#include <torch/csrc/jit/ir.h>

#include <c10/util/Exception.h>
#include <torch/csrc/jit/constants.h>
#include <torch/csrc/jit/operator.h>
#include <torch/csrc/jit/passes/python_print.h>
#include <torch/csrc/jit/script/schema_matching.h>

#include <algorithm>
#include <iostream>
#include <set>
#include <sstream>
#include <stack>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>

namespace torch {
namespace jit {
// Constants relating to maintaining the topological index of nodes.
//
// Lower and upper bounds of the index. Inclusive range.
static constexpr topo_position_t kLowerBound = INT64_MIN;
static constexpr topo_position_t kUpperBound = INT64_MAX;
static constexpr topo_position_t kMidPoint = 0;
// How far away to space nodes that are appended to the graph.
// should be 2^n, where:
//   - n is the maximum number of repeated insertions without a re-index
//   - 2^(64-n) is the maximum number of appends to the end without reindex
static constexpr topo_position_t kAppendInterval = 1099511627776ULL /* 2^40 */;

// Sigh, see
// https://stackoverflow.com/questions/8016780/undefined-reference-to-static-constexpr-char
constexpr Symbol PythonOp::Kind;

static void printValueRef(std::ostream& out, const Value* n) {
  out << "%" << n->uniqueName();
}

// NB: This overload will become ambiguous with the one Caffe2 provides in its
// logging, if they ever intersect.
template <typename T>
std::ostream& operator<<(std::ostream& out, const std::vector<T>& nodes) {
  out << at::ArrayRef<T>{nodes};
  return out;
}

template <typename T>
static std::ostream& printValueRefs(
    std::ostream& out,
    const at::ArrayRef<T>& nodes) {
  size_t i = 0;
  for (auto n : nodes) {
    if (i++ > 0) {
      out << ", ";
    }
    printValueRef(out, n);
  }
  return out;
}

// Can't make these two overloads directly a template, it'll be ambiguous with
// the global printer for operator<<.

std::ostream& operator<<(
    std::ostream& out,
    const at::ArrayRef<const Value*>& nodes) {
  return printValueRefs(out, nodes);
}

std::ostream& operator<<(std::ostream& out, const at::ArrayRef<Value*>& nodes) {
  return printValueRefs(out, nodes);
}

struct const_value_list_with_types {
  const ArrayRef<const Value*> values;
  bool use_newlines;
  const_value_list_with_types(
      ArrayRef<const Value*> values,
      bool use_newlines = false)
      : values(values), use_newlines(use_newlines) {}
};

std::ostream& operator<<(std::ostream& out, const_value_list_with_types l) {
  size_t i = 0;
  for (auto n : l.values) {
    if (i++ > 0) {
      if (l.use_newlines) {
        // TODO: Indent here is hard-coded for "graph(": un-hard-code it
        out << "\n      ";
      } else {
        out << ", ";
      }
    }
    printValueRef(out, n);
    out << " : ";
    out << *n->type();
  }
  return out;
}

template <typename T>
static void printPrimList(std::ostream& out, const std::vector<T>& items) {
  out << "[";
  int i = 0;
  for (auto& item : items) {
    if (i++ > 0) {
      out << ", ";
    }
    out << item;
  }
  out << "]";
}

static std::string escapeString(std::string s) {
  std::vector<char> search = {'\n', '\t', '\v'};
  std::vector<std::string> replace = {"\\n", "\\t", "\\v"};
  for (size_t i = 0; i < search.size(); i++) {
    size_t pos = s.find(search[i]);
    while (pos != std::string::npos) {
      s.replace(pos, 1, replace[i]);
      pos = s.find(search[i], pos + 1);
    }
  }
  return s;
}

void Node::printAttrValue(std::ostream& out, const Symbol& name) const {
  switch (kindOf(name)) {
    case AttributeKind::f:
      out << f(name);
      break;
    case AttributeKind::fs:
      printPrimList(out, fs(name));
      break;
    case AttributeKind::i:
      out << i(name);
      break;
    case AttributeKind::is:
      printPrimList(out, is(name));
      break;
    case AttributeKind::s:
      out << "\"" << escapeString(s(name)) << "\"";
      break;
    case AttributeKind::ss:
      printPrimList(out, ss(name));
      break;
    case AttributeKind::t: {
      at::Tensor tensor = t(name);
      // 1-elem tensors are usually boxed scalars, so print them like it
      if (tensor.numel() == 1) {
        auto scalar_tensor = tensor.view({}).item();
        out << "{";
        if (scalar_tensor.isFloatingPoint()) {
          out << scalar_tensor.toDouble();
        } else {
          out << scalar_tensor.toLong();
        }
        out << "}";
      } else if (tensor.numel() <= max_tensor_display_size) {
        // TODO: This is awful code.  Also it doesn't work on Windows.
        std::ostringstream tensor_ss;
        tensor_ss << tensor;
        std::string tensor_s{tensor_ss.str()};
        // Remove newlines
        std::replace(tensor_s.begin(), tensor_s.end(), '\n', ' ');
        out << tensor_s;
      } else {
        out << "<Tensor>";
      }
      break;
    }
    case AttributeKind::ts:
      out << "[<Tensors>]";
      break;
    case AttributeKind::g:
      out << "<Graph>";
      break;
    case AttributeKind::gs:
      out << "[<Graphs>]";
      break;
  }
}

