/* 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_NNAPI_NEURALNETWORKSTYPES_H_
#define TENSORFLOW_LITE_NNAPI_NEURALNETWORKSTYPES_H_

#include <stdint.h>
#include <stdio.h>

#include <string>

typedef struct AHardwareBuffer AHardwareBuffer;

// NN api types based on NNAPI header file
// https://developer.android.com/ndk/reference/group/neural-networks

/**
 * Operand types.
 *
 * The type of operands that can be added to a model.
 *
 * Although we define many types, most operators accept just a few
 * types.  Most used are ANEURALNETWORKS_TENSOR_FLOAT32,
 * ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, and ANEURALNETWORKS_INT32.
 */
enum {
  ANEURALNETWORKS_FLOAT32 = 0,
  ANEURALNETWORKS_INT32 = 1,
  ANEURALNETWORKS_UINT32 = 2,
  ANEURALNETWORKS_TENSOR_FLOAT32 = 3,
  ANEURALNETWORKS_TENSOR_INT32 = 4,
  ANEURALNETWORKS_TENSOR_QUANT8_ASYMM = 5,
  ANEURALNETWORKS_BOOL = 6,
  ANEURALNETWORKS_TENSOR_QUANT16_SYMM = 7,
  ANEURALNETWORKS_TENSOR_FLOAT16 = 8,
  ANEURALNETWORKS_TENSOR_BOOL8 = 9,
  ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
  ANEURALNETWORKS_TENSOR_QUANT8_SYMM = 13,
  ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED = 14,
};

/**
 * Operation types.
 *
 * The type of operations that can be added to a model.
 */
enum {
  ANEURALNETWORKS_ADD = 0,
  ANEURALNETWORKS_AVERAGE_POOL_2D = 1,
  ANEURALNETWORKS_CONCATENATION = 2,
  ANEURALNETWORKS_CONV_2D = 3,
  ANEURALNETWORKS_DEPTHWISE_CONV_2D = 4,
  ANEURALNETWORKS_DEPTH_TO_SPACE = 5,
  ANEURALNETWORKS_DEQUANTIZE = 6,
  ANEURALNETWORKS_EMBEDDING_LOOKUP = 7,
  ANEURALNETWORKS_FLOOR = 8,
  ANEURALNETWORKS_FULLY_CONNECTED = 9,
  ANEURALNETWORKS_HASHTABLE_LOOKUP = 10,
  ANEURALNETWORKS_L2_NORMALIZATION = 11,
  ANEURALNETWORKS_L2_POOL_2D = 12,
  ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION = 13,
  ANEURALNETWORKS_LOGISTIC = 14,
  ANEURALNETWORKS_LSH_PROJECTION = 15,
  ANEURALNETWORKS_LSTM = 16,
  ANEURALNETWORKS_MAX_POOL_2D = 17,
  ANEURALNETWORKS_MUL = 18,
  ANEURALNETWORKS_RELU = 19,
  ANEURALNETWORKS_RELU1 = 20,
  ANEURALNETWORKS_RELU6 = 21,
  ANEURALNETWORKS_RESHAPE = 22,
  ANEURALNETWORKS_RESIZE_BILINEAR = 23,
  ANEURALNETWORKS_RNN = 24,
  ANEURALNETWORKS_SOFTMAX = 25,
  ANEURALNETWORKS_SPACE_TO_DEPTH = 26,
  ANEURALNETWORKS_SVDF = 27,
  ANEURALNETWORKS_TANH = 28,
  ANEURALNETWORKS_BATCH_TO_SPACE_ND = 29,
  ANEURALNETWORKS_DIV = 30,
  ANEURALNETWORKS_MEAN = 31,
  ANEURALNETWORKS_PAD = 32,
  ANEURALNETWORKS_SPACE_TO_BATCH_ND = 33,
  ANEURALNETWORKS_SQUEEZE = 34,
  ANEURALNETWORKS_STRIDED_SLICE = 35,
  ANEURALNETWORKS_SUB = 36,
  ANEURALNETWORKS_TRANSPOSE = 37,
  ANEURALNETWORKS_ABS = 38,
  ANEURALNETWORKS_ARGMAX = 39,
  ANEURALNETWORKS_ARGMIN = 40,
  ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_LSTM = 42,
  ANEURALNETWORKS_CAST = 45,
  ANEURALNETWORKS_EQUAL = 48,
  ANEURALNETWORKS_EXP = 49,
  ANEURALNETWORKS_EXPAND_DIMS = 50,
  ANEURALNETWORKS_GATHER = 51,
  ANEURALNETWORKS_GREATER = 53,
  ANEURALNETWORKS_GREATER_EQUAL = 54,
  ANEURALNETWORKS_LESS = 58,
  ANEURALNETWORKS_LESS_EQUAL = 59,
  ANEURALNETWORKS_LOG = 60,
  ANEURALNETWORKS_LOGICAL_AND = 61,
  ANEURALNETWORKS_LOGICAL_NOT = 62,
  ANEURALNETWORKS_LOGICAL_OR = 63,
  ANEURALNETWORKS_LOG_SOFTMAX = 64,
  ANEURALNETWORKS_MAXIMUM = 65,
  ANEURALNETWORKS_MINIMUM = 66,
  ANEURALNETWORKS_NEG = 67,
  ANEURALNETWORKS_NOT_EQUAL = 68,
  ANEURALNETWORKS_PAD_V2 = 69,
  ANEURALNETWORKS_POW = 70,
  ANEURALNETWORKS_PRELU = 71,
  ANEURALNETWORKS_QUANTIZE = 72,
  ANEURALNETWORKS_QUANTIZED_16BIT_LSTM = 73,
  ANEURALNETWORKS_REDUCE_ANY = 76,
  ANEURALNETWORKS_REDUCE_MAX = 77,
  ANEURALNETWORKS_REDUCE_MIN = 78,
  ANEURALNETWORKS_REDUCE_PROD = 79,
  ANEURALNETWORKS_REDUCE_SUM = 80,
  ANEURALNETWORKS_RSQRT = 83,
  ANEURALNETWORKS_SELECT = 84,
  ANEURALNETWORKS_SIN = 85,
  ANEURALNETWORKS_SLICE = 86,
  ANEURALNETWORKS_SPLIT = 87,
  ANEURALNETWORKS_SQRT = 88,
  ANEURALNETWORKS_TILE = 89,
  ANEURALNETWORKS_TOPK_V2 = 90,
  ANEURALNETWORKS_TRANSPOSE_CONV = 91,
  ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
  ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_RNN = 93,
  ANEURALNETWORKS_RESIZE_NEAREST_NEIGHBOR = 94,
  ANEURALNETWORKS_QUANTIZED_LSTM = 95,
  ANEURALNETWORKS_IF = 96,
  ANEURALNETWORKS_WHILE = 97,
  ANEURALNETWORKS_ELU = 98,
  ANEURALNETWORKS_HARD_SWISH = 99,
  ANEURALNETWORKS_FILL = 100,
  ANEURALNETWORKS_RANK = 101,
};

