// Copyright (c) ONNX Project Contributors
//
// SPDX-License-Identifier: Apache-2.0

#include "onnx/checker.h"

#include <fstream>
#include <functional>
#include <iterator>
#include <set>
#include <string>
#include <unordered_set>
#include <vector>

#include "onnx/common/file_utils.h"
#include "onnx/defs/schema.h"
#include "onnx/defs/tensor_proto_util.h"
#include "onnx/proto_utils.h"
#include "onnx/shape_inference/implementation.h"
#include "onnx/string_utils.h"

#ifdef _WIN32
#include <direct.h>

#include <filesystem>

#else // POSIX
#include <sys/stat.h>
#endif

namespace ONNX_NAMESPACE {
namespace checker {

#define enforce_has_field(proto, field)                                              \
  do {                                                                               \
    if (!proto.has_##field()) {                                                      \
      fail_check("Field '", #field, "' of '", #proto, "' is required but missing."); \
    }                                                                                \
  } while (0)

#define enforce_non_empty_field(proto, field)                                            \
  do {                                                                                   \
    if (proto.field().empty()) {                                                         \
      fail_check("Field '", #field, "' of '", #proto, "' is required to be non-empty."); \
    }                                                                                    \
  } while (0)

void check_value_info(const ValueInfoProto& value_info, const CheckerContext& ctx) {
  enforce_non_empty_field(value_info, name);
  // Relax constraint for subgraph input/output.
  if (!ctx.is_main_graph())
    return;
  enforce_has_field(value_info, type);
  const auto value_case = value_info.type().value_case();
  switch (value_case) {
    case TypeProto::kTensorType: {
      const auto& type = value_info.type().tensor_type();
      enforce_has_field(type, elem_type);
      enforce_has_field(type, shape);
    } break;
    case TypeProto::kOptionalType: {
      const auto& type = value_info.type().optional_type();
      enforce_has_field(type, elem_type);
    } break;
    case TypeProto::kSequenceType: {
      const auto& type = value_info.type().sequence_type();
      enforce_has_field(type, elem_type);
    } break;
    case TypeProto::kMapType: {
      const auto& type = value_info.type().map_type();
      enforce_has_field(type, key_type);
      enforce_has_field(type, value_type);
    } break;
#ifdef ONNX_ML
    case TypeProto::kOpaqueType:
      break;
#endif
    case TypeProto::kSparseTensorType: {
      const auto& type = value_info.type().sparse_tensor_type();
      enforce_has_field(type, elem_type);
      enforce_has_field(type, shape);
    } break;

    default:
      fail_check("Unrecognized type value case (value_info name: ", value_info.name(), "): ", value_case);
  }
}

void check_tensor(const TensorProto& tensor, const CheckerContext& ctx) {
  enforce_has_field(tensor, data_type);
  if (tensor.data_type() == TensorProto::UNDEFINED) {
    fail_check("setting data_type field (tensor name: ", tensor.name(), ") to UNDEFINED is not allowed");
  }

  int num_value_fields = 0;

  const char* value_field = nullptr;

#define check_data_field(field)             \
  bool has_##field = tensor.field().size(); \
  if (has_##field) {                        \
    ++num_value_fields;                     \
    value_field = #field;                   \
  }

  check_data_field(float_data);
  check_data_field(int32_data);
  check_data_field(string_data);
  check_data_field(int64_data);
  check_data_field(raw_data);
  check_data_field(double_data);
  check_data_field(uint64_data);

#undef check_data_field

  bool stored_externally = tensor.has_data_location() && tensor.data_location() == TensorProto::EXTERNAL;
  if (stored_externally) {
    if (num_value_fields != 0) {
      fail_check(
          "Data of TensorProto ( tensor name: ",
          tensor.name(),
          ") is stored externally and should not have data field.",
          value_field);
    }

    bool has_location = false;
    for (const StringStringEntryProto& entry : tensor.external_data()) {
      if (entry.has_key() && entry.has_value() && entry.key() == "location") {
        has_location = true;
        resolve_external_data_location(ctx.get_model_dir(), entry.value(), tensor.name());
      }
    }
    if (!has_location) {
      fail_check("TensorProto ( tensor name: ", tensor.name(), ") is stored externally but doesn't have a location.");
    }
    return;
  }
  int64_t nelem = 1;
  for (auto x : tensor.dims()) {
    nelem *= x;
  }
  if (nelem == 0 && num_value_fields != 0) {
    fail_check("TensorProto (tensor name: ", tensor.name(), ") is 0-element but contains data!");
  }
  if (nelem != 0 && num_value_fields != 1) {
    fail_check("TensorProto (tensor name: ", tensor.name(), ") should contain one and only one value field.");
  }
  if (has_raw_data) {
    if (tensor.data_type() == TensorProto::STRING) {
      fail_check("STRING data (tensor name: ", tensor.name(), ") should not be stored in raw_data field");
    }
    return;
  } else {
#define check_field(field)               \
  if (nelem != 0 && !has_##field) {      \
    fail_check(                          \
        "values of data_type '",         \
        tensor.data_type(),              \
        "' should be stored in field '", \
        #field,                          \
        "' instead of '",                \
        value_field,                     \
        "'");                            \
  }

