/* Copyright 2019-2022 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;

import static org.tensorflow.internal.c_api.global.tensorflow.TF_DeleteBuffer;
import static org.tensorflow.internal.c_api.global.tensorflow.TF_DeleteLibraryHandle;
import static org.tensorflow.internal.c_api.global.tensorflow.TF_GetAllOpList;
import static org.tensorflow.internal.c_api.global.tensorflow.TF_GetOpList;
import static org.tensorflow.internal.c_api.global.tensorflow.TF_LoadLibrary;
import static org.tensorflow.internal.c_api.global.tensorflow.TF_RegisterFilesystemPlugin;
import static org.tensorflow.internal.c_api.global.tensorflow.TF_Version;

import com.google.protobuf.InvalidProtocolBufferException;
import java.util.Collections;
import java.util.IdentityHashMap;
import java.util.Set;
import java.util.stream.Collectors;
import org.bytedeco.javacpp.PointerPointer;
import org.bytedeco.javacpp.PointerScope;
import org.tensorflow.exceptions.TensorFlowException;
import org.tensorflow.internal.c_api.GradFunc;
import org.tensorflow.internal.c_api.GradOpRegistry;
import org.tensorflow.internal.c_api.NativeStatus;
import org.tensorflow.internal.c_api.TF_Buffer;
import org.tensorflow.internal.c_api.TF_Library;
import org.tensorflow.internal.c_api.TF_Status;
import org.tensorflow.op.CustomGradient;
import org.tensorflow.op.RawCustomGradient;
import org.tensorflow.op.RawOpInputs;
import org.tensorflow.op.annotation.OpInputsMetadata;
import org.tensorflow.op.annotation.OpMetadata;
import org.tensorflow.op.math.Add;
import org.tensorflow.proto.framework.OpList;

/** Static utility methods describing the TensorFlow runtime. */
public final class TensorFlow {

  /** Returns the version of the underlying TensorFlow runtime. */
  public static String version() {
    return TF_Version().getString();
  }

  /**
   * All the TensorFlow operations available in this address space.
   *
   * @return A <a
   *     href="https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto">OpList</a>
   *     protocol buffer, which lists all the available TensorFlow operations.
   */
  public static OpList registeredOpList() {
    TF_Buffer buf = TF_GetAllOpList();
    try {
      return OpList.parseFrom(buf.dataAsByteBuffer());
    } catch (InvalidProtocolBufferException e) {
      throw new TensorFlowException("Cannot parse OpList protocol buffer", e);
    } finally {
      TF_DeleteBuffer(buf);
    }
  }

  private static Set<String> statefulOps;

  public static synchronized boolean isOpStateful(String opType) {
    if (statefulOps == null) {
      statefulOps =
          registeredOpList().getOpList().stream()
              .filter(x -> x.getIsStateful())
              .map(x -> x.getName())
              .collect(Collectors.toSet());
    }

    return statefulOps.contains(opType);
  }

  /**
   * Load the dynamic library in filename and register the operations and kernels present in that
   * library.
   *
   * @param filename Path of the dynamic library containing operations and kernels to load.
   * @return A <a
   *     href="https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto">OpList</a>
   *     protocol buffer message defining the operations defined in the library.
   * @throws UnsatisfiedLinkError if filename cannot be loaded.
   */
  public static OpList loadLibrary(String filename) {
    TF_Library h = null;
    try {
      h = libraryLoad(filename);
    } catch (RuntimeException e) {
      throw new UnsatisfiedLinkError(e.getMessage());
    }
    try {
      return libraryOpList(h);
    } finally {
      libraryDelete(h);
    }
  }

  /**
   * Loads the filesystem plugin from filename and registers all the filesystems it supports.
   *
   * <p>Throws a TF runtime exception if the plugin failed to load.
   *
   * @param filename Path of the dynamic library containing the filesystem support.
   */
  public static void registerFilesystemPlugin(String filename) {
    try (PointerScope scope = new PointerScope()) {
      TF_Status status = TF_Status.newStatus();
      TF_RegisterFilesystemPlugin(filename, status);
      status.throwExceptionIfNotOK();
    }
  }

  private static TF_Library libraryLoad(String filename) {
    try (PointerScope scope = new PointerScope()) {
      TF_Status status = TF_Status.newStatus();
      TF_Library h = TF_LoadLibrary(filename, status);
      status.throwExceptionIfNotOK();
      return h;
    }
  }

