/* 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.
==============================================================================*/
#include "tensorflow/core/common_runtime/function.h"
#include "tensorflow/core/framework/partial_tensor_shape.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/kernels/data/captured_function.h"
#include "tensorflow/core/kernels/data/dataset.h"
#include "tensorflow/core/lib/random/random.h"

namespace tensorflow {

namespace {

// See documentation in ../ops/dataset_ops.cc for a high-level
// description of the following op.

class MapDatasetOp : public UnaryDatasetOpKernel {
 public:
  explicit MapDatasetOp(OpKernelConstruction* ctx)
      : UnaryDatasetOpKernel(ctx),
        graph_def_version_(ctx->graph_def_version()) {
    OP_REQUIRES_OK(ctx, ctx->GetAttr("f", &func_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_));
  }

  void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
                   DatasetBase** output) override {
    OpInputList inputs;
    OP_REQUIRES_OK(ctx, ctx->input_list("other_arguments", &inputs));
    std::vector<Tensor> other_arguments;
    other_arguments.reserve(inputs.size());
    for (const Tensor& t : inputs) {
      other_arguments.push_back(t);
    }

    std::unique_ptr<CapturedFunction> captured_func;
    OP_REQUIRES_OK(ctx, CapturedFunction::Create(
                            func_, std::move(other_arguments), &captured_func));

    *output = new Dataset(ctx, input, func_, std::move(captured_func),
                          output_types_, output_shapes_);
  }

 private:
  class Dataset : public DatasetBase {
   public:
    Dataset(OpKernelContext* ctx, const DatasetBase* input,
            const NameAttrList& func,
            std::unique_ptr<CapturedFunction> captured_func,
            const DataTypeVector& output_types,
            const std::vector<PartialTensorShape>& output_shapes)
        : DatasetBase(DatasetContext(ctx)),
          input_(input),
          func_(func),
          captured_func_(std::move(captured_func)),
          output_types_(output_types),
          output_shapes_(output_shapes) {
      input_->Ref();
    }

    ~Dataset() override { input_->Unref(); }

    std::unique_ptr<IteratorBase> MakeIteratorInternal(
        const string& prefix) const override {
      return std::unique_ptr<IteratorBase>(
          new Iterator({this, strings::StrCat(prefix, "::Map")}));
    }

    const DataTypeVector& output_dtypes() const override {
      return output_types_;
    }
    const std::vector<PartialTensorShape>& output_shapes() const override {
      return output_shapes_;
    }

    string DebugString() const override { return "MapDatasetOp::Dataset"; }

   protected:
    Status AsGraphDefInternal(SerializationContext* ctx,
                              DatasetGraphDefBuilder* b,
                              Node** output) const override {
      TF_RETURN_IF_ERROR(b->AddFunction(ctx, func_.name()));
      Node* input_graph_node = nullptr;
      TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph_node));

      DataTypeVector other_arguments_types;
      other_arguments_types.reserve(captured_func_->captured_inputs().size());
      std::vector<Node*> other_arguments;
      other_arguments.reserve(captured_func_->captured_inputs().size());
      for (const Tensor& t : captured_func_->captured_inputs()) {
        Node* node;
        TF_RETURN_IF_ERROR(b->AddTensor(t, &node));
        other_arguments.emplace_back(node);
        other_arguments_types.emplace_back(t.dtype());
      }
      AttrValue f;
      b->BuildAttrValue(func_, &f);
      AttrValue other_arguments_types_attr;
      b->BuildAttrValue(other_arguments_types, &other_arguments_types_attr);

      TF_RETURN_IF_ERROR(b->AddDataset(
          this, {std::make_pair(0, input_graph_node)},  // Single tensor inputs.
          {std::make_pair(1, other_arguments)},         // Tensor list inputs.
          {std::make_pair("f", f),
           std::make_pair("Targuments", other_arguments_types_attr)},  // Attrs
          output));
      return Status::OK();
    }

   private:
    class Iterator : public DatasetIterator<Dataset> {
     public:
      explicit Iterator(const Params& params)
          : DatasetIterator<Dataset>(params) {}

      Status Initialize(IteratorContext* ctx) override {
        TF_RETURN_IF_ERROR(
            dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_));
        return dataset()->captured_func_->Instantiate(ctx);
      }

      Status GetNextInternal(IteratorContext* ctx,
                             std::vector<Tensor>* out_tensors,
                             bool* end_of_sequence) override {
        // NOTE(mrry): This method is thread-safe as long as
        // `input_impl_` and `f` are thread-safe. However, if multiple
        // threads enter this method, outputs may be observed in a
        // non-deterministic order.

        std::vector<Tensor> args;
        TF_RETURN_IF_ERROR(input_impl_->GetNext(ctx, &args, end_of_sequence));
        if (*end_of_sequence) {
          return Status::OK();
        }

        // TODO(mrry): Avoid blocking a threadpool thread. We will need to
        // stack-rip the iterators and use async kernels.
        Status s =
            dataset()->captured_func_->Run(ctx, std::move(args), out_tensors);
        if (errors::IsOutOfRange(s)) {
          // `f` may deliberately raise `errors::OutOfRange` to indicate
          // that we should terminate the iteration early.
          *end_of_sequence = true;
          return Status::OK();
        } else {
          return s;
        }
      }

     protected:
      Status SaveInternal(IteratorStateWriter* writer) override {
        TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_));
        return Status::OK();
      }

      Status RestoreInternal(IteratorContext* ctx,
                             IteratorStateReader* reader) override {
        TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_));
        return Status::OK();
      }

     private:
      std::unique_ptr<IteratorBase> input_impl_;
    };

    const DatasetBase* const input_;
    const NameAttrList func_;
    const std::unique_ptr<CapturedFunction> captured_func_;
    const DataTypeVector output_types_;
    const std::vector<PartialTensorShape> output_shapes_;
  };

  const int graph_def_version_;
  DataTypeVector output_types_;
  std::vector<PartialTensorShape> output_shapes_;
  NameAttrList func_;
};

REGISTER_KERNEL_BUILDER(Name("MapDataset").Device(DEVICE_CPU), MapDatasetOp);

}  // namespace

}  // namespace tensorflow