    switch (tensor.data_type()) {
      case TensorProto::FLOAT:
      case TensorProto::COMPLEX64:
        check_field(float_data);
        break;

      case TensorProto::DOUBLE:
      case TensorProto::COMPLEX128:
        check_field(double_data);
        break;

      case TensorProto::INT32:
      case TensorProto::UINT8:
      case TensorProto::INT8:
      case TensorProto::UINT16:
      case TensorProto::INT16:
      case TensorProto::BOOL:
      case TensorProto::FLOAT16:
      case TensorProto::BFLOAT16:
      case TensorProto::FLOAT8E4M3FN:
      case TensorProto::FLOAT8E4M3FNUZ:
      case TensorProto::FLOAT8E5M2:
      case TensorProto::FLOAT8E5M2FNUZ:
      case TensorProto::UINT4:
      case TensorProto::INT4:
        check_field(int32_data);
        break;

      case TensorProto::INT64:
        check_field(int64_data);
        break;

      case TensorProto::UINT32:
      case TensorProto::UINT64:
        check_field(uint64_data);
        break;

      case TensorProto::STRING:
        check_field(string_data);
        break;

      default:
        fail_check("Unrecognized data_type (tensor name: ", tensor.name(), "): ", tensor.data_type());
    }
  }

#undef check_field
}

void check_sequence(const SequenceProto& sequence, const CheckerContext& ctx) {
  enforce_has_field(sequence, elem_type);
  if (sequence.elem_type() == SequenceProto::TENSOR) {
    for (const TensorProto& tensor : sequence.tensor_values()) {
      check_tensor(tensor, ctx);
    }
  } else if (sequence.elem_type() == SequenceProto::SPARSE_TENSOR) {
    for (const SparseTensorProto& sparse_tensor : sequence.sparse_tensor_values()) {
      check_sparse_tensor(sparse_tensor, ctx);
    }
  } else if (sequence.elem_type() == SequenceProto::SEQUENCE) {
    for (const SequenceProto& seq : sequence.sequence_values()) {
      check_sequence(seq, ctx);
    }
  } else if (sequence.elem_type() == SequenceProto::MAP) {
    for (const MapProto& map : sequence.map_values()) {
      check_map(map, ctx);
    }
  } else {
    fail_check(
        "Sequence ( Structure name: ",
        sequence.name(),
        ", elem_type: ",
        sequence.elem_type(),
        ") is not have a valid element type.");
  }
}

void check_optional(const OptionalProto& optional, const CheckerContext& ctx) {
  enforce_has_field(optional, elem_type);
  if (optional.elem_type() == OptionalProto::UNDEFINED) {
    return;
  } else if (optional.elem_type() == OptionalProto::TENSOR) {
    if (optional.has_tensor_value())
      check_tensor(optional.tensor_value(), ctx);
  } else if (optional.elem_type() == OptionalProto::SPARSE_TENSOR) {
    if (optional.has_sparse_tensor_value())
      check_sparse_tensor(optional.sparse_tensor_value(), ctx);
  } else if (optional.elem_type() == OptionalProto::SEQUENCE) {
    if (optional.has_sequence_value())
      check_sequence(optional.sequence_value(), ctx);
  } else if (optional.elem_type() == OptionalProto::MAP) {
    if (optional.has_map_value())
      check_map(optional.map_value(), ctx);
  } else {
    fail_check(
        "Optional ( Structure name: ",
        optional.name(),
        ", elem_type: ",
        optional.elem_type(),
        ") is not have a valid element type.");
  }
}

void check_map(const MapProto& map, const CheckerContext& ctx) {
  enforce_has_field(map, key_type);
  if (map.key_type() == TensorProto::UNDEFINED) {
    fail_check("setting key_type field (map name: ", map.name(), ") to UNDEFINED is not allowed");
  }
  // Check if key is a valid type, specifically INT8, INT16, INT32, INT64,
  // UINT8, UINT16, UINT32, UINT64, or STRING.
  if ((map.key_type() == TensorProto::FLOAT) || (map.key_type() == TensorProto::BOOL) ||
      (map.key_type() == TensorProto::FLOAT16) || (map.key_type() == TensorProto::COMPLEX64) ||
      (map.key_type() == TensorProto::COMPLEX128)) {
    fail_check(
        "setting key_type field (map name: ",
        map.name(),
        ") to invalid TensorProto key_type ",
        map.key_type(),
        " is not allowed");
  }

