/* 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.
==============================================================================*/

package org.tensorflow.lite;

import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.MappedByteBuffer;
import java.util.HashMap;
import java.util.Map;

/**
 * An internal wrapper that wraps native interpreter and controls model execution.
 *
 * <p><b>WARNING:</b> Resources consumed by the {@code NativeInterpreterWrapper} object must be
 * explicitly freed by invoking the {@link #close()} method when the {@code
 * NativeInterpreterWrapper} object is no longer needed.
 */
final class NativeInterpreterWrapper implements AutoCloseable {

  NativeInterpreterWrapper(String modelPath) {
    this(modelPath, /* options= */ null);
  }

  NativeInterpreterWrapper(String modelPath, Interpreter.Options options) {
    if (options == null) {
      options = new Interpreter.Options();
    }
    errorHandle = createErrorReporter(ERROR_BUFFER_SIZE);
    modelHandle = createModel(modelPath, errorHandle);
    interpreterHandle = createInterpreter(modelHandle, errorHandle, options.numThreads);
    isMemoryAllocated = true;
    inputTensors = new Tensor[getInputCount(interpreterHandle)];
    outputTensors = new Tensor[getOutputCount(interpreterHandle)];
    if (options.allowFp16PrecisionForFp32) {
      setAllowFp16PrecisionForFp32(options.allowFp16PrecisionForFp32);
    }
  }

  NativeInterpreterWrapper(ByteBuffer byteBuffer) {
    this(byteBuffer, /* options= */ null);
  }

  NativeInterpreterWrapper(ByteBuffer buffer, Interpreter.Options options) {
    if (options == null) {
      options = new Interpreter.Options();
    }
    if (buffer == null
        || (!(buffer instanceof MappedByteBuffer)
            && (!buffer.isDirect() || buffer.order() != ByteOrder.nativeOrder()))) {
      throw new IllegalArgumentException(
          "Model ByteBuffer should be either a MappedByteBuffer of the model file, or a direct "
              + "ByteBuffer using ByteOrder.nativeOrder() which contains bytes of model content.");
    }
    modelByteBuffer = buffer;
    errorHandle = createErrorReporter(ERROR_BUFFER_SIZE);
    modelHandle = createModelWithBuffer(modelByteBuffer, errorHandle);
    interpreterHandle = createInterpreter(modelHandle, errorHandle, options.numThreads);
    isMemoryAllocated = true;
    inputTensors = new Tensor[getInputCount(interpreterHandle)];
    outputTensors = new Tensor[getOutputCount(interpreterHandle)];
    if (options.useNNAPI) {
      setUseNNAPI(options.useNNAPI);
    }
    if (options.allowFp16PrecisionForFp32) {
      setAllowFp16PrecisionForFp32(options.allowFp16PrecisionForFp32);
    }
  }

  /** Releases resources associated with this {@code NativeInterpreterWrapper}. */
  @Override
  public void close() {
    // Close the tensors first as they may reference the native interpreter.
    for (int i = 0; i < inputTensors.length; ++i) {
      if (inputTensors[i] != null) {
        inputTensors[i].close();
        inputTensors[i] = null;
      }
    }
    for (int i = 0; i < outputTensors.length; ++i) {
      if (outputTensors[i] != null) {
        outputTensors[i].close();
        outputTensors[i] = null;
      }
    }
    delete(errorHandle, modelHandle, interpreterHandle);
    errorHandle = 0;
    modelHandle = 0;
    interpreterHandle = 0;
    modelByteBuffer = null;
    inputsIndexes = null;
    outputsIndexes = null;
    isMemoryAllocated = false;
  }

  /** Sets inputs, runs model inference and returns outputs. */
  void run(Object[] inputs, Map<Integer, Object> outputs) {
    inferenceDurationNanoseconds = -1;
    if (inputs == null || inputs.length == 0) {
      throw new IllegalArgumentException("Input error: Inputs should not be null or empty.");
    }
    if (outputs == null || outputs.isEmpty()) {
      throw new IllegalArgumentException("Input error: Outputs should not be null or empty.");
    }