/**
 * Fused activation function types.
 *
 */
enum {
  ANEURALNETWORKS_FUSED_NONE = 0,
  ANEURALNETWORKS_FUSED_RELU = 1,
  ANEURALNETWORKS_FUSED_RELU1 = 2,
  ANEURALNETWORKS_FUSED_RELU6 = 3,
};

/**
 * Execution preferences.
 */
enum {
  ANEURALNETWORKS_PREFER_LOW_POWER = 0,
  ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1,
  ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2,
};

/**
 * Result codes.
 */
// LINT.IfChange
enum {
  ANEURALNETWORKS_NO_ERROR = 0,
  ANEURALNETWORKS_OUT_OF_MEMORY = 1,
  ANEURALNETWORKS_INCOMPLETE = 2,
  ANEURALNETWORKS_UNEXPECTED_NULL = 3,
  ANEURALNETWORKS_BAD_DATA = 4,
  ANEURALNETWORKS_OP_FAILED = 5,
  ANEURALNETWORKS_BAD_STATE = 6,
  ANEURALNETWORKS_UNMAPPABLE = 7,
  ANEURALNETWORKS_OUTPUT_INSUFFICIENT_SIZE = 8,
  ANEURALNETWORKS_UNAVAILABLE_DEVICE = 9,
  ANEURALNETWORKS_MISSED_DEADLINE_TRANSIENT = 10,
  ANEURALNETWORKS_MISSED_DEADLINE_PERSISTENT = 11,
  ANEURALNETWORKS_RESOURCE_EXHAUSTED_TRANSIENT = 12,
  ANEURALNETWORKS_RESOURCE_EXHAUSTED_PERSISTENT = 13,
  ANEURALNETWORKS_DEAD_OBJECT = 14,
};
// LINT.ThenChange(//tensorflow/lite/delegates/nnapi/nnapi_delegate.cc:NnApiErrorDescription)