void Node::printAttributes(std::ostream& out, bool ignore_subgraph = false)
    const {
  out << "[";
  auto names = attributeNames();
  int i = 0;
  for (auto name : names) {
    if (ignore_subgraph && name == attr::Subgraph) {
      continue;
    }
    if (i++ > 0) {
      out << ", ";
    }
    // TODO: debugging mode to see the qualifier.  We definitely
    // don't want to print the qualifier since it should always
    // be attribute, but you might be able to track down a weird
    // bug by printing it out.
    out << name.toUnqualString() << "=";

    printAttrValue(out, name);
  }
  out << "]";
}

static std::ostream& indent(std::ostream& out, size_t level) {
  for (size_t i = 0; i < level; ++i) {
    out << "  ";
  }
  return out;
}

std::ostream& Node::print(
    std::ostream& out,
    size_t level,
    std::vector<const Node*>* groups) const {
  auto outs = outputs();
  indent(out, level) << const_value_list_with_types(outs);
  out << " = ";
  if (kind() == prim::PythonOp) {
    auto* pyOp = static_cast<const ::torch::jit::PythonOp*>(this);
    out << "^" << pyOp->name();
    pyOp->writeScalars(out);
  } else {
    if (hasAttribute(attr::Subgraph) && groups) {
      out << kind().toQualString() << "_" << groups->size();
      if (numAttributes() > 1 && kind() != prim::DifferentiableGraph) {
        printAttributes(out, /*ignore_subgraph=*/true);
      }
      groups->push_back(this);
    } else {
      out << kind().toQualString();
      if (hasAttributes()) {
        printAttributes(out);
      }
    }
  }

  out << "(" << inputs() << ")";
  std::string scName = scopeName();
  if (scName.empty()) {
    out << "\n";
  } else {
    out << ", ";
    out << "scope: " << scName << "\n";
  }
  for (size_t i = 0; i < blocks().size(); ++i) {
    auto b = blocks()[i];
    indent(out, level + 1) << "block" << i << "("
                           << const_value_list_with_types(b->inputs(), false)
                           << ") {\n";
    for (auto nested : b->nodes()) {
      nested->print(out, level + 2, groups);
    }
    indent(out, level + 2) << "-> (" << b->outputs() << ")\n";
    indent(out, level + 1) << "}\n";
  }
  return out;
}

std::ostream& operator<<(std::ostream& out, const Node& n) {
  return n.print(out, 0, nullptr);
}

std::ostream& operator<<(std::ostream& out, const Graph& g) {
  out << "graph(" << const_value_list_with_types(g.inputs(), true) << ") {\n";
  std::vector<const Node*> groups;
  for (auto n : g.nodes()) {
    n->print(out, 1, &groups);
  }
  out << "  return (" << g.outputs() << ");\n}\n";
  size_t i = 0;
  for (auto fg : groups) {
    out << "with " << fg->kind().toQualString() << "_" << i++ << " = "
        << *fg->g(attr::Subgraph);
  }
  /*
  // Uncomment this to debug all_nodes issues
  {
    out << "\n";
    out << "all_nodes:\n";
    for (auto& n : g.all_nodes) {
      printNode(out, const_cast<Node*>(n), nullptr);
    }
  }
  */
  return out;
}

std::ostream& Graph::prettyPrint(std::ostream& out) {
  std::vector<at::Tensor> tensor_table;
  PythonPrint(out, *this, tensor_table);
  return out;
}

void Graph::dumpPretty() {
  std::vector<at::Tensor> tensor_table;
  PythonPrint(std::cout, *this, tensor_table);
}

static void checkSameDevice(const Node* node) {
  bool has_device = false;
  c10::optional<at::Device> device = c10::nullopt;
  auto checkValue = [&](const Value* v) {
    if (CompleteTensorTypePtr type = v->type()->cast<CompleteTensorType>()) {
      if (!has_device) {
        has_device = true;
        device = type->device();
      } else {
        AT_ASSERT(device == type->device());
      }
    }
  };
  for (auto input : node->inputs()) {
    checkValue(input);
  }
  for (auto output : node->outputs()) {
    checkValue(output);
  }
}

using node_set = std::set<const Node*>;
#define ALL_OF(container) container.begin(), container.end()

// These functions purposely operate on the internal members directly, to force
// you to think about how the invariants change if you change the data
// representation (even if the external API does not change.)

// NB: This assert is written to assume you don't have any unattached
// nodes.  Unattached nodes can occur while manipulations to the
// graph are occurring.
void Node::lint() const {
  // Node invariants
  // - if node should live in list, nodes_iter is consistent
  // - Inputs are all marked as a use by the nodes they refer to
  // - Owning graph is non-null and consistent
  // - The "Select" invariant, when the node is MultiReturn
  //
  // The handle invariant:
  //    If a node takes a handle as an input, it is always the
  //    LAST input of the node.  There is at most one handle input.

