/* Copyright 2015 Google Inc. 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.
==============================================================================*/

#ifndef TENSORFLOW_PUBLIC_SESSION_H_
#define TENSORFLOW_PUBLIC_SESSION_H_

#include <string>
#include <vector>

#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/public/session_options.h"

namespace tensorflow {

/// \brief A Session instance lets a caller drive a TensorFlow graph
/// computation.
///
/// When a Session is created with a given target, a new Session object
/// is bound to the universe of resources specified by that target.
/// Those resources are available to this session to perform
/// computation described in the GraphDef.  After extending the session
/// with a graph, the caller uses the Run() API to perform the
/// computation and potentially fetch outputs as Tensors.
///
/// Example:
///
/// ```c++
///
///     tensorflow::GraphDef graph;
///     // ... Create or load graph into "graph".
///
///     // This example uses the default options which connects
///     // to a local runtime.
///     tensorflow::SessionOptions options;
///     std::unique_ptr<tensorflow::Session>
///     session(tensorflow::NewSession(options));
///
///     // Create the session with this graph.
///     tensorflow::Status s = session->Create(graph);
///     if (!s.ok()) { ... }
///
///     // Run the graph and fetch the first output of the "output"
///     // operation, and also run to but do not return anything
///     // for the "update_state" operation.
///     std::vector<tensorflow::Tensor> outputs;
///     s = session->Run({}, {"output:0"}, {"update_state"}, &outputs);
///     if (!s.ok()) { ... }
///
///     // Map the output as a flattened float tensor, and do something
///     // with it.
///     auto output_tensor = outputs[0].flat<float>();
///     if (output_tensor(0) > 0.5) { ... }
///
///     // Close the session to release the resources associated with
///     // this session.
///     session->Close();
///
/// ```
///
/// A Session allows concurrent calls to Run(), though a Session must
/// be created / extended by a single thread.
///
/// Only one thread must call Close(), and Close() must only be called
/// after all other calls to Run() have returned.
class Session {
 public:
  Session();
  virtual ~Session();

  /// \brief Create the graph to be used for the session.
  ///
  /// Returns an error if this session has already been created with a
  /// graph. To re-use the session with a different graph, the caller
  /// must Close() the session first.
  virtual Status Create(const GraphDef& graph) = 0;

  /// \brief Adds operations to the graph that is already registered with the
  /// Session.
  ///
  /// The names of new operations in "graph" must not exist in the
  /// graph that is already registered.
  virtual Status Extend(const GraphDef& graph) = 0;

  /// \brief Runs the graph with the provided input tensors and fills
  /// `outputs` for the endpoints specified in `output_tensor_names`.
  /// Runs to but does not return Tensors for the nodes in
  /// `target_node_names`.
  ///
  /// The order of tensors in `outputs` will match the order provided
  /// by `output_tensor_names`.
  ///
  /// If `Run` returns `OK()`, then `outputs->size()` will be equal to
  /// `output_tensor_names.size()`.  If `Run` does not return `OK()`, the
  /// state of `outputs` is undefined.
  ///
  /// REQUIRES: The name of each Tensor of the input or output must
  /// match a "Tensor endpoint" in the `GraphDef` passed to `Create()`.
  ///
  /// REQUIRES: outputs is not nullptr if `output_tensor_names` is non-empty.
  virtual Status Run(const std::vector<std::pair<string, Tensor> >& inputs,
                     const std::vector<string>& output_tensor_names,
                     const std::vector<string>& target_node_names,
                     std::vector<Tensor>* outputs) = 0;

  /// \brief Implementations which support `RunOptions`.
  //
  /// NOTE: This API is still experimental and may change.
  virtual Status Create(const RunOptions& run_options, const GraphDef& graph) {
    return errors::Unimplemented(
        "Create(const RunOptions& run_options, const GraphDef& graph) is not "
        "supported for this session.");
  }
  virtual Status Extend(const RunOptions& run_options, const GraphDef& graph) {
    return errors::Unimplemented(
        "Extend(const RunOptions& run_options, const GraphDef& graph) is not "
        "supported for this session.");
  }
  virtual Status Close(const RunOptions& run_options) {
    return errors::Unimplemented(
        "Close(const RunOptions& run_options) is not supported for this "
        "session.");
  }

  /// \brief Like `Run`, but allows users to pass in a `RunOptions` proto and
  /// to retrieve non-Tensor metadata output via a `RunOutputs` proto for this
  /// step.
  /// NOTE: This API is still experimental and may change.
  virtual Status Run(const RunOptions& run_options,
                     const std::vector<std::pair<string, Tensor> >& inputs,
                     const std::vector<string>& output_tensor_names,
                     const std::vector<string>& target_node_names,
                     std::vector<Tensor>* outputs, RunOutputs* run_outputs);

  /// \brief Sets up a graph for partial execution. All future feeds and
  /// fetches are specified by 'input_names' and 'output_names'. Returns
  /// 'handle' that can be used to perform a sequence of partial feeds and
  /// fetches.
  /// NOTE: This API is still experimental and may change.
  virtual Status PRunSetup(const std::vector<string>& input_names,
                           const std::vector<string>& output_names,
                           const std::vector<string>& target_nodes,
                           string* handle);

  /// \brief Continues the pending execution specified by 'handle' with the
  /// provided input tensors and fills `outputs` for the endpoints specified
  /// in `output_names`.
  /// NOTE: This API is still experimental and may change.
  virtual Status PRun(const string& handle,
                      const std::vector<std::pair<string, Tensor> >& inputs,
                      const std::vector<string>& output_names,
                      std::vector<Tensor>* outputs);

  /// \brief Closes this session.
  ///
  /// Closing a session releases the resources used by this session
  /// on the TensorFlow runtime (specified during session creation by
  /// the `SessionOptions::target` field).
  virtual Status Close() = 0;
};

/// \brief Create a new session with the given options.
///
/// If a new `Session` object could not be created, this function will
/// return nullptr.
Session* NewSession(const SessionOptions& options);

/// \brief Create a new session with the given options.
///
/// If session creation succeeds, the new `Session` will be stored in
/// `*out_session`, the caller will take ownership of the returned
/// `*out_session`, and this function will return `OK()`. Otherwise, this
/// function will return an error status.
Status NewSession(const SessionOptions& options, Session** out_session);

}  // end namespace tensorflow

#endif  // TENSORFLOW_PUBLIC_SESSION_H_