  // MapProto will use either keys or string_keys, so only one should be > 0.
  if ((map.keys_size() > 0) && (map.string_keys_size() > 0)) {
    fail_check("Map (name: ", map.name(), ") should not contain more than one keys field.");
  }

  int num_keys = map.keys_size() + map.string_keys_size();
  int num_values = 0;

  enforce_has_field(map, values);
  check_sequence(map.values(), ctx);

  if (map.values().elem_type() == SequenceProto::TENSOR) {
    num_values = map.values().tensor_values_size();
  } else if (map.values().elem_type() == SequenceProto::SPARSE_TENSOR) {
    num_values = map.values().sparse_tensor_values_size();
  } else if (map.values().elem_type() == SequenceProto::SEQUENCE) {
    num_values = map.values().sequence_values_size();
  } else if (map.values().elem_type() == SequenceProto::MAP) {
    num_values = map.values().map_values_size();
  }

  if (num_keys != num_values) {
    fail_check("Length of map keys and map values are not the same (map name: ", map.name(), ")");
  }
}

// Check that the index data stored in a SparseTensorProto is valid.
// indices: a 1-dimensional tensor; indices[i] represents the
// linearized index value for the i-th nonzero value.
void check_sparse_tensor_indices_1(
    const TensorProto& indices,
    const SparseTensorProto& sparse_tensor_proto,
    size_t nnz) {
  int dense_rank = sparse_tensor_proto.dims_size();
  int64_t dense_size = 1;
  for (int i = 0; i < dense_rank; ++i)
    dense_size *= sparse_tensor_proto.dims(i);
  if (static_cast<size_t>(indices.dims(0)) != nnz) {
    fail_check("Sparse tensor indices (", indices.name(), ") has ", indices.dims(0), " values, but NNZ is ", nnz);
  }

  // Check if indices appear in ascending order, and if they have valid
  // values. The i-th value in index_data is the linear index of the i-th
  // non-zero value.
  const std::vector<int64_t> index_data = ParseData<int64_t>(&indices);

  int64_t prev_index = -1;
  for (size_t i = 0; i < nnz; ++i) {
    int64_t curr_index = index_data[i]; // linearized index of i-th value
    if (curr_index < 0 || curr_index >= dense_size) {
      fail_check(
          "Sparse tensor (",
          indices.name(),
          ") index value at position [",
          i,
          "] out of range [0, ",
          dense_size - 1,
          "]");
    }
    if (curr_index <= prev_index) {
      fail_check("Sparse tensor (", indices.name(), ") index value at position [", i, "] not in sorted order.");
    }
    prev_index = curr_index;
  }
}

// Check that the index data stored in a SparseTensorProto is valid.
// indices: a 2-dimensional tensor; indices[i,j] represents the j-th
// index value for the i-th nonzero value.
void check_sparse_tensor_indices_2(
    const TensorProto& indices,
    const SparseTensorProto& sparse_tensor_proto,
    size_t nnz) {
  int dense_rank = sparse_tensor_proto.dims_size();
  if (static_cast<size_t>(indices.dims(0)) != nnz) {
    fail_check("Sparse tensor indices (", indices.name(), ") first dimension size does not equal NNZ.");
  }
  if (indices.dims(1) != dense_rank) {
    fail_check("Sparse tensor indices (", indices.name(), ") second dimension size does not match rank of tensor.");
  }

  // Check if indices appear in ascending order, and if they have valid
  // values.
  const std::vector<int64_t> index_data = ParseData<int64_t>(&indices);
  int64_t prev_index = -1;
  for (size_t i = 0; i < nnz; ++i) {
    int64_t curr_index = 0; // linearized index of i-th value
    for (int j = 0; j < dense_rank; ++j) {
      auto index_ij = index_data[i * dense_rank + j];
      if ((index_ij < 0) || (index_ij >= sparse_tensor_proto.dims(j))) {
        fail_check("Sparse tensor (", indices.name(), ") index value at position [", i, ",", j, "] out of range.");
      }
      curr_index = curr_index * sparse_tensor_proto.dims(j) + index_ij;
    }
    if (curr_index <= prev_index) {
      fail_check(
          "Sparse tensor (", indices.name(), ") index value at position [", i, "] not in lexicographic sorted order.");
    }
    prev_index = curr_index;
  }
}

void check_sparse_tensor(const SparseTensorProto& sparse_tensor_proto, const CheckerContext& ctx) {
  enforce_has_field(sparse_tensor_proto, values);

  const TensorProto& values = sparse_tensor_proto.values();
  check_tensor(values, ctx);