  {
    size_t i = 0;
    for (auto input : inputs_) {
      // WARNING: O(n^2)
      // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
      AT_ASSERT(
          std::find(ALL_OF(input->uses_), Use(const_cast<Node*>(this), i)) !=
          input->uses_.end());
      AT_ASSERT(graph_->all_nodes.count(this) == 1);
      i++;
    }
  }

  for (auto o : outputs()) {
    size_t i = 0;
    for (auto use : o->uses()) {
      // Use invariants
      // - Use is consistent with inputs
      // - Every user node is live (checked in Graph)
      AT_ASSERT(use.user->inputs_[use.offset] == o);
      i++;
    }
  }

  // Node subclass invariants
  IR_IF(this, Constant)
  AT_ASSERT(inputs_.size() == 0);
  IR_ELSEIF(Return)
  // Return uses is zero
  AT_ASSERT(outputs().size() == 0);
  IR_ELSEIF(Param)
  // Param inputs is zero
  AT_ASSERT(inputs_.size() == 0);
  IR_ELSEIFM_CONST(PythonOp)
  // Python operator cconv is correct
  size_t n_scalars = 0, n_tensors = 0;
  for (auto c : value->cconv) {
    if (c == 'c') {
      n_scalars++;
    } else if (c == 'd') {
      n_tensors++;
    } else {
      AT_ASSERT(0);
    }
    AT_ASSERT(static_cast<bool>(value->pyobj));
  }
  AT_ASSERT(n_scalars == value->scalar_args.size());
  AT_ASSERT(n_tensors == inputs_.size());
  IR_ELSEIF(Eval)
  // TODO: add invariants
  // TODO: It's not good for these ops to be top-level, it makes cases longer.
  IR_ELSEIF(FusionGroup)
  checkSameDevice(value);
  // TODO: Typecheck the parameters
  value->g(attr::Subgraph)->lint();
  IR_END()
}

// TODO: When lint fails, give better indication about which
// instruction triggered the failure.
void Graph::lint() const {
  // Graph invariants

  // Uncomment the following to see the graph
  // std::cout << *const_cast<Graph*>(this);

  // nodes
  // - nodes_ is a valid topological ordering for inputs
  // - No repeated nodes
  // - Params and return do NOT occur in nodes
  // - next_unique_ is greater than all uniques in graph
  // - uniques in all_nodes are unique
  // - every use will occur later in the topsort

  struct LintScope {
    LintScope() = default;
    LintScope(std::unique_ptr<LintScope> parent) : parent(std::move(parent)) {}
    bool contains(const Value* v) {
      return values.count(v) > 0 || (parent && parent->contains(v));
    }
    bool contains(const Node* n) {
      return nodes.count(n) > 0 || (parent && parent->contains(n));
    }
    void insert(const Value* v) {
      AT_ASSERT(!contains(v));
      values.insert(v);
    }
    void insert(const Node* n) {
      AT_ASSERT(!contains(n));
      nodes.insert(n);
    }
    std::unique_ptr<LintScope> parent;

   private:
    std::unordered_set<const Value*> values;
    std::unordered_set<const Node*> nodes;
  };
  // Struct enables mutual recursion in linting methods.
  // Putting it inside Graph::lint enables access to private Graph members
  struct LintImpl {
    LintImpl(const Graph& g)
        : g(g),
          scope(new LintScope()),
          all_nodes_set(ALL_OF(g.all_nodes)) {} // NB: all_nodes is *unordered*
    const Graph& g;
    std::unique_ptr<LintScope> scope;
    std::unordered_set<size_t> seen_uniques;
    std::unordered_map<const Node*, int64_t> anticipated_uses;
    node_set all_nodes_set;
    node_set sum_set;

    void check_value(const Value* v) {
      scope->insert(v);
      auto b2 = seen_uniques.insert(v->unique());
      AT_ASSERT(b2.second); // insertion took place
      AT_ASSERT(v->unique() < g.next_unique_);

      for (auto use : v->uses()) {
        AT_ASSERT(!scope->contains(use.user));
        AT_ASSERT(g.all_nodes.count(use.user) == 1);
        anticipated_uses[use.user]++; // int default constructs to 0
      }
    }
    void check_node(const Node* n) {
      for (auto input : n->inputs_) {
        if (!scope->contains(input)) {
          AT_ASSERTM(0, input->unique(), " not in scope");
        }
      }
      AT_ASSERT(anticipated_uses[n] == static_cast<int64_t>(n->inputs_.size()));
      anticipated_uses[n] = -1; // we saw the anticipated user!
      scope->insert(n);
      for (auto block : n->blocks()) {
        std::unique_ptr<LintScope> new_scope(new LintScope(std::move(scope)));
        scope = std::move(new_scope);
        check_block(block);
        scope = std::move(scope->parent);
      }
      size_t i = 0;
      for (auto o : n->outputs()) {
        AT_ASSERT(o->node() == n);
        AT_ASSERT(i++ == o->offset_);
        check_value(o);
      }
      n->lint();
    }
    void check_block(const Block* b) {
      // Check topological ordering
      AT_ASSERT(b->param_node()->isBefore(*b->nodes().begin()));
      auto curNode = *b->nodes().begin();
      while (curNode != b->return_node()) {
        AT_ASSERT(curNode->isBefore(curNode->next()));
        curNode = curNode->next();
      }

      for (auto input : b->inputs()) {
        check_value(input);
        AT_ASSERT(input->node()->kind_ == prim::Param);
      }

      for (auto n : b->nodes()) {
        AT_ASSERT(n->kind_ != prim::Param);
        AT_ASSERT(n->kind_ != prim::Return);
        check_node(n);
      }

      AT_ASSERT(b->output_->kind() == prim::Return);
      check_node(b->output_);

      // all_nodes
      // - inputs_, output_ and nodes_ are all included in all_nodes
      // - all_nodes does not contain dead nodes??? (likely to be temporarily
      // suspended).  Weaker: all_nodes contains all inputs and returns
      // - only one return node???

