// refreshes.
//
// This file includes another file, `tf_generated_ops.td`, which contains
// all ops whose definitions are generated from TensorFlow codebase.
// Changes made there are not respected.

#ifndef TF_OPS
#define TF_OPS

include "tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_op_base.td"
include "mlir/Interfaces/CallInterfaces.td"

        "//tensorflow/cc:cc_ops",
        "//tensorflow/cc:cc_ops_internal",
        "//tensorflow/cc:function_ops",
        "//tensorflow/cc:functional_ops",
        "//tensorflow/cc:ops",
        "//tensorflow/cc:resource_variable_ops",
        "//tensorflow/cc:sendrecv_ops",
        "//tensorflow/compiler/jit/kernels:xla_ops",
        "//tensorflow/compiler/tf2xla:xla_compiler",

  @parameterized.parameters(
      testing.parameter_combinations([{
          'bias_fn': (
              None,
              nn_ops.bias_add,
          ),
          'activation_fn': (
              None,
              nn_ops.relu,
              nn_ops.relu6,
          ),
          'dim_sizes': (
              # tf.MatMul cases.
              ([None, 1024], [1024, 3]),  # dynamic batch dim.

    //    `reshaped_permuted` according to `crops` to produce the output of
    //    shape:
    //      [batch / prod(block_shape),
    //
    //       input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
    //       ...,
    //       input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],
    //
    //       input_shape[M+1], ..., input_shape[N-1]]

                "//tensorflow/c:env",
                "//tensorflow/c:kernels",
                "//tensorflow/c:kernels_experimental",
                "//tensorflow/c:logging",
                "//tensorflow/c:ops",
                "//tensorflow/cc/saved_model:fingerprinting_impl",
                "//tensorflow/cc/saved_model:loader_lite_impl",
                "//tensorflow/cc/saved_model:metrics_impl",

#include "tensorflow/compiler/mlir/quantization/tensorflow/utils/tf_to_uniform_attribute_utils.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/mangling_util.h"
#include "tensorflow/core/ir/importexport/convert_tensor.h"

namespace mlir {
namespace quant {
namespace {

#include "tensorflow/compiler/mlir/lite/utils/size_utils.h"
#include "tensorflow/compiler/mlir/lite/utils/utils.h"
#include "tensorflow/compiler/mlir/lite/utils/validators.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/einsum.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/unroll_batch_matmul.h"

#include "tensorflow/compiler/mlir/tensorflow/analysis/resource_alias_analysis.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/resource_op_lifting_cleanup.h"

include "mlir/Dialect/Func/IR/FuncOps.td"
include "tensorflow/compiler/mlir/lite/ir/tfl_ops.td"
include "tensorflow/compiler/mlir/lite/utils/utils.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"

// Checks if the param passed is a F32 ElementsAttr.
def F32ElementsAttr : ElementsAttrBase<
  CPred<"$_self.isa<ElementsAttr>() && $_self.cast<ElementsAttr>().getShapedType().getElementType().isF32()">,

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_structs.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.h"