// CHECK-LABEL: func private @composite_depthwise_conv2d_fn(
// CHECK-SAME:                                             %arg0: tensor<1x3x4x3xf32>,
// CHECK-SAME:                                             %arg1: tensor<2x3x3x2xf32>)

// CHECK-LABEL: func private @composite_depthwise_conv2d_fn_0(
// CHECK-SAME:                                             %arg0: tensor<1x3x4x3xf32>,
// CHECK-SAME:                                             %arg1: tensor<2x3x1x6xf32>)

// CHECK-LABEL: func @main
// CHECK: tf.AddV2
// CHECK-SAME: {__op_key = 0 : i32}

// CHECK: tf.AddV2
// CHECK-SAME: {__op_key = 1 : i32}

// CHECK: tf.AddV2
// CHECK-SAME: {__op_key = 2 : i32}

// CHECK: tf.AddV2
// CHECK-SAME: {__op_key = 3 : i32}

// CHECK: tf.Sub
// CHECK-SAME: {__op_key = 4 : i32}

// CHECK: tf.Sub
// CHECK-SAME: {__op_key = 5 : i32}

// CHECK: tf.Sub

# it is being invoked.
# CHECK: module
# CHECK-SAME: tf.versions
# CHECK-SAME: bad_consumers
# CHECK-SAME: min_consumer
# CHECK-SAME: producer

# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR:[a-zA-Z_0-9]+]]", type = tensor<1x3xf32>, value = {{.*}} : tensor<1x3xf32>}> : () -> ()

# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME:   [[ARG0:%.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["x"]},

            }
        }
    }

    @Test
    fun `same script different target type`() {

        // given: same init, settings & build files all applying same script
        val same = withFile("same.gradle.kts", """println("Same script on ${'$'}this")""")
        val sameApply = """apply(from = "same.gradle.kts")"""
        val initScriptFile = withFile("same.init.gradle.kts", sameApply)

// CHECK-LABEL: @batchNormInference_2D_inner_features
// CHECK-SAME: %[[X:[^:[:space:]]+]]
// CHECK-SAME: %[[SCALE:[^:[:space:]]+]]
// CHECK-SAME: %[[OFFSET:[^:[:space:]]+]]
// CHECK-SAME: %[[MEAN:[^:[:space:]]+]]
// CHECK-SAME: %[[VARIANCE:[^:[:space:]]+]]
func.func @batchNormInference_2D_inner_features(

  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<f32> {tf._user_specified_name = "x", tf_saved_model.index_path = [0]},
  # CHECK-SAME:   %arg1: tensor<!tf_type.resource<{{.*}}>>
  # CHECK-SAME: ) -> (
  # CHECK-SAME:   tensor<f32> {tf_saved_model.index_path = [0]},
  # CHECK-SAME:   tensor<f32> {tf_saved_model.index_path = [1]})
  # CHECK-SAME: attributes{{.*}}tf_saved_model.exported_names = ["callee"]

  // CHECK: [[PI_0:%.*]] = "tf.TPUPartitionedInputV2"([[ARG0]], [[ARG1]])
  // CHECK-SAME: _XlaSharding = ""
  // CHECK-SAME: partition_dims = []
  // CHECK: [[PI_1:%.*]] = "tf.TPUPartitionedInputV2"([[ARG2]], [[ARG3]])
  // CHECK-SAME: _XlaSharding = ""
  // CHECK-SAME: partition_dims = []
  // CHECK: [[RI:%.*]] = "tf.TPUReplicatedInput"([[PI_0]], [[PI_1]])

    }

    def "repositories have the same id when m2 compatible and other configuration is the same"() {
        def repo = newRepo()
        def same = newRepo()
        def different = newRepo()

        given:
        repo.url = new URI("http://localhost")
        repo.patternLayout { it.m2compatible = true }
        same.url = new URI("http://localhost")
        same.patternLayout { it.m2compatible = true }

          strides = [1, 1, 2, 2]
        } : (tensor<?x3x230x230xf32>, tensor<7x7x3x64xf32>) -> tensor<?x64x112x112xf32>

  // CHECK: %[[CONV0:[0-9]*]] = "tf.Conv2D"
  // CHECK-SAME: %[[PAD]]
  // CHECK-SAME: data_format = "NHWC"
  // CHECK-SAME: strides = [1, 2, 2, 1]

  %6, %batch_mean, %batch_variance, %reserved_1, %reserved_2, %reserved_3 =
       "tf.FusedBatchNormV3"(%5, %arg1, %arg1, %arg1, %arg1)
       {

    func.return %4 : tensor<100x50xf32>
  }
  // CHECK: "tf.VarHandleOp"
  // CHECK-SAME: shared_name = "var1"
  // CHECK-SAME: _is_initialized = true
  // CHECK: "tf.VarHandleOp"
  // CHECK-SAME: shared_name = "var2"
  // CHECK-SAME: _is_initialized = true
  // CHECK: "tf.VarHandleOp"
  // CHECK-SAME: shared_name = "var3"
  // CHECK-SAME: _is_initialized = false

  // INVALID-NOT: _is_initialized