  // values must be a tensor of shape [NNZ]
  // Currently we restrict the value associated with a particular index-tuple
  // to be a single value. In the future, if there is a requirement,
  // we may extend this to permit the value to be a "sub-tensor", in which
  // case values will have dimension > 1.
  if (values.dims_size() != 1) {
    fail_check("Sparse tensor values (", values.name(), ") must have rank 1.");
  }
  size_t nnz = static_cast<size_t>(values.dims(0));
  int dense_rank = sparse_tensor_proto.dims_size();
  if (dense_rank == 0) {
    fail_check("Sparse tensor (", values.name(), ") must have a dense-rank > 0");
  }
  for (int i = 0; i < dense_rank; ++i) {
    if (sparse_tensor_proto.dims(i) <= 0) {
      fail_check("Sparse tensor (", values.name(), ") dimensions are not positive.");
    }
  }

  if (sparse_tensor_proto.has_indices()) {
    const TensorProto& indices = sparse_tensor_proto.indices();
    check_tensor(indices, ctx);
    if (indices.data_type() != TensorProto::INT64) {
      fail_check("Sparse tensor indices (", indices.name(), ") must have INT64 type.");
    }
    switch (indices.dims().size()) {
      case 1:
        // Indices in linearized format
        check_sparse_tensor_indices_1(indices, sparse_tensor_proto, nnz);
        return;
      case 2:
        // Check COO-style index. E.g., an index for a 3D tensor is a 3-tuple.
        check_sparse_tensor_indices_2(indices, sparse_tensor_proto, nnz);
        return;
      default:
        fail_check("Sparse tensor indices (", indices.name(), ") must have rank 1 or 2.");
    }
  } else if (nnz != 0) {
    fail_check("Sparse tensor (", values.name(), ") has no index values.");
  }
}

// NB: This is a generic "attribute well-formedness" check, it doesn't
// actually test if an attribute is valid per a schema
void check_attribute(const AttributeProto& attr, const CheckerContext& ctx, const LexicalScopeContext& lex_ctx) {
  enforce_non_empty_field(attr, name);

  if (ctx.get_ir_version() >= 0x00000002) {
    enforce_has_field(attr, type);
  }

  int used_fields = 0;

#define check_type(expected_type)                                                     \
  if (attr.has_type() && attr.type() != expected_type) {                              \
    fail_check("type field and data field mismatch in attribute ", attr.name(), "."); \
  }

#define check_singular_field(field, type) \
  if (attr.has_##field()) {               \
    ++used_fields;                        \
    check_type(type);                     \
  }

#define check_repeated_field(field, type) \
  if (attr.field##_size() > 0) {          \
    ++used_fields;                        \
    check_type(type);                     \
  }

  check_singular_field(f, AttributeProto::FLOAT);
  check_singular_field(i, AttributeProto::INT);
  check_singular_field(s, AttributeProto::STRING);
  check_singular_field(t, AttributeProto::TENSOR);
  check_singular_field(g, AttributeProto::GRAPH);
  check_singular_field(tp, AttributeProto::TYPE_PROTO);
  check_singular_field(sparse_tensor, AttributeProto::SPARSE_TENSOR);
  check_repeated_field(floats, AttributeProto::FLOATS);
  check_repeated_field(ints, AttributeProto::INTS);
  check_repeated_field(strings, AttributeProto::STRINGS);
  check_repeated_field(tensors, AttributeProto::TENSORS);
  check_repeated_field(graphs, AttributeProto::GRAPHS);
  check_repeated_field(sparse_tensors, AttributeProto::SPARSE_TENSORS);
  check_repeated_field(type_protos, AttributeProto::TYPE_PROTOS);

#undef check_type
#undef check_singular_field
#undef check_repeated_field

  // Normally, used_fields is expected to be 1.
  // In proto3, when the value to be set is type default value (say 0 for
  // int), used_fields may be 0.
  if (used_fields > 1) {
    fail_check("Attribute (name: ", attr.name(), ") should not contain more than one value field.");
  }

  if (!ctx.is_main_graph()) {
    // It's an attribute of a node in function body.
    if (attr.has_ref_attr_name() && used_fields != 0) {
      // The attribute proto is supposed to refer to data outside and does not
      // have its own value field set.
      fail_check("Attribute (name: ", attr.name(), ") should refer to attribute in parent node.");
    }
  }

  if (attr.has_t()) {
    check_tensor(attr.t(), ctx);
  }

  if (attr.has_sparse_tensor()) {
    check_sparse_tensor(attr.sparse_tensor(), ctx);
  }

  if (attr.has_g()) {
    CheckerContext subgraph_ctx(ctx);
    subgraph_ctx.set_is_main_graph(false);
    check_graph(attr.g(), subgraph_ctx, lex_ctx);
  }

  for (const auto& tensor : attr.tensors()) {
    check_tensor(tensor, ctx);
  }
  for (const auto& sparse_tensor : attr.sparse_tensors()) {
    check_sparse_tensor(sparse_tensor, ctx);
  }
  if (attr.graphs().size() > 0) {
    CheckerContext subgraph_ctx(ctx);
    subgraph_ctx.set_is_main_graph(false);
    for (const auto& graph : attr.graphs()) {
      check_graph(graph, subgraph_ctx, lex_ctx);
    }
  }
}

void print_warning_if_has_experimental(const std::unordered_set<std::string>& used_experimental_ops) {
  if (!used_experimental_ops.empty()) {
    std::string all_experimental_ops;
    for (const auto& op : used_experimental_ops) {
      all_experimental_ops += " " + op + ",";
    }
    // Remove the last comma which is unnecessary
    all_experimental_ops.pop_back();
    std::cout << "Warning: Model contains experimental ops:" + all_experimental_ops << std::endl;
  }
}

void check_node(const NodeProto& node, const CheckerContext& ctx, const LexicalScopeContext& lex_ctx) {
  enforce_non_empty_field(node, op_type);

  if (node.input().empty() && node.output().empty()) {
    fail_check("NodeProto (name: ", node.name(), ", type: ", node.op_type(), ") has zero input and zero output.");
  }