/**
 * Implicit padding algorithms.
 */
enum {
  ANEURALNETWORKS_PADDING_SAME = 1,
  ANEURALNETWORKS_PADDING_VALID = 2,
};

/**
 * Device types.
 *
 * The type of NNAPI device.
 */
enum {
  /** The device type cannot be provided. */
  ANEURALNETWORKS_DEVICE_UNKNOWN = 0,
  /** The device does not fall into any category below. */
  ANEURALNETWORKS_DEVICE_OTHER = 1,
  /** The device runs NNAPI models on single or multi-core CPU. */
  ANEURALNETWORKS_DEVICE_CPU = 2,
  /** The device can run NNAPI models and also accelerate graphics APIs such
   * as OpenGL ES and Vulkan. */
  ANEURALNETWORKS_DEVICE_GPU = 3,
  /** Dedicated accelerator for Machine Learning workloads. */
  ANEURALNETWORKS_DEVICE_ACCELERATOR = 4,
};

/**
 * Relative execution priority.
 *
 * Available since API level 30.
 */
enum {
  ANEURALNETWORKS_PRIORITY_LOW = 90,
  ANEURALNETWORKS_PRIORITY_MEDIUM = 100,
  ANEURALNETWORKS_PRIORITY_HIGH = 110,
  ANEURALNETWORKS_PRIORITY_DEFAULT = ANEURALNETWORKS_PRIORITY_MEDIUM,
};

/**
 * NNAPI feature levels.
 *
 * Each update of the NNAPI specification yields a new NNAPI feature level enum
 * value. NNAPI feature level corrseponds to an NNAPI specification version that
 * a driver and/or the NNAPI runtime can implement.
 */
enum {
  /** NNAPI specification available in Android O-MR1, Android NNAPI feature
     level 1 */
  ANEURALNETWORKS_FEATURE_LEVEL_1 = 27,
  /** NNAPI specification available in Android P, Android NNAPI feature level 2
   */
  ANEURALNETWORKS_FEATURE_LEVEL_2 = 28,
  /** NNAPI specification available in Android Q, Android NNAPI feature level 3
   */
  ANEURALNETWORKS_FEATURE_LEVEL_3 = 29,
  /** NNAPI specification available in Android R, Android NNAPI feature level 4
   */
  ANEURALNETWORKS_FEATURE_LEVEL_4 = 30,
  /**
   * NNAPI specification available in Android S, Android NNAPI feature level 5.
   * After Android S, the NNAPI specification can be updated between Android
   * API releases.
   */
  ANEURALNETWORKS_FEATURE_LEVEL_5 = 31,
};

/**
 * ANeuralNetworksMemoryDesc is an opaque type that represents a memory
 * descriptor.
 *
 * A memory descriptor describes the properties of a memory object, and is used
 * by
 * {@link ANeuralNetworksMemory_createFromDesc}.
 *
 * To use:
 *   - Create a new memory descriptor by calling
 *     {@link ANeuralNetworksMemoryDesc_create}.
 *   - Specify all of the intended input and output roles by calling
 *     {@link ANeuralNetworksMemoryDesc_addInputRole} and
 *     {@link ANeuralNetworksMemoryDesc_addOutputRole}.
 *   - Optionally, specify the memory dimensions by calling
 *     {@link ANeuralNetworksMemoryDesc_setDimensions}.
 *   - Complete the memory descriptor with {@link
 * ANeuralNetworksMemoryDesc_finish}.
 *   - Use the memory descriptor as many times as needed with
 *     {@link ANeuralNetworksMemory_createFromDesc}.
 *   - Destroy the memory descriptor with {@link
 * ANeuralNetworksMemoryDesc_free}.
 *
 * A memory descriptor is completed by calling {@link
 * ANeuralNetworksMemoryDesc_finish}. A memory descriptor is destroyed by
 * calling {@link ANeuralNetworksMemoryDesc_free}.
 *
 * A memory descriptor must not be modified once
 * {@link ANeuralNetworksMemoryDesc_finish}
 * has been called on it.
 *
 * It is the application's responsibility to make sure that only
 * one thread modifies a memory descriptor at a given time. It is however
 * safe for more than one thread to use the memory descriptor once
 * {@link ANeuralNetworksMemoryDesc_finish} has returned.
 *
 * It is also the application's responsibility to ensure that there are no other
 * uses of the memory descriptor after calling {@link
 * ANeuralNetworksMemoryDesc_free}. It is however safe to continue using a
 * {@link ANeuralNetworksMemory} object created from the memory descriptor.
 *
 * Available since API level 30.
 */
typedef struct ANeuralNetworksMemoryDesc ANeuralNetworksMemoryDesc;