      node_set nodes_set(ALL_OF(b->nodes()));
      node_set inputs_set{b->input_};
      node_set output_set{b->output_};
      // TODO: Make a more type safe std::includes wrapper which disallows use
      // on non-ordered containers
      AT_ASSERT(std::includes(ALL_OF(all_nodes_set), ALL_OF(nodes_set)));
      AT_ASSERT(std::includes(ALL_OF(all_nodes_set), ALL_OF(inputs_set)));
      AT_ASSERT(std::includes(ALL_OF(all_nodes_set), ALL_OF(output_set)));

      sum_set.insert(ALL_OF(nodes_set));
      sum_set.insert(ALL_OF(inputs_set));
      sum_set.insert(ALL_OF(output_set));
    }
    void check_graph() {
      node_set all_nodes_set(
          ALL_OF(g.all_nodes)); // NB: all_nodes is *unordered*

      check_block(g.block_);
      for (auto kv : anticipated_uses) {
        AT_ASSERT(kv.second == -1);
      }
      AT_ASSERT(std::includes(ALL_OF(sum_set), ALL_OF(all_nodes_set)));
    }
  };
  LintImpl(*this).check_graph();
}

void Graph::dump() const {
  std::cout << *this << "\n";
}

void LintGraph(std::shared_ptr<Graph>& graph) {
  graph->lint();
}

Block::Block(Graph* graph_, Node* node_)
    : graph_(graph_),
      output_(initOutput(graph_->create(prim::Return, 0))),
      input_(graph_->create(prim::Param, 0)),
      owning_node_(node_) {
  graph_->all_blocks.emplace(this);
  output_->owning_block_ = this;
  output_->topo_position_ = kUpperBound;
  input_->owning_block_ = this;
  input_->topo_position_ = kLowerBound;
}

void Block::reIndexTopology() {
  auto curPos = kLowerBound;
  for (auto node : nodes()) {
    AT_ASSERT(curPos <= (kUpperBound - kAppendInterval));
    curPos += kAppendInterval;
    node->topo_position_ = curPos;
  }
}

void Block::cloneFrom(Block* src, std::function<Value*(Value*)> value_map) {
  std::unordered_map<Value*, Value*> local_map;
  auto env = [&](Value* v) {
    auto it = local_map.find(v);
    if (it != local_map.end()) {
      return it->second;
    }
    return value_map(v);
  };

  auto graph = owningGraph();
  for (auto input : src->inputs()) {
    local_map[input] = this->addInput()->copyMetadata(input);
  }

  for (auto node : src->nodes()) {
    auto new_node = this->appendNode(graph->createClone(node, env));
    for (size_t i = 0; i < node->outputs().size(); ++i) {
      auto oo = node->outputs()[i];
      auto no = new_node->outputs()[i];
      local_map[oo] = no;
      no->copyMetadata(oo);
    }
  }
  for (auto output : src->outputs()) {
    this->registerOutput(env(output));
  }
}

void Block::destroy() {
  // we cannot destroy the output because it is used as the sentinel
  // for the nodes() list and has to remain valid for the loop
  output_->removeAllInputs();
  for (auto it = this->nodes().reverse().begin(),
            end = this->nodes().reverse().end();
       it != end;
       ++it) {
    it.destroyCurrent();
  }
  output_->destroy();
  input_->destroy();
  graph_->freeBlock(this);
}

std::shared_ptr<Graph> Graph::copy() {
  auto new_g = std::make_shared<Graph>();
  auto env = [](Value* v) -> Value* {
    AT_ERROR(
        "Graph::copy() encountered a use of a value not in scope. Run lint!");
  };
  new_g->block()->cloneFrom(this->block(), env);
  return new_g;
}

bool Value::mustBeNone() const {
  return node_->kind() == prim::None;
}

std::string Value::uniqueNameBase() const {
  std::string name = uniqueName();
  std::string name_base = name;
  auto last_dot_pos = name.find_last_of('.');
  if (last_dot_pos != std::string::npos && last_dot_pos + 1 != name.size()) {
    if (name.find_first_not_of("0123456789", last_dot_pos + 1) ==
        std::string::npos) {
      name_base = name.substr(0, last_dot_pos);
    }
  }
  return name_base;
}

Value* Value::setUniqueName(const std::string& name) {
  if (name.size() > 0 &&
      name.find_first_not_of("0123456789") == std::string::npos) {
    throw std::runtime_error("names may not be integers: " + name);
  }

  auto& names = node()->owningGraph()->unique_names_;

  // clear any old name from the map
  if (hasUniqueName()) {
    names.erase(unique_name_);
    unique_name_ = "";
  }

  // allow "" to clear the uniquename
  if (name == "") {
    return this;
  }

  // if someone else has this name, then rename the other value
  auto old_owner_of_name = names.find(name);
  if (old_owner_of_name != names.end()) {
    size_t suffix = 1;
    std::string name_base = name;
    auto last_dot_pos = name.find_last_of('.');
    if (last_dot_pos != std::string::npos && last_dot_pos + 1 != name.size()) {
      if (name.find_first_not_of("0123456789", last_dot_pos + 1) ==
          std::string::npos) {
        suffix = std::stoll(name.substr(last_dot_pos + 1));
        name_base = name.substr(0, last_dot_pos);
      }
    }
    std::string replacement_name;
    do {
      std::stringstream ss;
      ss << name_base << "." << suffix++;
      replacement_name = ss.str();
    } while (names.count(replacement_name) > 0);
    old_owner_of_name->second->setUniqueName(replacement_name);
  }

  names[name] = this;
  unique_name_ = name;
  return this;
}

Value* Value::copyMetadata(Value* from) {
  setType(from->type());
  if (from->hasUniqueName()) {
    setUniqueName(from->uniqueName());
  }
  return this;
}

