/* Copyright 2019 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.
==============================================================================*/

#ifndef TENSORFLOW_LITE_DELEGATES_GPU_COMMON_OPERATIONS_H_
#define TENSORFLOW_LITE_DELEGATES_GPU_COMMON_OPERATIONS_H_

#include <cstdint>
#include <string>
#include <vector>

#include "absl/types/variant.h"
#include "tensorflow/lite/delegates/gpu/common/data_type.h"
#include "tensorflow/lite/delegates/gpu/common/model.h"
#include "tensorflow/lite/delegates/gpu/common/shape.h"
#include "tensorflow/lite/delegates/gpu/common/status.h"

namespace tflite {
namespace gpu {

// Non exhaustive list of operations.
enum class OperationType {
  UNKNOWN = 0,
  ABS,
  ADD,
  // TODO(eignasheva): remove APPLY_MASK operation, is should be just MUL
  APPLY_MASK,
  BATCH_TO_SPACE,
  BATCH_NORMALIZATION,
  CONCAT,
  CONST,
  CONVOLUTION_2D,
  CONVOLUTION_TRANSPOSED,
  COS,
  DEPTHWISE_CONVOLUTION,
  DIV,
  FULLY_CONNECTED,
  LOG,
  LSTM,
  MAX_UNPOOLING_2D,
  MUL,
  MULTIPLY_SCALAR,
  POOLING_2D,
  POW,
  PAD,
  PRELU,
  RELU,
  RESHAPE,
  RESIZE,
  RSQRT,
  SIGMOID,
  SIN,
  SLICE,
  SOFT_MAX,
  SPACE_TO_BATCH,
  SQRT,
  SQUARE,
  SQUARED_DIFF,
  SUB,
  TANH,
  UPSAMPLE_2D,
};

std::string ToString(enum OperationType op);

OperationType OperationTypeFromString(const std::string& name);

struct Padding2D {
  Padding2D() = default;
  Padding2D& operator=(const Padding2D& value);
  bool operator==(const Padding2D& value);
  bool operator!=(const Padding2D& value);
  Padding2D& operator-(const Padding2D& value);

  // Padding values for every axis (if needed), where 'prepended' defines
  // padding for the beginning of each axis and 'appended' represents end part
  // of the corresponding axis.
  HW prepended = HW(-1, -1);
  HW appended = HW(-1, -1);
};

struct Crop2D : public Padding2D {};

struct SpaceToBatchAttributes {
  HW block;
  Padding2D padding;
};

struct BatchToSpaceAttributes {
  HW block;
  Crop2D crop;
};

enum class PoolingType {
  UNDEFINED = 0,

  // average pooling
  AVERAGE = 1,

  // max pooling
  MAX = 2,
};

struct Pooling2DAttributes {
  PoolingType type = PoolingType::UNDEFINED;
  // Strides for every axis.
  HW strides = HW(-1, -1);
  HW kernel = HW(-1, -1);
  Padding2D padding;
  // NOTE(akulik): technically the number of outputs from Pooling node indicates
  // whether indices are needed or not, but I decided to keep it inside
  // attributes to simplify processing.
  bool output_indices = false;
};

struct MaxUnpooling2DAttributes {
  // Strides for every axis.
  HW strides = HW(-1, -1);
  HW kernel = HW(-1, -1);
  Padding2D padding;
};

struct ConcatAttributes {
  // Defines axis by which to concat on.
  Axis axis = Axis::UNKNOWN;
};

// @return shape of a tensor after MaxUnpooling2D operation is applied to
//         the given input.
BHWC CalculateOutputShape(const BHWC& input,
                          const MaxUnpooling2DAttributes& attr);

// @return shape of a tensor after Pooling2D operation is applied to the given
//         input.
BHWC CalculateOutputShape(const BHWC& input, const Pooling2DAttributes& attr);

// @return shape of a tensor after Concat operation is applied to the given
//         input.
Status CalculateOutputShape(const std::vector<BHWC>& input,
                            const ConcatAttributes& attr, BHWC* output_shape);

// @return padding for pooling operation to make sure output keep the same shape
// as the given input.
Padding2D CalculateSamePadding(const BHWC& input,
                               const Pooling2DAttributes& attr);

// @return padding for max unpooling operation to make sure output keep the same
// shape as the given input.
Padding2D CalculateSamePadding(const BHWC& input,
                               const MaxUnpooling2DAttributes& attr);

struct Convolution2DAttributes {
  HW strides = HW(1, 1);    // Along each axis.
  HW dilations = HW(1, 1);  // Along each axis.
  Padding2D padding;

  Tensor<OHWI, DataType::FLOAT32> weights;
  Tensor<Linear, DataType::FLOAT32> bias;  // optional
};

// @return shape of a tensor after Convolution2D operation is applied to
//         the given input.
BHWC CalculateOutputShape(const BHWC& input,
                          const Convolution2DAttributes& attr);