/**
 * ANeuralNetworksMemory is an opaque type that represents memory.
 *
 * This type is used to represent shared memory, memory mapped files,
 * and similar memories.
 *
 * By using shared memory, a program can efficiently communicate to the
 * runtime and drivers the tensors that define a model. See
 * {@link ANeuralNetworksModel_setOperandValueFromMemory}. An application
 * should typically create one shared memory object that contains every tensor
 * needed to define a model. {@link ANeuralNetworksMemory_createFromFd} can be
 * used to create shared memory from a file handle. {@link
 * ANeuralNetworksMemory_createShared} can be used to directly created shared
 * memory.
 *
 * Memory objects can also be used to specify the input and output arguments of
 * an execution. See {@link ANeuralNetworksExecution_setInputFromMemory}
 * and {@link ANeuralNetworksExecution_setOutputFromMemory}.
 */
typedef struct ANeuralNetworksMemory ANeuralNetworksMemory;

/**
 * ANeuralNetworksModel is an opaque type that contains a description of the
 * mathematical operations that constitute the model.
 *
 * <p>The model will be built by calling<ul>
 * <li>{@link ANeuralNetworksModel_create},</li>
 * <li>{@link ANeuralNetworksModel_addOperation},</li>
 * <li>{@link ANeuralNetworksModel_addOperand},</li>
 * </ul>
 *
 * A model is completed by calling {@link ANeuralNetworksModel_finish}.
 * A model is destroyed by calling {@link ANeuralNetworksModel_free}.
 *
 * <p>It is the application's responsibility to make sure that only one thread
 * modifies a model at a given time. It is however safe for more than one
 * thread to use the model once {@link ANeuralNetworksModel_finish} has
 * returned.</p>
 *
 * <p>It is also the application's responsibility to ensure that there are no
 * other uses of the model after calling {@link ANeuralNetworksModel_free}. This
 * includes any compilation or execution object created using the model.</p>
 */
typedef struct ANeuralNetworksModel ANeuralNetworksModel;

/**
 * ANeuralNetworksCompilation is an opaque type that can be used to compile
 * a machine learning model.
 *
 * <p>To use:<ul>
 *    <li>Create a new compilation instance by calling the
 *        {@link ANeuralNetworksCompilation_create} function.</li>
 *    <li>Perform the compilation with {@link
 * ANeuralNetworksCompilation_start}.</li> <li>Wait for the compilation to
 * complete with {@link ANeuralNetworksCompilation_wait}.</li> <li>Use the
 * compilation as many times as needed with {@link
 * ANeuralNetworksExecution_create}.</li> <li>Destroy the compilation with
 * {@link ANeuralNetworksCompilation_free} once all executions using the
 * compilation have completed.</li></ul></p>
 *
 * <p>A compilation cannot be modified once {@link
 * ANeuralNetworksCompilation_start} has been called on it.</p>
 *
 * <p>It is the application's responsibility to make sure that only one thread
 * modifies a compilation at a given time. It is however safe for more than one
 * thread to use {@link ANeuralNetworksCompilation_wait} at the same time.
 * It is also safe for multiple threads to use a compilation object once
 * {@link ANeuralNetworksCompilation_wait} has completed.</p>
 *
 * <p>It is also the application's responsibility to ensure that there are no
 * other uses of the compilation after calling {@link
 * ANeuralNetworksCompilation_free}. This includes any execution object created
 * using the compilation.</p>
 */
typedef struct ANeuralNetworksCompilation ANeuralNetworksCompilation;

