Shinsuke Sugaya <******@****.***> 1444529815 +0900

Shinsuke Sugaya <******@****.***> 1444529815 +0900

        assertTrue(content.contains("テスト"));
    }

    public void test_getText_ooos() {
        InputStream in = ResourceUtil.getResourceAsStream("extractor/ooo/test.ods");
        Map<String, String> params = new HashMap<String, String>();
        params.put("resourceName", "test.ods");
        ExtractData extractData = jodExtractor.getText(in, params);
        String content = extractData.getContent();
        CloseableUtil.closeQuietly(in);

namespace tensorflow {
std::string OpAsString(mlir::Operation& op);
std::string AttrAsString(mlir::Attribute& attr);

// b/281863212 enable automatic without Op/AttrAsString.
// We add logging via a wrapper struct in order to respect ODS and avoid
// multiple symbol definitions if MLIR or someone else decides to add ostream
// definitions for the MLIR symbols.
struct LoggableOperation {
  mlir::Operation& v;
  // NOLINTNEXTLINE(google-explicit-constructor)

// A derived attribute that returns the size of `idx`-th ODS-declared variadic
// operand.
class TF_DerivedOperandSizeAttr<int idx> : DerivedAttr<
  "size_t",
  "auto range = getODSOperands(" # idx # ");\n"
  "return std::distance(range.begin(), range.end());",
  [{ $_builder.getI64IntegerAttr($_self) }]>;

// A derived attribute that returns the element type of `idx`-th ODS-declared

        extensionMap.put("docx", "txt");
        extensionMap.put("wpd", "txt");
        extensionMap.put("txt", "txt");
        extensionMap.put("html", "txt");
        // Spreadsheet Formats
        extensionMap.put("ods", "tsv");
        extensionMap.put("ots", "tsv");
        extensionMap.put("sxc", "tsv");
        extensionMap.put("xls", "tsv");
        extensionMap.put("xlsx", "tsv");
        extensionMap.put("csv", "tsv");

  static auto* const registry = []() {
    auto* registry = new CostFunctionRegistry;
    // TODO(chky): Find a more scalable way to register cost functions. One
    // option is to incorporate it is TF MLIR ODS.
    RegisterCostFunction<mlir::TF::GatherV2Op>(*registry, InferGatherV2Cost);
    RegisterCostFunction<mlir::TF::SparseSegmentSumOp>(
        *registry, InferSparseSegmentOpCost<mlir::TF::SparseSegmentSumOp>);

    if (spec->quantizable_operands.empty()) return failure();

    for (auto idx : spec->quantizable_operands) {
      // This op is guaranteed to be a constant as ODS checks IsConstTensor.
      // Check if the number of elements meets the requirement.
      int current_num_elements =
          mlir::cast<ShapedType>(call_op.getOperand(idx).getType())
              .getNumElements();

00000040  00 00 00 00 00 00 00 00  00 00 00 00 13 03 00 00  |................|
00000050  2e 00 2b 00 02 03 04 00  33 00 24 00 1d 00 20 21  |..+.....3.$... !|
00000060  18 6f 64 73 17 22 b0 11  60 92 ab 01 b3 fb 8a 2d  |.ods."..`......-|
00000070  27 15 5a 46 ec 6c c3 0c  a7 73 00 b2 7e 9f 4f 14  |'.ZF.l...s..~.O.|
00000080  03 03 00 01 01 17 03 03  00 24 68 7f 90 b2 66 74  |.........$h...ft|

  }

 private:
  // Return true is there are still unfilled ODS slots for adding more inputs.
  bool IsNextODSArgAvailable();

  MLIRContext* context_;
  MlirFunctionContext* function_context_;
  SmallVector<Value, 8> operands_;
  llvm::StringMap<Attribute> attrs_;
  std::unique_ptr<OperationState> state_;
  // This is the index of the next ODS operand that will be added with AddInput
  // or AddInput;