// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

package art

import (
	_ "embed"

	"github.com/fatih/color"
)

//go:embed istio-ascii.txt
var istioASCIIArt string

func IstioArt() string {
	return istioASCIIArt
}

func IstioColoredArt() string {

func.func @FusedBatchNormRelu(%arg0: tensor<8x8x8x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>, %arg3: tensor<8xf32>, %arg4: tensor<8xf32>) -> (tensor<8x8x8x8xf32>) {
// CHECK-NEXT: %[[Y:[a-z0-9]*]], {{.*}}_FusedBatchNormEx
// CHECK-NEXT: return %[[Y]]

    ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>, %arg4: tensor<i32>):
      %cst_0 = arith.constant dense<0> : tensor<i32>
      %1 = "tfl.greater"(%arg2, %cst_0) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
      "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
    ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>, %arg4: tensor<i32>):
      %1 = "tfl.sub"(%arg2, %arg4) {fused_activation_function = "NONE"} :

module {
  func.func public @main(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>, %arg2: tensor<1x100x32x4xf32>,
      %arg3: tensor<1x500x4x4xf32>, %arg4: tensor<1x500x4x4xf32>, %arg5: tensor<1x1x100x500xf32>, %arg6: tensor<f32>)
      -> tensor<1x100x32x4xf32> {

      "tfl.yield"(%3) : (tensor<i1>) -> ()
  },  {
    ^bb0(%arg3: tensor<*xi32>, %arg4: tensor<*x!tf_type.resource<tensor<*xi32>>>):
      %cst1 = arith.constant dense<2> : tensor<4xi32>
      "tf.AssignAddVariableOp"(%arg4, %cst1) {} : (tensor<*x!tf_type.resource<tensor<*xi32>>>, tensor<4xi32>) -> ()
      %4 = "tf.ReadVariableOp"(%arg4) {dtype = i32} : (tensor<*x!tf_type.resource<tensor<*xi32>>>) -> tensor<*xi32>

  // CHECK-NOT: %arg3
  // CHECK-NOT: %arg4
  func.func @f(
    %arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v},
    %arg1: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @w},
    %arg2: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v},
    %arg3: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @x},

  // CHECK-NEXT:   scf.condition(%[[CONDITION]]) %[[ARG3]], %[[ARG4]] : tensor<f32>, tensor<*xf32>
  // CHECK-NEXT: } do {
  // CHECK-NEXT: ^bb0(%[[ARG3]]: tensor<f32>, %[[ARG4]]: tensor<*xf32>):
  // CHECK-NEXT:   %[[SUB:.*]] = "tf.Sub"(%[[ARG3]], %[[CST]]) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  // CHECK-NEXT:   scf.yield %[[SUB]], %[[ARG4]] : tensor<f32>, tensor<*xf32>
  // CHECK-NEXT: }

            getExtension() >> "art-ext"
            getType() >> "art-type"
            getClassifier() >> "art-classifier"
        }

        when:
        1 * publishArtifact.getName() >> "art-name"

        then:
        def name = DefaultIvyArtifactName.forPublishArtifact(publishArtifact)
        name.name == "art-name"
        name.extension == "art-ext"
        name.type == "art-type"

// CHECK-SAME:  ([[ARG0:%.+]]: tensor<i1>, [[ARG1:%.+]]: tensor<i32>, [[ARG2:%.+]]: tensor<i64>, [[ARG3:%.+]]: tensor<f32>, [[ARG4:%.+]]: tensor<f64>, [[ARG5:%.+]]: tensor<!tf_type.resource>, [[ARG6:%.+]]: tensor<!tf_type.resource>)
func.func @replace_multiple_outputs(%arg0: tensor<i1>, %arg1: tensor<i32>, %arg2: tensor<i64>, %arg3: tensor<f32>, %arg4: tensor<f64>, %arg5: tensor<!tf_type.resource>, %arg6: tensor<!tf_type.resource>) {

// CHECK-SAME: %[[ARG_7:[a-z0-9]*]]: tensor<!tf_type.string>
func.func @check_enqueue_ops_update_for_eval(%arg0: tensor<?x2xi32>, %arg1: tensor<?x2xi32>,
  %arg2 :tensor<?x2xi32>, %arg3: tensor<?xi32>, %arg4: tensor<?xi32>, %arg5: tensor<?xi32>,
  %arg6: tensor<!tf_type.string>, %arg7: tensor<!tf_type.string>) -> () {
  // CHECK: %[[CONST_0:.*]] = "tf.Const"()
  %0 = "tf.Const"() {value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>