/**
 * ANeuralNetworksExecution is an opaque type that can be used to apply a
 * machine learning model to a set of inputs.
 *
 * <p>To use:<ul>
 *    <li>Create a new execution instance by calling the
 *        {@link ANeuralNetworksExecution_create} function.</li>
 *    <li>Associate data to the model inputs with
 *        {@link ANeuralNetworksExecution_setInput} or
 *        {@link ANeuralNetworksExecution_setInputFromMemory}.</li>
 *    <li>Associate output buffers to the model outputs with
 *        {@link ANeuralNetworksExecution_setOutput} or
 *        {@link ANeuralNetworksExecution_setOutputFromMemory}.</li>
 *    <li>Apply the model with {@link
 * ANeuralNetworksExecution_startCompute}.</li> <li>Wait for the execution to
 * complete with {@link ANeuralNetworksExecution_wait}.</li> <li>Destroy the
 * execution with
 *        {@link ANeuralNetworksExecution_free}.</li></ul></p>
 *
 * <p>An execution cannot be modified once {@link
 * ANeuralNetworksExecution_start} has been called on it.</p>
 *
 * <p>An execution can be applied to a model with
 * {@link ANeuralNetworksExecution_startCompute} only once. Create new
 * executions to do new evaluations of the model.</p>
 *
 * <p>It is the application's responsibility to make sure that only one thread
 * modifies an execution at a given time. It is however safe for more than one
 * thread to use {@link ANeuralNetworksExecution_wait} at the same time.</p>
 *
 * <p>It is also the application's responsibility to ensure that there are no
 * other uses of the request after calling {@link
 * ANeuralNetworksRequest_free}.</p>
 */
typedef struct ANeuralNetworksExecution ANeuralNetworksExecution;

/**
 * Parameters for ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
 */
typedef struct ANeuralNetworksSymmPerChannelQuantParams {
  /* The index of the channel dimension. */
  uint32_t channelDim;
  /** The size of the scale array. Should be equal to dimension[channelDim] of
   * the Operand. */
  uint32_t scaleCount;
  /** The array of scaling values for each channel. Each value must be greater
   * than zero. */
  const float* scales;
} ANeuralNetworksSymmPerChannelQuantParams;

/**
 * ANeuralNetworksBurst is an opaque type that can be used to reduce the latency
 * of a rapid sequence of executions. It will likely cause overhead if only used
 * for a single execution.
 *
 * ANeuralNetworksBurst serves as a context object for any number of inferences
 * using {@link ANeuralNetworksExecution} objects. An ANeuralNetworksBurst
 * object and the {@link ANeuralNetworksExecution} objects used with it must all
 * have been created from the same {@link ANeuralNetworksCompilation} object.
 *
 * This object is also used as a hint to drivers, providing insight to the
 * lifetime of a rapid sequence of executions. For example, a driver may choose
 * to increase the clock frequency of its accelerator for the lifetime of a
 * burst object.
 *
 * <p>To use:<ul>
 *    <li>Create a new burst object by calling the
 *        {@link ANeuralNetworksBurst_create} function.</li>
 *    <li>For each execution:</li><ul>
 *        <li>Create {@link ANeuralNetworksExecution} and configure its
 *            properties (see {@link ANeuralNetworksExecution} for
 * details).</li> <li>Apply the model synchronously with
 *            {@link ANeuralNetworksExecution_burstCompute}, reusing the same
 *            {@link ANeuralNetworksBurst} with the new
 *            {@link ANeuralNetworksExecution}.</li>
 *        <li>Use and free the {@link ANeuralNetworksExecution}.</li></ul>
 *    <li>Destroy the burst with
 *        {@link ANeuralNetworksBurst_free}.</li></ul></p>
 *
 * Available since API level 29.
 */
typedef struct ANeuralNetworksBurst ANeuralNetworksBurst;

/**
 * ANeuralNetworksOperandType describes the type of an operand.
 * This structure is used to describe both scalars and tensors.
 */
typedef struct ANeuralNetworksOperandType {
  /** The data type, e.g ANEURALNETWORKS_INT8. */
  int32_t type;
  /** The number of dimensions. It should be 0 for scalars. */
  uint32_t dimensionCount;
  /** The dimensions of the tensor. It should be nullptr for scalars. */
  const uint32_t* dimensions;
  /** These two fields are only used for quantized tensors.
   * They should be zero for scalars and non-fixed point tensors.
   * The dequantized value of each entry is (value - offset) * scale.
   */
  float scale;
  int32_t zeroPoint;
} ANeuralNetworksOperandType;

