/* Copyright 2021 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.
==============================================================================*/

#include "tensorflow/core/transforms/graph_to_func/graph_to_func.h"

#include <string>

#include "absl/status/status.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
#include "mlir/IR/Attributes.h"  // from @llvm-project
#include "mlir/IR/BuiltinAttributes.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/core/ir/dialect.h"
#include "tensorflow/core/ir/ops.h"
#include "tensorflow/core/ir/tf_op_wrapper.h"
#include "tensorflow/core/platform/errors.h"

using tensorflow::Status;
using tensorflow::errors::InvalidArgument;

namespace mlir {
namespace tfg {

static std::string OpResultToSlotName(OpResult value) {
  return (TFOp(*value.getDefiningOp()).name() + "_" +
          Twine(value.getResultNumber()))
      .str();
}

static ArrayAttr createLiftedValueAttr(OpBuilder &builder, OpResult value) {
  llvm::SmallVector<Attribute, 2> attrs = {
      TFOp(*value.getDefiningOp()).nameAttr(),
      builder.getIndexAttr(value.getResultNumber())};
  return builder.getArrayAttr(attrs);
}

absl::Status GraphToFunc(GraphOp graph, ArrayRef<Value> feeds,
                         ArrayRef<Value> fetches,
                         ArrayRef<Value> control_rets) {
  OpBuilder builder(graph);
  ControlType control_ty = ControlType::get(graph.getContext());
  llvm::SmallVector<Type> arg_types;
  llvm::SmallVector<Type> ret_types;
  for (Value feed : feeds) {
    arg_types.push_back(feed.getType());
    arg_types.push_back(control_ty);
  }
  for (Value fetch : fetches) ret_types.push_back(fetch.getType());

  auto *dialect = cast<TFGraphDialect>(graph->getDialect());
  StringRef func_name = dialect->getLiftedGraphFuncNameKey();

  FunctionType func_type = builder.getFunctionType(arg_types, ret_types);
  auto loc = graph.getLoc();
  auto func_op = GraphFuncOp::create(builder, loc, func_name, func_type,
                                     /*generic=*/false);
  func_op->setAttr("tfg.lifted_graph_version", graph.getVersion());
  func_op.getRegion().takeBody(graph.getRegion());

  // Create the returnOp first so that if there are nodes in both feeds and
  // fetches, the fetch value will be replaced with feed argument.
  OpBuilder body_builder =
      OpBuilder::atBlockEnd(func_op.SingleBlock::getBody());
  ReturnOp::create(body_builder, loc, fetches, control_rets);

  StringAttr tfg_name = dialect->getTfgNameAttrIdentifier();
  StringAttr lifted_value_name = builder.getStringAttr("tfg.lifted_value_attr");

  Block *body = func_op.SingleBlock::getBody();
  llvm::SmallVector<Attribute> args_rets_attrs;
  for (Value feed : feeds) {
    feed.replaceAllUsesWith(body->addArgument(feed.getType(), loc));
    body->addArgument(control_ty, loc);
    llvm::SmallVector<NamedAttribute> arg_attrs;
    std::string slot = OpResultToSlotName(mlir::cast<OpResult>(feed));
    arg_attrs.push_back(NamedAttribute(tfg_name, builder.getStringAttr(slot)));
    arg_attrs.push_back(NamedAttribute(
        lifted_value_name,
        createLiftedValueAttr(builder, mlir::cast<OpResult>(feed))));
    args_rets_attrs.push_back(builder.getDictionaryAttr(arg_attrs));
    args_rets_attrs.push_back(Attribute{});
  }
  func_op.setAllArgAttrs(args_rets_attrs);

  args_rets_attrs.clear();
  for (Value fetch : fetches) {
    llvm::SmallVector<NamedAttribute> arg_attrs;
    std::string slot = OpResultToSlotName(mlir::cast<OpResult>(fetch));
    arg_attrs.push_back(NamedAttribute(tfg_name, builder.getStringAttr(slot)));
    args_rets_attrs.push_back(builder.getDictionaryAttr(arg_attrs));
  }
  func_op.setAllResultAttrs(args_rets_attrs);

  graph.erase();
  return absl::OkStatus();
}

Status GraphToFunc(GraphOp graph, ArrayRef<std::string> feeds_names,
                   ArrayRef<std::string> fetches_names,
                   ArrayRef<std::string> control_rets_names) {
  DenseMap<StringRef, int> feeds_to_position;
  feeds_to_position.reserve(feeds_names.size());
  for (const auto &indexed_name : llvm::enumerate(feeds_names)) {
    const std::string &name = indexed_name.value();
    feeds_to_position.insert({StringRef(name), indexed_name.index()});
  }
  DenseMap<StringRef, int> fetches_to_position;
  fetches_to_position.reserve(fetches_names.size());
  for (const auto &indexed_name : llvm::enumerate(fetches_names)) {
    const std::string &name = indexed_name.value();
    fetches_to_position.insert({StringRef(name), indexed_name.index()});
  }
  DenseMap<StringRef, int> control_rets_to_position;
  control_rets_to_position.reserve(control_rets_names.size());
  for (const auto &indexed_name : llvm::enumerate(control_rets_names)) {
    control_rets_to_position.insert(
        {StringRef(indexed_name.value()), indexed_name.index()});
  }

  SmallVector<ValueRange> feeds(feeds_names.size());
  SmallVector<ValueRange> fetches(fetches_names.size());
  SmallVector<Value> control_rets(control_rets_names.size());
  for (Operation &op : *graph.getBody()) {
    TFOp tf_op(op);
    StringRef node_name = tf_op.name();

    // feeds and fetches are supposed to be mutually exclusive but control-ret
    // may include both of them.
    auto control_ret_pos = control_rets_to_position.find(node_name);
    if (control_ret_pos != control_rets_to_position.end()) {
      control_rets[control_ret_pos->second] = tf_op.controlRet();
    }
    auto feed_pos = feeds_to_position.find(node_name);
    if (feed_pos != feeds_to_position.end()) {
      feeds[feed_pos->second] = op.getResults().drop_back();
    }
    auto fetch_pos = fetches_to_position.find(node_name);
    if (fetch_pos != fetches_to_position.end()) {
      fetches[fetch_pos->second] = op.getResults().drop_back();
    }
  }

  SmallVector<Value> flattened_feeds;
  for (ValueRange values : feeds) {
    for (Value value : values) flattened_feeds.push_back(value);
  }

  SmallVector<Value> flattened_fetches;
  for (ValueRange values : fetches) {
    for (Value value : values) flattened_fetches.push_back(value);
  }

  llvm::erase_if(control_rets, [](Value value) { return !value; });

  return GraphToFunc(graph, flattened_feeds, flattened_fetches, control_rets);
}

}  // namespace tfg
}  // namespace mlir
