteType type, const char* name,
      const std::vector<int>& dims, TfLiteQuantizationParams quantization,
      const char* buffer, size_t bytes, const Allocation* allocation = nullptr);

  // Set description of inputs/outputs/data/fptrs for node `node_index`.
  // This variant assumes an external buffer has been allocated of size
  // bytes. The lifetime of buffer must be ensured to be greater or equal
  // to Interpreter.
  TfLiteStatus SetTensorParametersReadWrite(
      int tensor_index, TfLiteType type, const char* name,
      const std::vector<int>& dims, TfLiteQuantizationParams quantization);

  // Functions to access tensor data

  // Read only access to list of inputs.
  const std::vector<int>& inputs() const { return inputs_; }

  // Return the name of a given input. The given index must be between 0 and
  // inputs().size().
  const char* GetInputName(int index) const {
    return context_.tensors[inputs_[index]].name;
  }

  // Read only access to list of outputs.
  const std::vector<int>& outputs() const { return outputs_; }

  // Return the name of a given output. The given index must be between 0 and
  // outputs().size().
  const char* GetOutputName(int index) const {
    return context_.tensors[outputs_[index]].name;
  }

  // Return the number of tensors in the model.
  int tensors_size() const { return context_.tensors_size; }

  // Return the number of ops in the model.
  int nodes_size() const { return nodes_and_registration_.size(); }

  // WARNING: Experimental interface, subject to change
  const std::vector<int>& execution_plan() const { return execution_plan_; }

  // WARNING: Experimental interface, subject to change
  // Overrides execution plan. This bounds checks indices sent in.
  TfLiteStatus SetExecutionPlan(const std::vector<int>& new_plan);

  // Get a tensor data structure.
  // TODO(aselle): Create a safe ArrayHandle interface to avoid exposing this
  // read/write access to structure
  TfLiteTensor* tensor(int tensor_index) {
    if (tensor_index >= context_.tensors_size || tensor_index < 0)
      return nullptr;
    return &context_.tensors[tensor_index];
  }

  // Get a pointer to an operation and registration data structure if in bounds.
  // TODO(aselle): Create a safe ArrayHandle interface to avoid exposing this
  // read/write access to structure
  const std::pair<TfLiteNode, TfLiteRegistration>* node_and_registration(
      int node_index) {
    if (node_index >= nodes_and_registration_.size() || node_index < 0)
      return nullptr;
    return &nodes_and_registration_[node_index];
  }

  // Perform a checked cast to the appropriate tensor type.
  template <class T>
  T* typed_tensor(int tensor_index) {
    if (TfLiteTensor* tensor_ptr = tensor(tensor_index)) {
      if (tensor_ptr->type == typeToTfLiteType<T>()) {
        return reinterpret_cast<T*>(tensor_ptr->data.raw);
      }
    }
    return nullptr;
  }

  // Return a pointer into the data of a given input tensor. The given index
  // must be between 0 and inputs().size().
  template <class T>
  T* typed_input_tensor(int index) {
    return typed_tensor<T>(inputs_[index]);
  }

  // Return a pointer into the data of a given output tensor. The given index
  // must be between 0 and outputs().size().
  template <class T>
  T* typed_output_tensor(int index) {
    return typed_tensor<T>(outputs_[index]);
  }

  // Change the dimensionality of a given tensor. Note, this is only acceptable
  // for tensor indices that are inputs.
  // Returns status of failure or success.
  // TODO(aselle): Consider implementing ArraySlice equivalent to make this
  //   more adept at accepting data without an extra copy. Use absl::ArraySlice
  //   if our partners determine that dependency is acceptable.
  TfLiteStatus ResizeInputTensor(int tensor_index,
                                 const std::vector<int>& dims);

  // Update allocations for all tensors. This will redim dependent tensors using
  // the input tensor dimensionality as given. This is relatively expensive.
  // If you know that your sizes are not changing, you need not call this.

  // Returns status of success or failure.
  TfLiteStatus AllocateTensors();

  // Invoke the interpreter (run the whole graph in dependency order).
  //
  // NOTE: It is possible that the interpreter is not in a ready state
  // to evaluate (i.e. if a ResizeTensor() has been performed without an
  // AllocateTensors().
  // Returns status of success or failure.
  TfLiteStatus Invoke();

  // Enable or disable the NN API (true to enable)
  void UseNNAPI(bool enable);

  // Set the number of threads available to the interpreter.
  void SetNumThreads(int num_threads);

 private:
  // Give 'op_reg' a chance to initialize itself using the contents of
  // 'buffer'.
  void* OpInit(const TfLiteRegistration& op_reg, const char* buffer,
               size_t length) {
    if (op_reg.init == nullptr) return nullptr;
    return op_reg.init(&context_, buffer, length);
  }

  // Let 'op_reg' release any memory it might have allocated via 'OpInit'.
  void OpFree(const TfLiteRegistration& op_reg, void* buffer) {
    if (op_reg.free == nullptr) return;
    if (buffer) {
      op_reg.free(&context_, buffer);
    }
  }