    // TODO(b/80431971): Remove implicit resize after deprecating multi-dimensional array inputs.
    // Rather than forcing an immediate resize + allocation if an input's shape differs, we first
    // flush all resizes, avoiding redundant allocations.
    for (int i = 0; i < inputs.length; ++i) {
      Tensor tensor = getInputTensor(i);
      int[] newShape = tensor.getInputShapeIfDifferent(inputs[i]);
      if (newShape != null) {
        resizeInput(i, newShape);
      }
    }

    boolean needsAllocation = !isMemoryAllocated;
    if (needsAllocation) {
      allocateTensors(interpreterHandle, errorHandle);
      isMemoryAllocated = true;
    }

    for (int i = 0; i < inputs.length; ++i) {
      getInputTensor(i).setTo(inputs[i]);
    }

    long inferenceStartNanos = System.nanoTime();
    run(interpreterHandle, errorHandle);
    long inferenceDurationNanoseconds = System.nanoTime() - inferenceStartNanos;

    // Allocation can trigger dynamic resizing of output tensors, so refresh all output shapes.
    if (needsAllocation) {
      for (int i = 0; i < outputTensors.length; ++i) {
        if (outputTensors[i] != null) {
          outputTensors[i].refreshShape();
        }
      }
    }
    for (Map.Entry<Integer, Object> output : outputs.entrySet()) {
      getOutputTensor(output.getKey()).copyTo(output.getValue());
    }

    // Only set if the entire operation succeeds.
    this.inferenceDurationNanoseconds = inferenceDurationNanoseconds;
  }

  private static native boolean run(long interpreterHandle, long errorHandle);

  /** Resizes dimensions of a specific input. */
  void resizeInput(int idx, int[] dims) {
    if (resizeInput(interpreterHandle, errorHandle, idx, dims)) {
      isMemoryAllocated = false;
      if (inputTensors[idx] != null) {
        inputTensors[idx].refreshShape();
      }
    }
  }

  private static native boolean resizeInput(
      long interpreterHandle, long errorHandle, int inputIdx, int[] dims);

  void setUseNNAPI(boolean useNNAPI) {
    useNNAPI(interpreterHandle, useNNAPI);
  }

  void setAllowFp16PrecisionForFp32(boolean allow) {
    allowFp16PrecisionForFp32(interpreterHandle, allow);
  }

  void setNumThreads(int numThreads) {
    numThreads(interpreterHandle, numThreads);
  }

  /** Gets index of an input given its name. */
  int getInputIndex(String name) {
    if (inputsIndexes == null) {
      String[] names = getInputNames(interpreterHandle);
      inputsIndexes = new HashMap<>();
      if (names != null) {
        for (int i = 0; i < names.length; ++i) {
          inputsIndexes.put(names[i], i);
        }
      }
    }
    if (inputsIndexes.containsKey(name)) {
      return inputsIndexes.get(name);
    } else {
      throw new IllegalArgumentException(
          String.format(
              "Input error: '%s' is not a valid name for any input. Names of inputs and their "
                  + "indexes are %s",
              name, inputsIndexes.toString()));
    }
  }

  /** Gets index of an output given its name. */
  int getOutputIndex(String name) {
    if (outputsIndexes == null) {
      String[] names = getOutputNames(interpreterHandle);
      outputsIndexes = new HashMap<>();
      if (names != null) {
        for (int i = 0; i < names.length; ++i) {
          outputsIndexes.put(names[i], i);
        }
      }
    }
    if (outputsIndexes.containsKey(name)) {
      return outputsIndexes.get(name);
    } else {
      throw new IllegalArgumentException(
          String.format(
              "Input error: '%s' is not a valid name for any output. Names of outputs and their "
                  + "indexes are %s",
              name, outputsIndexes.toString()));
    }
  }

  /**
   * Gets the last inference duration in nanoseconds. It returns null if there is no previous
   * inference run or the last inference run failed.
   */
  Long getLastNativeInferenceDurationNanoseconds() {
    return (inferenceDurationNanoseconds < 0) ? null : inferenceDurationNanoseconds;
  }

