package org.gradle.api.tasks.testing.logging;

/**
 * Stack trace filters for test logging. Multiple filters can be combined.
 */
public enum TestStackTraceFilter {
    // ordered by how much they filter
    ENTRY_POINT, TRUNCATE, GROOVY

       "http://clang.llvm.org/docs/CrossCompilation.html#cpu-fpu-abi"},
      {"target_features", &flags->target_features,
       "Target features, e.g. +avx2, +neon, etc."},
      {"entry_point", &flags->entry_point,
       "Name of the generated function.  If multiple generated object files "
       "will be linked into the same binary, each will need a unique entry "
       "point."},
      {"cpp_class", &flags->cpp_class,

  string graph;
  string debug_info;
  string debug_info_path_begin_marker;
  string config;
  bool dump_fetch_nodes = false;
  string target_triple;
  string target_cpu;
  string target_features;
  string entry_point;
  string cpp_class;
  string out_function_object;
  string out_metadata_object;
  string out_header;
  string out_session_module;
  string mlir_components;
  bool experimental_quantize = false;

struct CompileResult {
  // Contains object file and meta-info.
  std::unique_ptr<xla::cpu::CpuAotCompilationResult> aot;
  xla::ProgramShapeProto program_shape;  // Static shape of args and results.
  string entry_point;                    // Name of generated function.
  int pointer_size = 0;                  // Size of a pointer in bytes.
};

// CompileGraph compiles the graph_def into an object file containing a function

#ifndef __s390x__
  flags.target_triple = "x86_64-pc-linux";
#endif
  flags.out_function_object = "out_model.o";
  flags.out_metadata_object = "out_helper.o";
  flags.out_header = "out.h";
  flags.entry_point = "entry";
  flags.debug_info_path_begin_marker = "";

  // Note that tfcompile.bzl's tf_library macro sets fast math flags as that is
  // generally the preferred case.
  std::vector<tensorflow::Flag> flag_list;

        return new AndSpec<StackTraceElement>(filters);
    }

    private Spec<StackTraceElement> createFilter(TestDescriptor descriptor, TestStackTraceFilter filterType) {
        switch (filterType) {
            case ENTRY_POINT:
                return new ClassMethodNameStackTraceSpec(descriptor.getClassName(), descriptor.getName());
            case TRUNCATE:

      // always a main. This is strictly not required for TFlite. We need to
      // remove that restriction once we have support to attribute the main
      // tensorflow function in MLIR TF import using an entry_point attr.
      if (!llvm::is_contained(trim_funcs_allowlist_, "main") &&
          func.getName() == trim_funcs_allowlist_[0]) {
        func.setName(StringAttr::get(func.getContext(), "main"));
      }
    } else {

 * by subclasses as they define our entry point behaviour, but they are protected to enable
 * testing as it's difficult to test something that will call System.exit().
 */
public abstract class EntryPoint {
    private final PrintStream originalStdErr = System.err;

    /**
     * Unless the createCompleter() method is overridden, the JVM will exit before returning from this method.
     */

        if (!sym_attr) break;
        entrypoints.erase(sym_attr.getValue());
      }
    }

    if (entrypoints.empty()) {
      return fail(module, "No entrypoints found");
    }
    if (entrypoints.size() == 1) {
      auto entrypoint = entrypoints.begin()->second;
      Builder builder(entrypoint);
      entrypoint.setName(builder.getStringAttr("main"));
      return;
    }

        when:
        def entryPoint1 = new Object()
        def entryPoint2 = new Object()
        tracker.enterDynamicCall(entryPoint1)
        tracker.enterDynamicCall(entryPoint2)
        tracker.leaveDynamicCall(entryPoint2)
        tracker.leaveDynamicCall(entryPoint1)

        then:
        log == ["enter", entryPoint1, "enter", entryPoint2, "leave", entryPoint2, "leave", entryPoint1]
    }