void Value::replaceFirstUseWith(Value* newValue) {
  AT_ASSERT(owningGraph() == newValue->owningGraph());
  auto u = uses()[0];
  u.user->inputs_[u.offset] = newValue;
  newValue->uses_.push_back(u);
  uses_.erase(uses_.begin());
}

void Value::replaceAllUsesWith(Value* newValue) {
  while (!uses().empty()) {
    replaceFirstUseWith(newValue);
  }
}

size_t findArgument(const FunctionSchema& the_schema, Symbol name) {
  auto name_str = name.toUnqualString();
  for (size_t i = 0; i < the_schema.arguments().size(); ++i) {
    const Argument* arg = &the_schema.arguments()[i];
    if (arg->name() == name_str) {
      return i;
    }
  }
  throw std::runtime_error(
      std::string("Couldn't find an argument called ") + name.toQualString());
}

c10::optional<IValue> Node::get(Symbol name) const {
  return toIValue(namedInput(name));
}

Value* Node::namedInput(Symbol name) const {
  return input(findArgument(schema(), name));
}

bool Node::matches(
    const char* signature_literal,
    at::ArrayRef<Symbol> const_inputs) const {
  if (!sig(signature_literal).matches(this)) {
    return false;
  }
  for (Symbol s : const_inputs) {
    if (!is_constant(s)) {
      return false;
    }
  }
  return true;
}

void Node::dump() const {
  std::cout << *this << "\n";
}

void Node::findSchema() const {
  schema_ = &getOperatorFor(this).schema();
}

const FunctionSchema* Node::maybeSchema() const {
  if (!schema_) {
    if (auto op = findOperatorFor(this)) {
      schema_ = &op->schema();
    }
  }
  return schema_;
}

bool Node::isNondeterministic() const {
  static const OperatorSet nondeterministic_ops = {
      "aten::dropout(Tensor input, float p, bool train) -> Tensor",
      "aten::_fused_dropout(Tensor self, float p, Generator? generator) -> (Tensor, Tensor)",
      "aten::_standard_gamma(Tensor self, Generator? generator) -> Tensor",
      "aten::bernoulli(Tensor self, *, Generator? generator) -> Tensor",
      "aten::bernoulli(Tensor self, float p, *, Generator? generator) -> Tensor",
      "aten::multinomial(Tensor self, int num_samples, bool replacement, *, Generator? generator) -> Tensor",
      "aten::normal(Tensor mean, Tensor std, *, Generator? generator) -> Tensor",
      "aten::normal(float mean, Tensor std, *, Generator? generator) -> Tensor",
      "aten::normal(Tensor mean, float std, *, Generator? generator) -> Tensor",
      "aten::poisson(Tensor self, Generator? generator) -> Tensor",
      "aten::rrelu(Tensor self, Scalar lower, Scalar upper, bool training, Generator? generator) -> Tensor",
      "aten::rrelu_with_noise(Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, Generator? generator) -> Tensor",
      "aten::rand(int[] size, *, int dtype, int layout, Device device) -> Tensor",
      "aten::rand_like(Tensor self) -> Tensor",
      "aten::rand_like(Tensor self, *, int dtype, int layout, Device device) -> Tensor",
      "aten::randint(int high, int[] size, *, int dtype, int layout, Device device) -> Tensor",
      "aten::randint(int low, int high, int[] size, *, int dtype, int layout, Device device) -> Tensor",
      "aten::randint_like(Tensor self, int high) -> Tensor",
      "aten::randint_like(Tensor self, int low, int high) -> Tensor",
      "aten::randint_like(Tensor self, int high, *, int dtype, int layout, Device device) -> Tensor",
      "aten::randint_like(Tensor self, int low, int high, *, int dtype, int layout, Device device) -> Tensor",
      "aten::randn(int[] size, *, int dtype, int layout, Device device) -> Tensor",
      "aten::randn_like(Tensor self) -> Tensor",
      "aten::randn_like(Tensor self, *, int dtype, int layout, Device device) -> Tensor",
      "aten::randperm(int n, *, int dtype, int layout, Device device) -> Tensor"};

  if (nondeterministic_ops.find(this) == nullptr) {
    return false;
  }
  // Dropout with train = False is deterministic
  if (matches("aten::dropout(Tensor input, float p, bool train) -> Tensor") &&
      is_constant(attr::train) && !get<bool>(attr::train).value()) {
    return false;
  }
  return true;
}

bool Node::hasSideEffects() const {
  switch (kind_) {
    case prim::PythonOp:
    case prim::Print:
    case prim::RaiseException:
    case aten::warn:
      return true;
  }
  return false;
}

// Assign this node a topological position, to facilitate fast isBefore() and
// isAfter() queries. Must be called right after a node is inserted into the
// node list.
//
// The basic scheme is: assign every node a position (uint64_t).  The common
// case (appending to the end of the graph) is made more efficient by advancing
// a fixed interval past the previous node and placing `this` there. Otherwise,
// assign `this` a position at the midpoint between its prev() and next()
// nodes.
//
// If we ever run out of space (by, e.g. inserting too much in place), we
// reindex by spreading out all the nodes again.
void Node::assignTopoPosition() {
  auto returnNode = owningBlock()->return_node();
  const auto prevPos = prev()->topo_position_;
  const auto nextPos = next()->topo_position_;

  // Append to the end of the graph
  if (next() == returnNode) {
    if (next() == prev()) {
      // the node list is empty, assign the first position
      topo_position_ = kMidPoint;
      return;
    }

    if (prevPos >= (kUpperBound - kAppendInterval)) {
      // we're running off the edge
      owningBlock()->reIndexTopology();
      return;
    }

    topo_position_ = prevPos + kAppendInterval;