// @return padding for convolution operation to make sure output keep the same
// shape as the given input.
Padding2D CalculateSamePadding(const BHWC& input,
                               const Convolution2DAttributes& attr);

struct ConvolutionTransposedAttributes {
  HW stride = HW(1, 1);  // Along each axis.
  HW adjacent;           // TODO(sorokin): No op on Flow.
  Padding2D padding;

  Tensor<OHWI, DataType::FLOAT32> weights;
  Tensor<Linear, DataType::FLOAT32> bias;  // optional
};

Padding2D CalculateSamePadding(const BHWC& input,
                               const ConvolutionTransposedAttributes& attr);

// @return shape of a tensor after ConvolutionTransposed operation is applied to
//         the given input.
BHWC CalculateOutputShape(const BHWC& input,
                          const ConvolutionTransposedAttributes& attr);

struct DepthwiseConvolution2DAttributes : public Convolution2DAttributes {};

// @return shape of a tensor after DepthwiseConvolution2D operation is applied
//         to the given input.
BHWC CalculateOutputShape(const BHWC& input,
                          const DepthwiseConvolution2DAttributes& attr);

// @return padding for depthwise convolution operation to make sure output keep
// the same shape as the given input.
Padding2D CalculateSamePadding(const BHWC& input,
                               const DepthwiseConvolution2DAttributes& attr);

BHWC CalculateOutputShape(const BHWC& input,
                          const DepthwiseConvolution2DAttributes& attr);

// f(x):= {
//   if x < 0  : x -> alpha * x
//   if x >= 0 : x -> min(clip, x)
// }
//
// Examples:
//   - ReLU: clip = 0, alpha = 0
//   - ReLU6: clip = 6, alpha = 0
//   - Leaky ReLU: clip = 0, alpha = a
struct ReLUAttributes {
  // clip <= 0 mean it is not set.
  float clip = 0;

  float alpha = 0;
};

struct PReLUAttributes {
  // clip <= 0 mean it is not set.
  float clip = 0;

  // If alpha is linear, then it is sharded across CHANNELS axis, otherwise
  // full shape alpha is required.
  absl::variant<Tensor<Linear, DataType::FLOAT32>,
                Tensor<HWC, DataType::FLOAT32>>
      alpha;
};

struct SoftMaxAttributes {
  Axis axis = Axis::UNKNOWN;
};

enum LstmKernelType {
  FULL = 0,
  BASIC = 1,  // Currently, only basic is supported.
};

struct LstmAttributes {
  LstmKernelType kernel_type = LstmKernelType::BASIC;
};

struct MultiplyScalarAttributes {
  absl::variant<absl::monostate, Tensor<Linear, DataType::FLOAT32>, float>
      param;
};

enum class UpsamplingType {
  NEAREST = 0,
  BILINEAR = 1,
};

struct Upsample2DAttributes {
  HW new_shape;

  UpsamplingType type = UpsamplingType::NEAREST;

  // If true, the centers of the 4 corner pixels of the input and output tensors
  // are aligned, preserving the values at the corner pixels. Defaults to false.
  bool align_corners = false;
};

float CalculateResizeScale(int32_t input_size, int32_t output_size,
                           const Upsample2DAttributes& attr);

// @return shape of a tensor after upscale operation is applied to the given
// input.
BHWC CalculateOutputShape(const BHWC& input, const Upsample2DAttributes& attr);

enum class PaddingContentType {
  ZEROS = 0,
  REFLECT = 1,
  EDGE = 2,
};

struct PadAttributes {
  PaddingContentType type = PaddingContentType::ZEROS;

  HWC prepended;
  HWC appended;
};

// @return shape of a tensor after Pad operation is applied to the given input.
BHWC CalculateOutputShape(const BHWC& input, const PadAttributes& attr);

struct ConstTensorAttributes {
  Tensor<BHWC, DataType::FLOAT32> tensor;
};

// Simple slicing without advanced support for shrinking, reverse slicing etc.
struct SliceAttributes {
  // Specifies start and end dimensions for slicing.
  HWC starts;
  HWC ends;

  // Stride should be >= 1.
  HWC strides;
};

// @return shape of a tensor after Slice2D operation is applied to the given
//         input.
BHWC CalculateOutputShape(const BHWC& input, const SliceAttributes& attr);

struct AddAttributes {
  absl::variant<absl::monostate, Tensor<Linear, DataType::FLOAT32>, float>
      param;
};

struct FullyConnectedAttributes {
  Tensor<OHWI, DataType::FLOAT32> weights;
  Tensor<Linear, DataType::FLOAT32> bias;
};

// @return shape of a tensor after FullyConnected operation is applied to
// the given input.
BHWC CalculateOutputShape(const BHWC& input,
                          const FullyConnectedAttributes& attr);

struct ReshapeAttributes {
  BHWC new_shape;
};

}  // namespace gpu
}  // namespace tflite

#endif  // TENSORFLOW_LITE_DELEGATES_GPU_COMMON_OPERATIONS_H_