  // Resolve domain for node
  const auto& opset_imports = ctx.get_opset_imports();
  auto dit = opset_imports.find(node.domain());
  if (dit == opset_imports.end()) {
    fail_check("No opset import for domain '" + node.domain() + "'");
  }
  auto domain_version = dit->second;

  // for ops referencing local functions, there is no schema to verify it.
  // will add a check to verify consistency between these ops and local functions.
  std::unordered_set<std::string> seen_attr_names{};
  for (const auto& attr : node.attribute()) {
    if (!seen_attr_names.insert(attr.name()).second) {
      fail_check("Attribute '", attr.name(), "' appeared multiple times.");
    };

    check_attribute(attr, ctx, lex_ctx);
  }

  // This issue will be caught by check_graph instead
  if (check_is_experimental_op(node)) {
    return;
  }

  const auto* schema = ctx.get_schema_registry()->GetSchema(node.op_type(), domain_version, node.domain());
  if (!schema) {
    if (node.domain() == ONNX_DOMAIN || node.domain() == AI_ONNX_ML_DOMAIN || node.domain() == "ai.onnx" ||
        node.domain() == AI_ONNX_TRAINING_DOMAIN || ctx.check_custom_domain()) {
      // fail the checker if op is in built-in domains or if it has no schema when `check_custom_domain` is true
      fail_check(
          "No Op registered for " + node.op_type() + " with domain_version of " +
          ONNX_NAMESPACE::to_string(domain_version));
    }
  } else if (schema->Deprecated()) {
    fail_check(
        "Op registered for " + node.op_type() + " is deprecated in domain_version of " +
        ONNX_NAMESPACE::to_string(domain_version));
  } else {
    schema->Verify(node);
  }
}

void check_graph(const GraphProto& graph, const CheckerContext& ctx, const LexicalScopeContext& parent_lex) {
  enforce_non_empty_field(graph, name);

  for (const auto& value_info : graph.input()) {
    check_value_info(value_info, ctx);
  }
  for (const auto& value_info : graph.output()) {
    check_value_info(value_info, ctx);
  }

  // Inherit values available in outer scope
  // Note that we do not allow shadowing, so the presence of an already-defined
  // name is always an error.
  LexicalScopeContext lex_ctx{parent_lex};

  for (const auto& value_info : graph.input()) {
    // TODO: If shadowing isn't allowed, this should maybe use
    // this_or_ancestor_graph_has
    if (lex_ctx.this_graph_has(value_info.name())) {
      fail_check(
          "Graph must be in single static assignment (SSA) form, however '",
          value_info.name(),
          "' has been used as graph input names multiple times.");
    }
    lex_ctx.add(value_info.name());
  }

  std::unordered_set<std::reference_wrapper<const std::string>, std::hash<std::string>, std::equal_to<std::string>>
      initializer_name_checker;

  for (const auto& init : graph.initializer()) {
    enforce_has_field(init, name);
    const auto& name = init.name();
    if (name.empty()) {
      fail_check("Tensor initializers must have a non-empty name");
    }

    if (!initializer_name_checker.insert(std::cref(name)).second) {
      fail_check(name + " initializer name is not unique");
    }

    check_tensor(init, ctx);

    if (ctx.get_ir_version() <= 0x00000003) {
      // Initializers are a subset of graph inputs for IR_VERSION <= 3
      if (!lex_ctx.this_graph_has(name)) {
        fail_check(name + " in initializer but not in graph input");
      }
    } else {
      // An initializer is allowed to have the same name as an input,
      // but is not required to (for IR_VERSION >= 4)
      lex_ctx.add(name);
    }
  }

  for (const auto& sparse_init : graph.sparse_initializer()) {
    const auto& values = sparse_init.values();
    enforce_has_field(values, name);
    const auto& name = values.name();
    if (name.empty()) {
      fail_check("Sparse tensor initializers must have a non-empty name");
    }
    if (!initializer_name_checker.insert(std::cref(name)).second) {
      fail_check(name + " sparse initializer name is not unique across initializers and sparse_initializers");
    }
    check_sparse_tensor(sparse_init, ctx);
    lex_ctx.add(name);
  }
  std::unordered_set<std::string> used_experimental_ops;
  for (const auto& node : graph.node()) {
    // nodes must be in topologically sorted order
    for (const auto& input : node.input()) {
      // explicit optional input
      if (input.empty()) {
        continue;
      }
      if (!lex_ctx.this_or_ancestor_graph_has(input)) {
        fail_check(
            "Nodes in a graph must be topologically sorted, however input '",
            input,
            "' of node: \n",
            "name: ",
            node.name(),
            " OpType: ",
            node.op_type(),
            "\n is not output of any previous nodes.");
      }
    }

    if (check_is_experimental_op(node)) {
      used_experimental_ops.insert(node.op_type());
    }