    // Prepend to the graph
  } else if (prev() == returnNode) {
    // next() is the first element in the block list
    if (nextPos <= (kLowerBound + kAppendInterval)) {
      // we're running off the edge
      owningBlock()->reIndexTopology();
      return;
    }

    topo_position_ = nextPos - kAppendInterval;

    // insert between two existing nodes
  } else {
    const auto posBetween = prevPos + (nextPos - prevPos) / 2;
    if (posBetween == prevPos) {
      // There was no room
      owningBlock()->reIndexTopology();
      return;
    }
    topo_position_ = posBetween;
  }
}

Node::Node(Graph* graph_, NodeKind kind_)
    : kind_(kind_),
      graph_(graph_),
      owning_block_(nullptr),
      scope_(graph_->current_scope_),
      schema_(nullptr),
      topo_position_(0) {
  graph_->all_nodes.emplace(this);
}

void Node::eraseOutput(size_t i) {
  AT_ASSERT(i < outputs_.size());
  AT_ASSERT(outputs_[i]->uses().empty());
  schema_ = nullptr;
  Value* n = outputs_[i];
  outputs_.erase(outputs_.begin() + i);
  owningGraph()->freeValue(n);
  for (size_t j = i; j < outputs_.size(); j++) {
    outputs_[j]->offset_--;
  }
}

Block* Node::addBlock() {
  schema_ = nullptr;
  blocks_.push_back(new Block(owningGraph(), this));
  return blocks_.back();
}

void Node::eraseBlock(size_t i) {
  AT_ASSERT(i < blocks_.size());
  schema_ = nullptr;
  Block* n = blocks_[i];
  blocks_.erase(blocks_.begin() + i);
  n->destroy();
}

void Node::destroy() {
  while (!outputs().empty()) {
    eraseOutput(outputs().size() - 1);
  }
  while (!blocks().empty()) {
    eraseBlock(blocks().size() - 1);
  }
  removeAllInputs();
  if (inBlockList()) {
    removeFromList();
  }
  graph_->freeNode(this);
}

void Node::cloneFrom(Node* s) {
  setSourceLocation(s->getSourceLocation());
  if (s->scope_ && !s->scope_->isBlank()) {
    scope_ = s->scope_;
  }
  copyAttributes(*s);
}

void Node::replaceAllUsesWith(Node* n) {
  AT_ASSERT(outputs().size() == n->outputs().size());
  size_t nOutputs = outputs().size();
  for (size_t i = 0; i < nOutputs; i++) {
    outputs()[i]->replaceAllUsesWith(n->outputs()[i]);
  }
}

Value* Node::insertInput(size_t i, Value* value) {
  AT_ASSERT(graph_ == value->owningGraph());
  schema_ = nullptr;
  // First we update the offsets for all existing inputs that will reside
  // after the one we're inserting. Concretely, these are the inputs at
  // indices [i, # input). Since we're inserting one input before all of
  // these inputs, increment their use offsets for this value by 1
  for (size_t use_itr = i; use_itr < inputs_.size(); ++use_itr) {
    // See Note [User node does not uniquely identify use]
    auto use = findUseForInput(use_itr);
    use->offset += 1;
  }
  // Insert the actual input at the specified index
  inputs_.insert(inputs_.begin() + i, value);
  // Register the new use of the value we're inserted as an input.
  value->uses_.emplace_back(this, i);
  return value;
}

Value* Node::addInput(Value* value) {
  AT_ASSERT(graph_ == value->owningGraph());
  schema_ = nullptr;
  value->uses_.emplace_back(this, inputs_.size());
  inputs_.push_back(value);
  return value;
}

Value* Node::replaceInput(size_t i, Value* newValue) {
  AT_ASSERT(newValue->owningGraph() == graph_);
  schema_ = nullptr;
  Value* old = dropInput(i);
  inputs_[i] = newValue;
  newValue->uses_.emplace_back(this, i);
  return old;
}

void Node::replaceInputWith(Value* from, Value* to) {
  AT_ASSERT(from->owningGraph() == graph_);
  AT_ASSERT(to->owningGraph() == graph_);
  schema_ = nullptr;
  size_t i = 0;
  for (auto input : inputs()) {
    if (input == from) {
      replaceInput(i, to);
    }
    i++;
  }
}

Value* Node::addOutput() {
  outputs_.push_back(new Value(this, outputs_.size()));
  schema_ = nullptr;
  return outputs_.back();
}

Value* Node::insertOutput(size_t i) {
  schema_ = nullptr;
  outputs_.insert(outputs_.begin() + i, new Value(this, i));
  for (size_t itr = i + 1; itr < outputs_.size(); ++itr) {
    outputs_[itr]->setOffset(outputs_[itr]->offset() + 1);
  }
  return outputs_.at(i);
}

bool Node::isBeforeOrAfter(const Node* n, MoveSide moveSide) const {
  if (this->owningBlock() == n->owningBlock()) {
    if (moveSide == MoveSide::BEFORE) {
      return this->topo_position_ < n->topo_position_;
    }

    if (moveSide == MoveSide::AFTER) {
      return this->topo_position_ > n->topo_position_;
    }