  /**
   * Gets the quantization zero point of an output.
   *
   * @throws IllegalArgumentException if the output index is invalid.
   */
  int getOutputQuantizationZeroPoint(int index) {
    return getOutputQuantizationZeroPoint(interpreterHandle, index);
  }

  /**
   * Gets the quantization scale of an output.
   *
   * @throws IllegalArgumentException if the output index is invalid.
   */
  float getOutputQuantizationScale(int index) {
    return getOutputQuantizationScale(interpreterHandle, index);
  }

  /** Gets the number of input tensors. */
  int getInputTensorCount() {
    return inputTensors.length;
  }

  /**
   * Gets the input {@link Tensor} for the provided input index.
   *
   * @throws IllegalArgumentException if the input index is invalid.
   */
  Tensor getInputTensor(int index) {
    if (index < 0 || index >= inputTensors.length) {
      throw new IllegalArgumentException("Invalid input Tensor index: " + index);
    }
    Tensor inputTensor = inputTensors[index];
    if (inputTensor == null) {
      inputTensor =
          inputTensors[index] =
              Tensor.fromIndex(interpreterHandle, getInputTensorIndex(interpreterHandle, index));
    }
    return inputTensor;
  }

  /** Gets the number of output tensors. */
  int getOutputTensorCount() {
    return inputTensors.length;
  }

  /**
   * Gets the output {@link Tensor} for the provided output index.
   *
   * @throws IllegalArgumentException if the output index is invalid.
   */
  Tensor getOutputTensor(int index) {
    if (index < 0 || index >= outputTensors.length) {
      throw new IllegalArgumentException("Invalid output Tensor index: " + index);
    }
    Tensor outputTensor = outputTensors[index];
    if (outputTensor == null) {
      outputTensor =
          outputTensors[index] =
              Tensor.fromIndex(interpreterHandle, getOutputTensorIndex(interpreterHandle, index));
    }
    return outputTensor;
  }

  private static native int getOutputDataType(long interpreterHandle, int outputIdx);

  private static native int getOutputQuantizationZeroPoint(long interpreterHandle, int outputIdx);

  private static native float getOutputQuantizationScale(long interpreterHandle, int outputIdx);

  private static final int ERROR_BUFFER_SIZE = 512;

  private long errorHandle;

  private long interpreterHandle;

  private long modelHandle;

  private long inferenceDurationNanoseconds = -1;

  private ByteBuffer modelByteBuffer;

  // Lazily constructed maps of input and output names to input and output Tensor indexes.
  private Map<String, Integer> inputsIndexes;
  private Map<String, Integer> outputsIndexes;

  // Lazily constructed and populated arrays of input and output Tensor wrappers.
  private final Tensor[] inputTensors;
  private final Tensor[] outputTensors;

  private boolean isMemoryAllocated = false;

  private static native long allocateTensors(long interpreterHandle, long errorHandle);

  private static native int getInputTensorIndex(long interpreterHandle, int inputIdx);

  private static native int getOutputTensorIndex(long interpreterHandle, int outputIdx);

  private static native int getInputCount(long interpreterHandle);

  private static native int getOutputCount(long interpreterHandle);

  private static native String[] getInputNames(long interpreterHandle);

  private static native String[] getOutputNames(long interpreterHandle);

  private static native void useNNAPI(long interpreterHandle, boolean state);

  private static native void numThreads(long interpreterHandle, int numThreads);

  private static native void allowFp16PrecisionForFp32(long interpreterHandle, boolean allow);

  private static native long createErrorReporter(int size);

  private static native long createModel(String modelPathOrBuffer, long errorHandle);

  private static native long createModelWithBuffer(ByteBuffer modelBuffer, long errorHandle);

  private static native long createInterpreter(long modelHandle, long errorHandle, int numThreads);

  private static native void delete(long errorHandle, long modelHandle, long interpreterHandle);

  static {
    TensorFlowLite.init();
  }
}