/**
 * ANeuralNetworksEvent is an opaque type that represents an event
 * that will be signaled once an execution completes.
 */
typedef struct ANeuralNetworksEvent ANeuralNetworksEvent;

typedef int32_t ANeuralNetworksOperationType;

/**
 * ANeuralNetworksDevice is an opaque type that represents a device.
 *
 * This type is used to query basic properties and supported operations of the
 * corresponding device, and control which device(s) a model is to be run on.
 *
 * Available since API level 29.
 */
typedef struct ANeuralNetworksDevice ANeuralNetworksDevice;

// nn api function types

typedef int (*ANeuralNetworksMemory_createFromFd_fn)(
    size_t size, int protect, int fd, size_t offset,
    ANeuralNetworksMemory** memory);

typedef void (*ANeuralNetworksMemory_free_fn)(ANeuralNetworksMemory* memory);

typedef int (*ANeuralNetworksModel_create_fn)(ANeuralNetworksModel** model);

typedef int (*ANeuralNetworksModel_finish_fn)(ANeuralNetworksModel* model);

typedef void (*ANeuralNetworksModel_free_fn)(ANeuralNetworksModel* model);

typedef int (*ANeuralNetworksCompilation_create_fn)(
    ANeuralNetworksModel* model, ANeuralNetworksCompilation** compilation);

typedef void (*ANeuralNetworksCompilation_free_fn)(
    ANeuralNetworksCompilation* compilation);

typedef int (*ANeuralNetworksCompilation_setPreference_fn)(
    ANeuralNetworksCompilation* compilation, int32_t preference);

typedef int (*ANeuralNetworksCompilation_finish_fn)(
    ANeuralNetworksCompilation* compilation);

typedef int (*ANeuralNetworksModel_addOperand_fn)(
    ANeuralNetworksModel* model, const ANeuralNetworksOperandType* type);

typedef int (*ANeuralNetworksModel_setOperandValue_fn)(
    ANeuralNetworksModel* model, int32_t index, const void* buffer,
    size_t length);

typedef int (*ANeuralNetworksModel_setOperandSymmPerChannelQuantParams_fn)(
    ANeuralNetworksModel* model, int32_t index,
    const ANeuralNetworksSymmPerChannelQuantParams* channelQuant);

typedef int (*ANeuralNetworksModel_setOperandValueFromMemory_fn)(
    ANeuralNetworksModel* model, int32_t index,
    const ANeuralNetworksMemory* memory, size_t offset, size_t length);

typedef int (*ANeuralNetworksModel_addOperation_fn)(
    ANeuralNetworksModel* model, ANeuralNetworksOperationType type,
    uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount,
    const uint32_t* outputs);

typedef int (*ANeuralNetworksModel_identifyInputsAndOutputs_fn)(
    ANeuralNetworksModel* model, uint32_t inputCount, const uint32_t* inputs,
    uint32_t outputCount, const uint32_t* outputs);

typedef int (*ANeuralNetworksModel_relaxComputationFloat32toFloat16_fn)(
    ANeuralNetworksModel* model, bool allow);

typedef int (*ANeuralNetworksExecution_create_fn)(
    ANeuralNetworksCompilation* compilation,
    ANeuralNetworksExecution** execution);

typedef void (*ANeuralNetworksExecution_free_fn)(
    ANeuralNetworksExecution* execution);

typedef int (*ANeuralNetworksExecution_setInput_fn)(
    ANeuralNetworksExecution* execution, int32_t index,
    const ANeuralNetworksOperandType* type, const void* buffer, size_t length);

typedef int (*ANeuralNetworksExecution_setInputFromMemory_fn)(
    ANeuralNetworksExecution* execution, int32_t index,
    const ANeuralNetworksOperandType* type, const ANeuralNetworksMemory* memory,
    size_t offset, size_t length);

typedef int (*ANeuralNetworksExecution_setOutput_fn)(
    ANeuralNetworksExecution* execution, int32_t index,
    const ANeuralNetworksOperandType* type, void* buffer, size_t length);

typedef int (*ANeuralNetworksExecution_setOutputFromMemory_fn)(
    ANeuralNetworksExecution* execution, int32_t index,
    const ANeuralNetworksOperandType* type, const ANeuralNetworksMemory* memory,
    size_t offset, size_t length);