  private static void libraryDelete(TF_Library handle) {
    if (handle != null && !handle.isNull()) {
      TF_DeleteLibraryHandle(handle);
    }
  }

  private static OpList libraryOpList(TF_Library handle) {
    TF_Buffer buf = TF_GetOpList(handle);
    try {
      return OpList.parseFrom(buf.dataAsByteBuffer());
    } catch (InvalidProtocolBufferException e) {
      throw new TensorFlowException("Cannot parse OpList protocol buffer", e);
    }
  }

  private TensorFlow() {}

  /** Load the TensorFlow runtime C library. */
  static {
    try {
      NativeLibrary.load();
    } catch (Exception e) {
      /*
       * This code is called during static initialization of this and of other classes.
       * If this fails then a NoClassDefFoundError is thrown however this does not
       * include a cause. Printing the exception manually here ensures that the
       * necessary information to fix the problem is available.
       */
      System.err.println("Failed to load TensorFlow native library");
      e.printStackTrace();
      throw e;
    }
  }

  /**
   * Keeps references to custom gradient functions to prevent them from being deallocated. All
   * access of this set should be synchronized on this class.
   *
   * <p><b>Required for correctness</b>
   */
  private static final Set<GradFunc> gradientFuncs =
      Collections.newSetFromMap(new IdentityHashMap<>());

  private static synchronized boolean hasGradient(String opType) {
    try (PointerScope scope = new PointerScope()) {
      NativeStatus status =
          GradOpRegistry.Global().Lookup(opType, new GradFunc(new PointerPointer<>(1)));
      return status.ok();
    }
  }

  /**
   * Register a custom gradient function for ops of {@code opType} type.
   *
   * <p>Creates the gradient based off of a {@link GraphOperation}. To operate on the op input class
   * instead use {@link CustomGradient}.
   *
   * <p>Note that this only works with graph gradients, and will eventually be deprecated in favor
   * of unified gradient support once it is fully supported by tensorflow core.
   *
   * @param opType the type of op to register the gradient for. Should usually be an {@code OP_NAME}
   *     field, i.e. {@link Add#OP_NAME}.
   * @param gradient the gradient function to use
   * @return {@code true} if the gradient was registered, {@code false} if there was already a
   *     gradient registered for this op
   */
  public static synchronized boolean registerCustomGradient(
      String opType, RawCustomGradient gradient) {
    if (hasGradient(opType)) {
      return false;
    }
    GradFunc g = RawCustomGradient.adapter(gradient);
    GradOpRegistry.Global().Register(opType, g);
    gradientFuncs.add(g);
    return true;
  }

  /**
   * Register a custom gradient function for ops of {@code inputClass}'s op type. The actual op type
   * is detected from the class's {@link OpInputsMetadata} annotation. As such, it only works on
   * generated op classes or custom op classes with the correct annotations. To operate on the
   * {@link org.tensorflow.GraphOperation} directly use {@link RawCustomGradient}.
   *
   * @param inputClass the inputs class of op to register the gradient for.
   * @param gradient the gradient function to use
   * @return {@code true} if the gradient was registered, {@code false} if there was already a
   *     gradient registered for this op
   * @throws IllegalArgumentException if {@code inputClass} is not annotated with {@link
   *     OpInputsMetadata} or the op class is not annotated with {@link OpMetadata}.
   */
  public static synchronized <T extends RawOpInputs<?>> boolean registerCustomGradient(
      Class<T> inputClass, CustomGradient<T> gradient) {
    OpInputsMetadata metadata = inputClass.getAnnotation(OpInputsMetadata.class);

    if (metadata == null) {
      throw new IllegalArgumentException(
          "Inputs Class "
              + inputClass
              + " does not have a OpInputsMetadata annotation.  Was it generated by tensorflow/java?  If it was, this is a bug.");
    }
    OpMetadata outputMetadata = metadata.outputsClass().getAnnotation(OpMetadata.class);

    if (outputMetadata == null) {
      throw new IllegalArgumentException(
          "Op Class "
              + metadata.outputsClass()
              + " does not have a OpMetadata annotation.  Was it generated by tensorflow/java?  If it was, this is a bug.");
    }

    String opType = outputMetadata.opType();

    if (hasGradient(opType)) {
      return false;
    }

    GradFunc g = CustomGradient.adapter(gradient, inputClass);
    GradOpRegistry.Global().Register(opType, g);
    gradientFuncs.add(g);
    return true;
  }
}
