/* Copyright 2016 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.
==============================================================================*/

// See docs in ../ops/ctc_ops.cc.

#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/kernels/bounds_check.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/util/ctc/ctc_loss_calculator.h"
#include "tensorflow/core/util/sparse/sparse_tensor.h"

namespace tensorflow {

typedef Eigen::ThreadPoolDevice CPUDevice;

class CTCLossOp : public OpKernel {
  typedef Eigen::Map<const Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic,
                                         Eigen::RowMajor> >
      InputMap;
  typedef Eigen::Map<
      Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> >
      OutputMap;

 public:
  explicit CTCLossOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
    OP_REQUIRES_OK(ctx, ctx->GetAttr("preprocess_collapse_repeated",
                                     &preprocess_collapse_repeated_));
    OP_REQUIRES_OK(ctx,
                   ctx->GetAttr("ctc_merge_repeated", &ctc_merge_repeated_));
    OP_REQUIRES_OK(ctx, ctx->GetAttr("ignore_longer_outputs_than_inputs",
                                     &ignore_longer_outputs_than_inputs_));
  }

  void Compute(OpKernelContext* ctx) override {
    const Tensor* inputs;
    const Tensor* labels_indices;
    const Tensor* labels_values;
    const Tensor* seq_len;
    OP_REQUIRES_OK(ctx, ctx->input("inputs", &inputs));
    OP_REQUIRES_OK(ctx, ctx->input("labels_indices", &labels_indices));
    OP_REQUIRES_OK(ctx, ctx->input("labels_values", &labels_values));
    OP_REQUIRES_OK(ctx, ctx->input("sequence_length", &seq_len));

    OP_REQUIRES(ctx, inputs->shape().dims() == 3,
                errors::InvalidArgument("inputs is not a 3-Tensor"));
    OP_REQUIRES(ctx, TensorShapeUtils::IsVector(seq_len->shape()),
                errors::InvalidArgument("sequence_length is not a vector"));
    OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(labels_indices->shape()),
                errors::InvalidArgument("labels_indices is not a matrix"));
    OP_REQUIRES(ctx, TensorShapeUtils::IsVector(labels_values->shape()),
                errors::InvalidArgument("labels_values is not a vector"));

    const TensorShape& inputs_shape = inputs->shape();
    const int64 max_time = inputs_shape.dim_size(0);
    const int64 batch_size = inputs_shape.dim_size(1);
    const int64 num_classes_raw = inputs_shape.dim_size(2);
    OP_REQUIRES(
        ctx, FastBoundsCheck(num_classes_raw, std::numeric_limits<int>::max()),
        errors::InvalidArgument("num_classes cannot exceed max int"));
    const int num_classes = static_cast<const int>(num_classes_raw);

    OP_REQUIRES(
        ctx, batch_size == seq_len->dim_size(0),
        errors::InvalidArgument("len(sequence_length) != batch_size.  ",
                                "len(sequence_length):  ", seq_len->dim_size(0),
                                " batch_size: ", batch_size));
    auto seq_len_t = seq_len->vec<int32>();

    OP_REQUIRES(ctx, labels_indices->dim_size(0) == labels_values->dim_size(0),
                errors::InvalidArgument(
                    "labels_indices and labels_values must contain the "
                    "same number of rows, but saw shapes: ",
                    labels_indices->shape().DebugString(), " vs. ",
                    labels_values->shape().DebugString()));

    OP_REQUIRES(ctx, batch_size != 0,
                errors::InvalidArgument("batch_size must not be 0"));

    // Figure out the maximum label length to use as sparse tensor dimension.
    auto labels_indices_t = labels_indices->matrix<int64>();
    int64 max_label_len = 0;
    for (int i = 0; i < labels_indices->dim_size(0); i++) {
      max_label_len = std::max(max_label_len, labels_indices_t(i, 1) + 1);
    }

    TensorShape labels_shape({batch_size, max_label_len});
    std::vector<int64> order{0, 1};
    sparse::SparseTensor labels_sp;
    OP_REQUIRES_OK(
        ctx, sparse::SparseTensor::Create(*labels_indices, *labels_values,
                                          labels_shape, order, &labels_sp));

    Status labels_sp_valid = labels_sp.IndicesValid();
    OP_REQUIRES(ctx, labels_sp_valid.ok(),
                errors::InvalidArgument("label SparseTensor is not valid: ",
                                        labels_sp_valid.error_message()));

    ctc::CTCLossCalculator::LabelSequences labels_t(batch_size);
    for (const auto& g : labels_sp.group({0})) {  // iterate by batch
      const int64 batch_indices = g.group()[0];
      OP_REQUIRES(ctx, FastBoundsCheck(batch_indices, batch_size),
                  errors::InvalidArgument("labels batch index must be between ",
                                          0, " and ", batch_size,
                                          " but saw: ", batch_indices));

      auto values = g.values<int32>();
      std::vector<int>* b_values = &labels_t[batch_indices];
      b_values->resize(values.size());
      for (int i = 0; i < values.size(); ++i) (*b_values)[i] = values(i);
    }

    OP_REQUIRES(ctx, static_cast<size_t>(batch_size) == labels_t.size(),
                errors::InvalidArgument("len(labels) != batch_size.  ",
                                        "len(labels):  ", labels_t.size(),
                                        " batch_size: ", batch_size));

    for (int64 b = 0; b < batch_size; ++b) {
      OP_REQUIRES(
          ctx, seq_len_t(b) <= max_time,
          errors::InvalidArgument("sequence_length(", b, ") <= ", max_time));
    }

    Tensor* loss = nullptr;
    OP_REQUIRES_OK(ctx, ctx->allocate_output("loss", seq_len->shape(), &loss));
    auto loss_t = loss->vec<float>();

    Tensor* gradient;
    OP_REQUIRES_OK(ctx,
                   ctx->allocate_output("gradient", inputs_shape, &gradient));
    auto gradient_t = gradient->tensor<float, 3>();
    auto inputs_t = inputs->tensor<float, 3>();
    std::vector<OutputMap> gradient_list_t;
    std::vector<InputMap> input_list_t;

    for (std::size_t t = 0; t < max_time; ++t) {
      input_list_t.emplace_back(inputs_t.data() + t * batch_size * num_classes,
                                batch_size, num_classes);
      gradient_list_t.emplace_back(
          gradient_t.data() + t * batch_size * num_classes, batch_size,
          num_classes);
    }

    gradient_t.setZero();

    // Assumption: the blank index is num_classes - 1
    ctc::CTCLossCalculator ctc_loss_calculator(num_classes - 1, 0);
    DeviceBase::CpuWorkerThreads workers =
        *ctx->device()->tensorflow_cpu_worker_threads();
    OP_REQUIRES_OK(ctx, ctc_loss_calculator.CalculateLoss(
                            seq_len_t, labels_t, input_list_t,
                            preprocess_collapse_repeated_, ctc_merge_repeated_,
                            ignore_longer_outputs_than_inputs_, &loss_t,
                            &gradient_list_t, &workers));
  }

 private:
  bool preprocess_collapse_repeated_;
  bool ctc_merge_repeated_;
  bool ignore_longer_outputs_than_inputs_;

  TF_DISALLOW_COPY_AND_ASSIGN(CTCLossOp);
};

REGISTER_KERNEL_BUILDER(Name("CTCLoss").Device(DEVICE_CPU), CTCLossOp);

}  // end namespace tensorflow