    AT_ASSERT(this == n);
    return false;
  }

  // These nodes don't share a common block. Traverse the blockchains upward
  // until we find the first common block.
  auto lhs = this;
  while (lhs) {
    AT_ASSERT(lhs->owningBlock());

    auto rhs = n;
    while (rhs) {
      if (!rhs->owningBlock()) {
        break;
      }

      if (lhs->owningBlock() == rhs->owningBlock()) {
        return lhs->isBeforeOrAfter(rhs, moveSide);
      }
      rhs = rhs->owningBlock()->owningNode();
    }

    lhs = lhs->owningBlock()->owningNode();
  }
  // should never reach here, since both nodes are ultimately in the same graph
  AT_ASSERT(false);
}

bool Node::isBefore(const Node* n) const {
  return isBeforeOrAfter(n, MoveSide::BEFORE);
}

bool Node::isAfter(const Node* n) const {
  return isBeforeOrAfter(n, MoveSide::AFTER);
}

Node* Node::insertBefore(Node* n) {
  AT_ASSERT(n->inBlockList());
  insertAfter(n->prev());
  return this;
}

Node* Node::insertAfter(Node* n) {
  AT_ASSERT(!inBlockList() && n->inBlockList());
  AT_ASSERT(n->owningBlock());
  this->owning_block_ = n->owningBlock();
  Node* next = n->next();
  n->next() = this;
  this->prev() = n;
  this->next() = next;
  next->prev() = this;
  assignTopoPosition();
  return this;
}

void Node::moveAfter(Node* n) {
  removeFromList();
  insertAfter(n);
}

void Node::moveBefore(Node* n) {
  removeFromList();
  insertBefore(n);
}

void Node::removeInput(size_t i) {
  schema_ = nullptr;
  dropInput(i);
  // everything after this input shifts left,
  // so we need to update their use offsets to match
  for (size_t j = i + 1; j < inputs_.size(); j++) {
    auto it = findUseForInput(j);
    it->offset--;
  }
  inputs_.erase(inputs_.begin() + i);
}

void Node::removeAllInputs() {
  schema_ = nullptr;
  for (size_t i = 0; i < inputs().size(); ++i) {
    dropInput(i);
  }
  inputs_.clear();
}

use_list::iterator Node::findUseForInput(size_t i) {
  auto& input_uses = inputs_[i]->uses_;
  // O(N) on the use list, but unless we get nodes with +100 uses
  // vector traversal still is probably faster than linked list
  auto use_it = std::find(input_uses.begin(), input_uses.end(), Use(this, i));
  AT_ASSERT(use_it != input_uses.end());
  return use_it;
}

Value* Node::dropInput(size_t i) {
  AT_ASSERT(i < inputs_.size());
  auto input_node = inputs_[i];
  auto use_it = findUseForInput(i);
  input_node->uses_.erase(use_it);
  inputs_[i] = nullptr;
  return input_node;
}

void Node::removeFromList() {
  AT_ASSERT(inBlockList());
  this->owning_block_ = nullptr;
  Node* next = this->next();
  Node* prev = this->prev();
  prev->next() = next;
  next->prev() = prev;
  this->next() = nullptr;
  this->prev() = nullptr;
}

inline const SourceRange& fakeRange() {
  static SourceRange range(
      std::make_shared<std::string>("<internally-created-node>"), 0, 1);
  return range;
}

Value* Graph::insert(
    Symbol opname,
    at::ArrayRef<NamedValue> args,
    at::ArrayRef<NamedValue> kwargs,
    const c10::optional<SourceRange>& range) {
  return script::emitBuiltinCall(
      range.value_or(fakeRange()),
      *this,
      opname,
      c10::nullopt,
      args,
      kwargs,
      /*required=*/true);
}

Node* Graph::create(NodeKind kind, size_t num_outputs) {
  // NB: Node constructor adds node to all_nodes
  auto n = new Node(this, kind);
  for (size_t i = 0; i < num_outputs; i++) {
    n->addOutput();
  }
  return n;
}

Node* Graph::create(
    NodeKind kind,
    ArrayRef<Value*> inputs,
    size_t num_outputs) {
  auto n = create(kind, num_outputs);
  for (auto i : inputs) {
    n->addInput(i);
  }
  return n;
}

Node* Graph::createUndefined() {
  return create(prim::Undefined);
}

Node* Graph::createNone(TypePtr typ) {
  Node* n = create(prim::None);
  n->output()->setType(OptionalType::create(std::move(typ)));
  return n;
}

Node* Graph::createFusionGroup() {
  auto n = create(prim::FusionGroup, 0);
  n->g_(attr::Subgraph, std::make_shared<Graph>(current_scope()));
  return n;
}

Node* Graph::createTuple(at::ArrayRef<Value*> values) {
  auto types = fmap(values, [](Value* v) { return v->type(); });
  auto tt = TupleType::create(std::move(types));
  auto n = create(prim::TupleConstruct, values);
  n->output()->setType(tt);
  return n;
}

Node* Graph::createTupleUnpack(Value* v) {
  TupleTypePtr tt = v->type()->expect<TupleType>();
  auto n = create(prim::TupleUnpack, {v}, 0);
  for (auto& element : tt->elements()) {
    n->addOutput()->setType(element);
  }
  return n;
}

Node* Graph::createTupleIndex(Value* tup, int64_t index) {
  auto n = create(prim::TupleIndex, {tup});
  n->i_(attr::index, index);
  auto tuple_type = tup->type()->expect<TupleType>();
  n->output()->setType(tuple_type->elements().at(index));
  return n;
}

Node* Graph::createTupleSlice(Value* tup, int64_t beg, int64_t end) {
  auto n = create(prim::TupleSlice, {tup});
  auto tuple_type = tup->type()->expect<TupleType>();
  n->i_(attr::beg, beg);
  n->i_(attr::end, end);
  std::vector<TypePtr> output_types;
  for (auto i = beg; i < end; ++i) {
    output_types.push_back(tuple_type->elements().at(i));
  }
  auto tt = TupleType::create(std::move(output_types));
  n->output()->setType(tt);
  return n;
}