    // This needs to happen before SSA check since we don't want to recurse and
    // find that outputs from control flow ops are colliding with names in the
    // inner block

    ONNX_TRY {
      check_node(node, ctx, lex_ctx);
    }
    ONNX_CATCH(ValidationError & ex) {
      ONNX_HANDLE_EXCEPTION([&]() {
        ex.AppendContext("Bad node spec for node. Name: " + node.name() + " OpType: " + node.op_type());
        ONNX_THROW_EX(ex);
      });
    }
    // check for SSA form
    for (const auto& output : node.output()) {
      // optional output
      if (output.empty()) {
        continue;
      }

      if (lex_ctx.this_or_ancestor_graph_has(output)) {
        fail_check(
            "Graph must be in single static assignment (SSA) form, however '",
            output,
            "' has been used as output names multiple times.");
      }
      lex_ctx.add(output);
    }
  }
  print_warning_if_has_experimental(used_experimental_ops);
}

// Utilify function to get the imported version of domain from opset imports
// Returns -1 if requested domain is not found in the opset_imports
int get_version_for_domain(const std::string& domain, const std::unordered_map<std::string, int>& opset_imports) {
  auto it = opset_imports.find(domain);
  if (it == opset_imports.end()) {
    return -1;
  }

  return it->second;
}

void check_opset_compatibility(
    const NodeProto& node,
    const CheckerContext& ctx,
    const std::unordered_map<std::string, int>& func_opset_imports,
    const std::unordered_map<std::string, int>& model_opset_imports) {
  auto func_opset_version = get_version_for_domain(node.domain(), func_opset_imports);
  auto model_opset_version = get_version_for_domain(node.domain(), model_opset_imports);

  if (func_opset_version == -1) {
    fail_check("No Opset registered for domain " + node.domain());
  }

  if (model_opset_version == -1) {
    // model does not include opset import for a node present in function body.
    // This is ok as along as the opset import is present in function level opset imports.
    return;
  }

  if (func_opset_version == model_opset_version) {
    // both versions are same, no need to verify schema.
    return;
  }

  const auto* schema_for_model_import =
      ctx.get_schema_registry()->GetSchema(node.op_type(), model_opset_version, node.domain());

  const auto* schema_for_function_import =
      ctx.get_schema_registry()->GetSchema(node.op_type(), func_opset_version, node.domain());

  if (!schema_for_model_import && !schema_for_function_import) {
    // the op belongs to a custom domain so we cannot verify schema
    return;
  }

  // if schema is present for 1 but not other or the schema since versions do not match then raise an error
  if (!schema_for_model_import || !schema_for_function_import ||
      schema_for_function_import->since_version() != schema_for_model_import->since_version()) {
    fail_check(
        "Opset import for domain " + node.domain() + " in function op " + node.op_type() +
        "is not compatible with the version imported by model. FunctionOp imports version " +
        ONNX_NAMESPACE::to_string(func_opset_version) + " whereas model imports version " +
        ONNX_NAMESPACE::to_string(model_opset_version));
  }
}

void check_model_local_functions(
    const ModelProto& model,
    const CheckerContext& ctx,
    const LexicalScopeContext& parent_lex) {
  // make a copy of model opset imports to maintain a main copy of opset imports across the model and
  // all model local functions to verify opset compatibility
  std::unordered_map<std::string, int> model_opset_imports(ctx.get_opset_imports());

  // merge the opset imports from every function in model_opset_imports
  // only add the opset import if an entry for it does not exist in model_opset_imports
  // if there is an entry then the compatibility will be checked later on in check_opset_compatibility
  // called by check_function.
  for (const auto& function_proto : model.functions()) {
    for (const auto& opset_import : function_proto.opset_import()) {
      if (get_version_for_domain(opset_import.domain(), model_opset_imports) == -1) {
        model_opset_imports[opset_import.domain()] = opset_import.version();
      }
    }
  }

