# RUN: tf_tfl_translate -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -unfold_batchmatmul=true -output-mlir %s -o - 2>&1 | FileCheck %s

node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2
        }

  // CHECK: tfrt_fallback_async.executeop key(2) cost({{.*}}) device("/device:CPU:0") "tf.MatMul"
  %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
  %1 = "tf.MatMul"(%arg0, %0) {T = f32, device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>

// CHECK-NEXT:    [[TENSOR:%.*]] = "tf_mlrt.tf_await"([[FURTURE]]) : (!mlrt.future) -> tensor<3x1xf32>
// CHECK-NEXT:    "tf.MatMul"(%arg0, [[TENSOR]]) : (tensor<1x3xf32>, tensor<3x1xf32>) -> tensor<1x1xf32>

        out = math_ops.matmul(input_tensor, self.filters, name='sample/matmul')
        if bias_fn is not None:
          out = bias_fn(out, self.bias)
        if activation_fn is not None:
          out = activation_fn(out)
        return {'output': out}

    model = MatmulModel(weight_shape)
    saved_model_save.save(
        model,
        saved_model_path,
        signatures=model.matmul.get_concrete_function(

// CHECK-LABEL: private @composite_matmul_with_bias_and_relu6_fn_1
// CHECK-NEXT: %[[matmul:.*]] = "tf.MatMul"(%arg0, %arg1)
// CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b"
// CHECK-NEXT: tf.BiasAdd
// CHECK-NEXT: tf.Relu6
// CHECK-NEXT: return

// CHECK-LABEL: private @composite_matmul_with_bias_and_relu_fn_1
// CHECK-NEXT: tf.MatMul"(%arg0, %arg1)
// CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b"

// CHECK:  "tf.VarHandleOp"
// CHECK-NOT:  [[VARIABLE:%.*]] = "tf.ReadVariableOp"
// CHECK-NEXT:  [[KEY:%.*]], [[FUTURE:%.*]] = "tf.IfrtLoadVariable"
// CHECK-SAME:    used_by_host = true
// CHECK-NEXT:  [[MATRES:%.*]] = "tf.MatMul"(%arg0, [[FUTURE]])
// CHECK-NEXT:   [[RES:%.*]] = "tf.IfrtCall"(%arg0, [[KEY]]) <{program_id = 6515870160938153680 : i64, variable_arg_indices = [1 : i32]}>
// CHECK-NEXT:    return [[RES]], [[MATRES]] : tensor<1x1xf32>, tensor<1x1xf32>

    (TF_SubOp $beta, (TF_MulOp $m, $mul)))>;

class TFi32<int v> : ConstantAttr<I32ElementsAttr, !cast<string>(v)>;

// Matmul without transpose on b to matmul with explicit transpose op and
// transposed b.
def ConvertMatmulWithoutTransposeToWithTranspose :
      Pat<(TF_MatMulOp $a, $b, ConstBoolAttrFalse:$at, ConstBoolAttrFalse, $grad_a, $grad_b),

// CHECK:    return
//
// CHECK:  func.func @_ifrt_program__func(%arg0: tensor<1x3xf32>, %arg1: tensor<3x1xf32>) -> tensor<1x1xf32>
// CHECK-SAME:      tfrt_ifrt_serving.program_id = [[PROGRAM_ID]] : i64
// CHECK-NEXT:     %0 = "tf.MatMul"(%arg0, %arg1)
// CHECK:          return

      ]
  )
  def matmul(self, matmul_input: core.Tensor) -> Mapping[str, core.Tensor]:
    """Performs a matrix multiplication.

    Args:
      matmul_input: Input tensor to matmul with the filter.

    Returns:
      A map of: output key -> output result.
    """
    out = math_ops.matmul(matmul_input, self.matmul_filters)

    return {'output': out}

    ).astype(np.float32)

    class TwoMatmulModel(module.Module):
      """A model with two matmul ops."""

      @def_function.function
      def matmul(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]:
        """Performs a matrix multiplication.

        Args:
          input_tensor: Input tensor to matmul with the filter.

        Returns:
          A 'output' -> output tensor mapping
        """