typedef int (*ANeuralNetworksExecution_startCompute_fn)(
    ANeuralNetworksExecution* execution, ANeuralNetworksEvent** event);

typedef int (*ANeuralNetworksEvent_wait_fn)(ANeuralNetworksEvent* event);

typedef void (*ANeuralNetworksEvent_free_fn)(ANeuralNetworksEvent* event);

typedef int (*ASharedMemory_create_fn)(const char* name, size_t size);

typedef int (*ANeuralNetworks_getDeviceCount_fn)(uint32_t* numDevices);

typedef int (*ANeuralNetworks_getDevice_fn)(uint32_t devIndex,
                                            ANeuralNetworksDevice** device);

typedef int (*ANeuralNetworksDevice_getName_fn)(
    const ANeuralNetworksDevice* device, const char** name);

typedef int (*ANeuralNetworksDevice_getType_fn)(
    const ANeuralNetworksDevice* device, int32_t* type);

typedef int (*ANeuralNetworksDevice_getVersion_fn)(
    const ANeuralNetworksDevice* device, const char** version);

typedef int (*ANeuralNetworksDevice_getFeatureLevel_fn)(
    const ANeuralNetworksDevice* device, int64_t* featureLevel);

typedef int (*ANeuralNetworksModel_getSupportedOperationsForDevices_fn)(
    const ANeuralNetworksModel* model,
    const ANeuralNetworksDevice* const* devices, uint32_t numDevices,
    bool* supportedOps);

typedef int (*ANeuralNetworksCompilation_createForDevices_fn)(
    ANeuralNetworksModel* model, const ANeuralNetworksDevice* const* devices,
    uint32_t numDevices, ANeuralNetworksCompilation** compilation);

typedef int (*ANeuralNetworksCompilation_setCaching_fn)(
    ANeuralNetworksCompilation* compilation, const char* cacheDir,
    const uint8_t* token);

typedef int (*ANeuralNetworksCompilation_setTimeout_fn)(
    ANeuralNetworksCompilation* compilation, uint64_t duration);

typedef int (*ANeuralNetworksCompilation_setPriority_fn)(
    ANeuralNetworksCompilation* compilation, int priority);

typedef int (*ANeuralNetworksExecution_compute_fn)(
    ANeuralNetworksExecution* execution);

typedef int (*ANeuralNetworksExecution_setTimeout_fn)(
    ANeuralNetworksExecution* execution, uint64_t duration);

typedef int (*ANeuralNetworksExecution_setLoopTimeout_fn)(
    ANeuralNetworksExecution* execution, uint64_t duration);

typedef int (*ANeuralNetworksExecution_getOutputOperandRank_fn)(
    ANeuralNetworksExecution* execution, int32_t index, uint32_t* rank);

typedef int (*ANeuralNetworksExecution_getOutputOperandDimensions_fn)(
    ANeuralNetworksExecution* execution, int32_t index, uint32_t* dimensions);

typedef int (*ANeuralNetworksBurst_create_fn)(
    ANeuralNetworksCompilation* compilation, ANeuralNetworksBurst** burst);

typedef void (*ANeuralNetworksBurst_free_fn)(ANeuralNetworksBurst* burst);

typedef int (*ANeuralNetworksExecution_burstCompute_fn)(
    ANeuralNetworksExecution* execution, ANeuralNetworksBurst* burst);

typedef int (*ANeuralNetworksMemory_createFromAHardwareBuffer_fn)(
    const AHardwareBuffer* ahwb, ANeuralNetworksMemory** memory);

typedef int (*ANeuralNetworksExecution_setMeasureTiming_fn)(
    ANeuralNetworksExecution* execution, bool measure);