  CheckerContext ctx_copy = ctx;
  ctx_copy.set_opset_imports(model_opset_imports);

  for (const auto& function_proto : model.functions()) {
    check_function(function_proto, ctx_copy, parent_lex);
  }
}

void check_function(const FunctionProto& function, const CheckerContext& ctx, const LexicalScopeContext& parent_lex) {
  enforce_non_empty_field(function, name);

  if (ctx.get_ir_version() >= 0x00000008) {
    enforce_has_field(function, domain);
  }

  const auto& model_opset_imports = ctx.get_opset_imports();
  CheckerContext ctx_copy = ctx;

  std::unordered_map<std::string, int> func_opset_imports;
  for (auto& relied_opset : function.opset_import()) {
    func_opset_imports[relied_opset.domain()] = static_cast<int>(relied_opset.version());
  }

  ctx_copy.set_opset_imports(func_opset_imports);

  LexicalScopeContext lex_ctx{parent_lex};

  for (const auto& input : function.input()) {
    // TODO: If shadowing isn't allowed, this should maybe use
    // this_or_ancestor_graph_has
    if (lex_ctx.this_graph_has(input)) {
      fail_check(
          "Graph must be in single static assignment (SSA) form, however '", input, "' has been used multiple times.");
    }
    lex_ctx.add(input);
  }

  std::unordered_set<std::string> outputs;
  for (const auto& output : function.output()) {
    auto result = outputs.insert(output);
    if (!result.second) {
      fail_check("function (", function.name(), ") should not have duplicate outputs specified.");
    }
  }

  std::unordered_set<std::string> attrs;
  for (const auto& attr : function.attribute()) {
    auto result = attrs.insert(attr);
    if (!result.second) {
      fail_check("function (", function.name(), ") should not have duplicate attributes specified.");
    }
  }
  std::unordered_set<std::string> used_experimental_ops;
  for (const auto& node : function.node()) {
    // nodes must be in topologically sorted order
    for (const auto& input : node.input()) {
      // explicit optional input
      if (input.empty()) {
        continue;
      }
      if (!lex_ctx.this_graph_has(input)) {
        fail_check(
            "Nodes in a function must be topologically sorted, however input '",
            input,
            "' of node: \n",
            "Name: ",
            node.name(),
            " OpType: ",
            node.op_type(),
            "\n is neither output of any previous nodes nor input of the function.");
      }
    }

    // check whether the opset version imported for a domain by function and model are
    // compatible
    if (!ctx_copy.skip_opset_compatibility_check())
      check_opset_compatibility(node, ctx_copy, func_opset_imports, model_opset_imports);
    if (check_is_experimental_op(node)) {
      used_experimental_ops.insert(node.op_type());
    }
    check_node(node, ctx_copy, lex_ctx);

    // check for SSA form
    for (const auto& output : node.output()) {
      // optional output
      if (output.empty()) {
        continue;
      }
      if (lex_ctx.this_or_ancestor_graph_has(output)) {
        fail_check(
            "Function must be in single static assignment (SSA) form, however '",
            output,
            "' has been used as output names multiple times.");
      }
      lex_ctx.add(output);
    }
  }
  print_warning_if_has_experimental(used_experimental_ops);
}

void check_model(const ModelProto& model, CheckerContext& ctx) {
  if (!model.ir_version()) {
    fail_check("The model does not have an ir_version set properly.");
  }
  if (model.ir_version() > IR_VERSION) {
    fail_check("Your model ir_version ", model.ir_version(), " is higher than the checker's (", IR_VERSION, ").");
  }
  if (model.metadata_props_size() > 1) {
    std::unordered_set<std::string> keys;
    for (const StringStringEntryProto& entry : model.metadata_props()) {
      auto i = keys.insert(entry.key());
      if (!i.second) {
        fail_check("Your model has duplicate keys in metadata_props.");
      }
    }
  }
  std::unordered_map<std::string, int> versions;
  ctx.set_ir_version(static_cast<int>(model.ir_version()));
  std::unordered_map<std::string, int> opset_imports;
  for (const auto& opset_import : model.opset_import()) {
    opset_imports[opset_import.domain()] = static_cast<int>(opset_import.version());
  }
  if (model.ir_version() >= 3) {
    if (opset_imports.empty()) {
      fail_check("model with IR version >= 3 must specify opset_import for ONNX");
    }
  } else {
    if (opset_imports.empty())
      opset_imports[ONNX_DOMAIN] = 1;
    else {
      fail_check("model with IR version < 3 cannot have opset_import specified");
    }
  }
  ctx.set_opset_imports(opset_imports);
  LexicalScopeContext lex_ctx;
  check_graph(model.graph(), ctx, lex_ctx);

  if (ctx.get_ir_version() >= 0x00000008) {
    check_model_local_functions(model, ctx, lex_ctx);
    // TODO: check consistency between local functions and ops referencing it.
  }
}

void check_model(
    const std::string& model_path,
    bool full_check,
    bool skip_opset_compatibility_check,
    bool check_custom_domain) {
  ModelProto model;
  LoadProtoFromPath(model_path, model);