  // Prepare the given 'node' for execution.
  TfLiteStatus OpPrepare(const TfLiteRegistration& op_reg, TfLiteNode* node) {
    if (op_reg.prepare == nullptr) return kTfLiteOk;
    return op_reg.prepare(&context_, node);
  }

  // Invoke the operator represented by 'node'.
  TfLiteStatus OpInvoke(const TfLiteRegistration& op_reg, TfLiteNode* node) {
    if (op_reg.invoke == nullptr) return kTfLiteError;
    return op_reg.invoke(&context_, node);
  }

  // Call OpPrepare() for as many ops as possible, allocating memory for their
  // tensors. If an op containing dynamic tensors is found, preparation will be
  // postponed until this function is called again. This allows the interpreter
  // to wait until Invoke() to resolve the sizes of dynamic tensors.
  TfLiteStatus PrepareOpsAndTensors();

  // Call OpPrepare() for all ops starting at 'first_node'. Stop when a
  // dynamic tensors is found or all ops have been prepared. Fill
  // 'last_node_prepared' with the id of the op containing dynamic tensors, or
  // the last in the graph.
  TfLiteStatus PrepareOpsStartingAt(int first_execution_plan_index,
                                    int* last_execution_plan_index_prepared);

  // Tensors needed by the interpreter. Use `AddTensors` to add more blank
  // tensor entries. Note, `tensors_.data()` needs to be synchronized to the
  // `context_` whenever this std::vector is reallocated. Currently this
  // only happens in `AddTensors()`.
  std::vector<TfLiteTensor> tensors_;

  // Check if an array of tensor indices are valid with respect to the Tensor
  // array.
  // NOTE: this changes consistent_ to be false if indices are out of bounds.
  TfLiteStatus CheckTensorIndices(const char* label, const int* indices,
                                  int length);

  // Compute the number of bytes required to represent a tensor with dimensions
  // specified by the array dims (of length dims_size). Returns the status code
  // and bytes.
  TfLiteStatus BytesRequired(TfLiteType type, const int* dims, int dims_size,
                             size_t* bytes);

  // Request an tensor be resized implementation.
  TfLiteStatus ResizeTensorImpl(TfLiteTensor* tensor, TfLiteIntArray* new_size);

  // Report a detailed error string (will be printed to stderr).
  // TODO(aselle): allow user of class to provide alternative destinations.
  void ReportErrorImpl(const char* format, va_list args);

  // Entry point for C node plugin API to request an tensor be resized.
  static TfLiteStatus ResizeTensor(TfLiteContext* context, TfLiteTensor* tensor,
                                   TfLiteIntArray* new_size);
  // Entry point for C node plugin API to report an error.
  static void ReportError(TfLiteContext* context, const char* format, ...);

  // Entry point for C node plugin API to add new tensors.
  static TfLiteStatus AddTensors(TfLiteContext* context, int tensors_to_add,
                                 int* first_new_tensor_index);

  // A pure C data structure used to communicate with the pure C plugin
  // interface. To avoid copying tensor metadata, this is also the definitive
  // structure to store tensors.
  TfLiteContext context_;

  // Node inputs/outputs are stored in TfLiteNode and TfLiteRegistration stores
  // function pointers to actual implementation.
  std::vector<std::pair<TfLiteNode, TfLiteRegistration>>
      nodes_and_registration_;

  // Whether the model is consistent. That is to say if the inputs and outputs
  // of every node and the global inputs and outputs are valid indexes into
  // the tensor array.
  bool consistent_ = true;

  // Whether the model is safe to invoke (if any errors occurred this
  // will be false).
  bool invokable_ = false;

  // Array of indices representing the tensors that are inputs to the
  // interpreter.
  std::vector<int> inputs_;

  // Array of indices representing the tensors that are outputs to the
  // interpreter.
  std::vector<int> outputs_;

  // The error reporter delegate that tflite will forward queries errors to.
  ErrorReporter* error_reporter_;

  // Index of the next node to prepare.
  // During Invoke(), Interpreter will allocate input tensors first, which are
  // known to be fixed size. Then it will allocate outputs from nodes as many
  // as possible. When there is a node that produces dynamic sized tensor.
  // Intepreter will stop allocating tensors, set the value of next allocate
  // node id, and execute the node to generate the output tensor before continue
  // to allocate successors. This process repeats until all nodes are executed.
  // NOTE: this relies on the order of nodes that is in topological order.
  int next_execution_plan_index_to_prepare_;

  // WARNING: This is an experimental interface that is subject to change.
  // This is a list of node indices (to index into nodes_and_registration).
  // This represents a valid topological sort (dependency ordered) execution
  // plan. In particular, it is valid for this ordering to contain only a
  // subset of the node indices.
  std::vector<int> execution_plan_;

  // Whether to delegate to NN API
  std::unique_ptr<NNAPIDelegate> nnapi_delegate_;

  std::unique_ptr<MemoryPlanner> memory_planner_;
};

}  // namespace tflite
#endif  // TENSORFLOW_CONTRIB_LITE_INTERPRETER_H_