typedef enum {
  // Execution time on hardware (not driver, which runs on host processor).
  ANEURALNETWORKS_DURATION_ON_HARDWARE = 0,
  // Execution time in driver (including time on hardware).  Excludes overhead
  // such as that of the runtime itself and the IPC needed for the runtime to
  // communicate with the driver.
  ANEURALNETWORKS_DURATION_IN_DRIVER = 1,
  // Execution time on hardware, after all dependencies have been signaled.
  // If no dependencies specified (for example, if the execution was scheduled
  // other
  // than with {@link ANeuralNetworksExecution_startComputeWithDependencies}),
  // the
  // reported time will be the same as ANEURALNETWORKS_DURATION_ON_HARDWARE.
  // Available since API level 30.
  ANEURALNETWORKS_FENCED_DURATION_ON_HARDWARE = 2,
  // Execution time in driver, after all dependencies have been signaled.
  // Excludes
  // overhead such as that of the runtime itself and the IPC needed for the
  // runtime
  // to communicate with the driver.
  // If no dependencies specified (for example, if the execution was scheduled
  // other
  // than with {@link ANeuralNetworksExecution_startComputeWithDependencies}),
  // the
  // reported time will be the same as ANEURALNETWORKS_DURATION_IN_DRIVER.
  // Available since API level 30.
  ANEURALNETWORKS_FENCED_DURATION_IN_DRIVER = 3,
} DurationCode;

typedef int (*ANeuralNetworksExecution_getDuration_fn)(
    const ANeuralNetworksExecution* execution, int32_t durationCode,
    uint64_t* duration);

typedef int (*ANeuralNetworksDevice_getExtensionSupport_fn)(
    const ANeuralNetworksDevice* device, const char* extensionName,
    bool* isExtensionSupported);

typedef int (*ANeuralNetworksModel_getExtensionOperandType_fn)(
    ANeuralNetworksModel* model, const char* extensionName,
    uint16_t operandCodeWithinExtension, int32_t* type);

typedef int (*ANeuralNetworksModel_getExtensionOperationType_fn)(
    ANeuralNetworksModel* model, const char* extensionName,
    uint16_t operationCodeWithinExtension, ANeuralNetworksOperationType* type);

typedef int (*ANeuralNetworksModel_setOperandExtensionData_fn)(
    ANeuralNetworksModel* model, int32_t index, const void* data,
    size_t length);

typedef int (*ANeuralNetworksMemoryDesc_create_fn)(
    ANeuralNetworksMemoryDesc** desc);

typedef void (*ANeuralNetworksMemoryDesc_free_fn)(
    ANeuralNetworksMemoryDesc* desc);

typedef int (*ANeuralNetworksMemoryDesc_addInputRole_fn)(
    ANeuralNetworksMemoryDesc* desc,
    const ANeuralNetworksCompilation* compilation, uint32_t index,
    float frequency);

typedef int (*ANeuralNetworksMemoryDesc_addOutputRole_fn)(
    ANeuralNetworksMemoryDesc* desc,
    const ANeuralNetworksCompilation* compilation, uint32_t index,
    float frequency);

typedef int (*ANeuralNetworksMemoryDesc_setDimensions_fn)(
    ANeuralNetworksMemoryDesc* desc, uint32_t rank, const uint32_t* dimensions);

typedef int (*ANeuralNetworksMemoryDesc_finish_fn)(
    ANeuralNetworksMemoryDesc* desc);

typedef int (*ANeuralNetworksMemory_createFromDesc_fn)(
    const ANeuralNetworksMemoryDesc* desc, ANeuralNetworksMemory** memory);

typedef int (*ANeuralNetworksMemory_copy_fn)(const ANeuralNetworksMemory* src,
                                             const ANeuralNetworksMemory* dst);

typedef int (*ANeuralNetworksEvent_createFromSyncFenceFd_fn)(
    int sync_fence_fd, ANeuralNetworksEvent** event);

typedef int (*ANeuralNetworksEvent_getSyncFenceFd_fn)(
    const ANeuralNetworksEvent* event, int* sync_fence_fd);

typedef int (*ANeuralNetworksExecution_startComputeWithDependencies_fn)(
    ANeuralNetworksExecution* execution,
    const ANeuralNetworksEvent* const* dependencies, uint32_t num_dependencies,
    uint64_t duration, ANeuralNetworksEvent** event);

typedef int (*ANeuralNetworksExecution_enableInputAndOutputPadding_fn)(
    ANeuralNetworksExecution* execution, bool enable);

typedef int (*ANeuralNetworksExecution_setReusable_fn)(
    ANeuralNetworksExecution* execution, bool reusable);

typedef int64_t (*ANeuralNetworks_getRuntimeFeatureLevel_fn)();
#endif  // TENSORFLOW_LITE_NNAPI_NEURALNETWORKSTYPES_H_