  CheckerContext ctx;
  std::string model_dir;
  size_t pos = model_path.find_last_of("\\/");
  if (pos != std::string::npos) {
    model_dir = model_path.substr(0, pos + 1);
  }
  ctx.set_model_dir(model_dir);
  ctx.set_skip_opset_compatibility_check(skip_opset_compatibility_check);
  ctx.set_check_custom_domain(check_custom_domain);
  check_model(model, ctx);

  if (full_check) {
    ShapeInferenceOptions options{true, 1, false};
    ONNX_NAMESPACE::shape_inference::InferShapes(model, ctx.get_schema_registry(), options);
  }
}

void check_model(
    const ModelProto& model,
    bool full_check,
    bool skip_opset_compatibility_check,
    bool check_custom_domain) {
  CheckerContext ctx;
  ctx.set_skip_opset_compatibility_check(skip_opset_compatibility_check);
  ctx.set_check_custom_domain(check_custom_domain);
  check_model(model, ctx);
  if (full_check) {
    ShapeInferenceOptions options{true, 1, false};
    // Do not update the model in place by the check from shape inference
    // because checker should not modify the original model
    ModelProto copy = model;
    ONNX_NAMESPACE::shape_inference::InferShapes(copy, ctx.get_schema_registry(), options);
  }
}

std::string resolve_external_data_location(
    const std::string& base_dir,
    const std::string& location,
    const std::string& tensor_name) {
#ifdef _WIN32
  auto file_path = std::filesystem::path(utf8str_to_wstring(location));
  if (file_path.is_absolute()) {
    fail_check(
        "Location of external TensorProto ( tensor name: ",
        tensor_name,
        ") should be a relative path, but it is an absolute path: ",
        location);
  }
  auto relative_path = file_path.lexically_normal().make_preferred().wstring();
  // Check that normalized relative path contains ".." on Windows.
  if (relative_path.find(L"..", 0) != std::string::npos) {
    fail_check(
        "Data of TensorProto ( tensor name: ",
        tensor_name,
        ") should be file inside the ",
        base_dir,
        ", but the '",
        location,
        "' points outside the directory");
  }
  std::wstring data_path = path_join(utf8str_to_wstring(base_dir), relative_path);
  struct _stat64 buff;
  if (data_path.empty() || (data_path[0] != '#' && _wstat64(data_path.c_str(), &buff) != 0)) {
    fail_check(
        "Data of TensorProto ( tensor name: ",
        tensor_name,
        ") should be stored in ",
        location,
        ", but it doesn't exist or is not accessible.");
  }
  return wstring_to_utf8str(data_path);
#else // POSIX
  if (location.empty()) {
    fail_check("Location of external TensorProto ( tensor name: ", tensor_name, ") should not be empty.");
  } else if (location[0] == '/') {
    fail_check(
        "Location of external TensorProto ( tensor name: ",
        tensor_name,
        ") should be a relative path, but it is an absolute path: ",
        location);
  }
  std::string relative_path = clean_relative_path(location);
  // Check that normalized relative path contains ".." on POSIX
  if (relative_path.find("..", 0) != std::string::npos) {
    fail_check(
        "Data of TensorProto ( tensor name: ",
        tensor_name,
        ") should be file inside the ",
        base_dir,
        ", but the '",
        location,
        "' points outside the directory");
  }
  std::string data_path = path_join(base_dir, relative_path);
  // use stat64 to check whether the file exists
#if defined(__APPLE__) || defined(__wasm__) || !defined(__GLIBC__)
  struct stat buffer; // APPLE, wasm and non-glic stdlibs do not have stat64
  if (data_path.empty() || (data_path[0] != '#' && stat((data_path).c_str(), &buffer) != 0)) {
#else
  struct stat64 buffer; // All POSIX under glibc except APPLE and wasm have stat64
  if (data_path.empty() || (data_path[0] != '#' && stat64((data_path).c_str(), &buffer) != 0)) {
#endif
    fail_check(
        "Data of TensorProto ( tensor name: ",
        tensor_name,
        ") should be stored in ",
        data_path,
        ", but it doesn't exist or is not accessible.");
  }
  // Do not allow symlinks or directories.
  if (data_path.empty() || (data_path[0] != '#' && !S_ISREG(buffer.st_mode))) {
    fail_check(
        "Data of TensorProto ( tensor name: ",
        tensor_name,
        ") should be stored in ",
        data_path,
        ", but it is not regular file.");
  }
  return data_path;
#endif
}

std::set<std::string> experimental_ops = {
    "ATen",
    "Affine",
    "ConstantFill",
    "Crop",
    "DynamicSlice",
    "GRUUnit",
    "GivenTensorFill",
    "ImageScaler",
    "ParametricSoftplus",
    "Scale",
    "ScaledTanh"};

bool check_is_experimental_op(const NodeProto& node) {
  return (node.domain() == ONNX_DOMAIN || node.domain() == "ai.onnx") && experimental_ops.count(node.op_type());
}

#undef enforce_has_field
#undef enforce_non_empty_field

} // namespace checker
} // namespace ONNX_NAMESPACE