Node* Graph::createList(const TypePtr& elem_type, at::ArrayRef<Value*> values) {
  auto n = create(prim::ListConstruct, values);
  for (const auto& v : values) {
    AT_ASSERT(v->type()->isSubtypeOf(elem_type));
  }
  n->output()->setType(ListType::create(elem_type));
  return n;
}
Node* Graph::createListUnpack(Value* v, size_t size) {
  ListTypePtr list_type = v->type()->expect<ListType>();
  TypePtr elem_type = list_type->getElementType();
  auto n = create(prim::ListUnpack, {v}, 0);
  for (size_t i = 0; i < size; ++i) {
    n->addOutput()->setType(elem_type);
  }
  return n;
}

Node* Graph::createNumToTensor(Value* value) {
  auto typ = value->type();
  Node* result = create(prim::NumToTensor, {value});
  result->output()->setType(CompleteTensorType::fromNumberType(std::move(typ)));
  return result;
}

Node* Graph::createImplicitTensorToNum(const TypePtr& type, Value* value) {
  auto* result = create(prim::ImplicitTensorToNum, {value});
  result->output()->setType(type);
  return result;
}

Node* Graph::createClone(
    Node* n,
    const std::function<Value*(Value*)>& value_map,
    bool copy_blocks) {
  // n can be from a different graph
  Node* r = n->allocNewInstance(this);
  for (auto o : n->outputs()) {
    r->addOutput()->copyMetadata(o);
  }
  r->cloneFrom(n);
  for (auto i : n->inputs()) {
    r->addInput(value_map(i));
  }
  if (copy_blocks) {
    for (auto b : n->blocks()) {
      r->addBlock()->cloneFrom(b, value_map);
    }
  }
  return r;
}

Value* Graph::insertConstant(
    IValue val,
    c10::optional<SourceRange> loc,
    c10::optional<ScopePtr> scope) {
  return jit::insertConstant(
      *this, std::move(val), std::move(loc), std::move(scope));
}

std::string Graph::toString() const {
  std::ostringstream oss;
  oss << *this;
  return oss.str();
}

Graph::~Graph() {
  for (const Node* n : all_nodes) {
    delete n;
  }
  for (const Value* v : all_values) {
    delete v;
  }
  for (const Block* b : all_blocks) {
    delete b;
  }
}

void Graph::freeNode(Node* n) {
  auto it = all_nodes.find(n);
  AT_ASSERT(it != all_nodes.end());
  delete *it;
  all_nodes.erase(it);
}
void Graph::freeValue(Value* v) {
  v->setUniqueName("");
  auto it = all_values.find(v);
  AT_ASSERT(it != all_values.end());
  delete *it;
  all_values.erase(it);
}
void Graph::freeBlock(Block* b) {
  auto it = all_blocks.find(b);
  AT_ASSERT(it != all_blocks.end());
  delete *it;
  all_blocks.erase(it);
}

at::ArrayRef<Value*> createTupleUnpack(Value* v) {
  // small peephole optimization to ensure IntList attributes can still turn
  // into constants e.g. in x.expand([3, 4])
  if (v->node()->kind() == prim::TupleConstruct) {
    return v->node()->inputs();
  }
  auto& g = *v->owningGraph();
  return g.insertNode(g.createTupleUnpack(v))->outputs();
}

std::vector<Value*> inlineCallTo(
    Graph& g,
    Graph& callee,
    ArrayRef<Value*> inputs,
    bool unpack_outputs) {
  std::unordered_map<Value*, Value*> value_map;
  auto value_map_func = [&](Value* v) { return value_map.at(v); };
  AT_ASSERT(callee.inputs().size() == inputs.size());
  for (size_t i = 0; i < inputs.size(); ++i) {
    value_map[callee.inputs()[i]] = inputs[i];
  }
  for (auto* node : callee.nodes()) {
    auto* new_node = g.insertNode(g.createClone(node, value_map_func));
    for (size_t i = 0; i < node->outputs().size(); ++i) {
      value_map[node->outputs()[i]] = new_node->outputs()[i];
    }
  }

  std::vector<Value*> outputs;
  for (auto* output : callee.outputs()) {
    outputs.push_back(value_map_func(output));
  }

  if (unpack_outputs && outputs.size() == 1 &&
      callee.outputs().at(0)->type()->kind() == TupleType::Kind) {
    auto tup = outputs[0];
    outputs.clear();
    for (Value* v : createTupleUnpack(tup)) {
      outputs.emplace_back(v);
    }
    // if this was a peephole tuple unpack we can just get rid of
    // the tuple construct here and prevent needing DCE
    if (tup->node()->kind() == prim::TupleConstruct &&
        !tup->node()->hasUses()) {
      tup->node()->destroy();
    }
  }

  return outputs;
}

PythonOp* defaultAllocPythonOp(Graph* g) {
  throw std::runtime_error(
      "Trying to allocate a Python object without python bindings loaded");
}
std::atomic<decltype(&defaultAllocPythonOp)> alloc_python_op;

// patched in when python bindings are loaded
PythonOp* allocPythonOp(Graph* g) {
  return alloc_python_op.load()(g);
}
void setAllocPythonOp(PythonOp* (*v)(Graph* g)) {
  alloc_python_op.store(v);
}

} // namespace jit
} // namespace torch
