#include "llvm/Support/raw_ostream.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_utils.h"
#include "tensorflow/lite/model.h"

using llvm::cl::opt;

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s.mlir -o - \
// RUN:   | %p/importer_test_min_max - \
// RUN:   | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - \
// RUN:   | FileCheck %s

#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_export.h"
#include "tensorflow/compiler/mlir/lite/flatbuffer_import.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/utils/convert_type.h"

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace TFL {
namespace {

#define GEN_PASS_DEF_ANALYZEVARIABLESPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

// Attribute name to be added on the module to identify whether

#include "tensorflow/compiler/mlir/lite/flatbuffer_operator.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/offset_buffer.h"
#include "tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.h"
#include "tensorflow/compiler/mlir/lite/schema/mutable/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_utils.h"

If you want to explore the SQLite database (file) directly, independently of FastAPI, to debug its contents, add tables, columns, records, modify data, etc. you can use <a href="https://sqlitebrowser.org/" class="external-link" target="_blank">DB Browser for SQLite</a>.

It will look like this:

<img src="/img/tutorial/sql-databases/image02.png">

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/runtime_metadata_generated.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.h"

  bool outline_tf_while = false;
  // Whether to do shape inference.
  bool shape_inference = true;
  // Whether to do TFLite runtime verification.
  bool runtime_verification = true;
  // Whether to enable TFLite variables or not, this will allow
  // mutable variables and produce ReadVariable/AssignVariable ops in TFLite.
  bool enable_tflite_variables = false;
  // Whether to disable the variable freezing pass or not.

bool IsStablehloOp(const tflite::OperatorCodeT &op_code);

// Returns the MLIR op name for the flatbuffer operator corresponding to
// `op_code`.
std::string GetMlirOpNameFromOpCode(const ::tflite::OperatorCodeT &op_code);

// Returns the builtin op code for the given MLIR operation on success; emits
// error and returns std::nullopt on failure.
std::optional<tflite::BuiltinOperator> GetBuiltinOpCode(Operation *mlir_op);

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Ensure bucketize roundtrip exactly

func.func @main(%arg0: tensor<3x2xf32>) -> tensor<3x2xi32> {
  // CHECK-LABEL: @main
  // CHECK: "tfl.bucketize"(%arg0) <{boundaries = [0.000000e+00 : f32, 1.000000e+01 : f32, 1.000000e+02 : f32]}> : (tensor<3x2xf32>) -> tensor<3x2xi32>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Ensure "quantfork.stats" roundtrip exactly

func.func @main(%arg0: tensor<1x512x672x8xf32>) -> tensor<1x512x672x8xf32> {
// CHECK-LABEL: @main
// CHECK: %[[RES0:.*]] = "quantfork.stats"(%arg0) <{layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>}> : (tensor<1x512x672x8xf32>) -> tensor<1x512x672x8xf32>