// license that can be found in the LICENSE file.

// ChaCha8 is ChaCha with 8 rounds.
// See https://cr.yp.to/chacha/chacha-20080128.pdf.
//
// ChaCha8 operates on a 4x4 matrix of uint32 values, initially set to:
//
//	const1 const2 const3 const4
//	seed   seed   seed   seed
//	seed   seed   seed   seed
//	counter64     0      0
//
// We use the same constants as ChaCha20 does, a random seed,
// and a counter. Running ChaCha8 on this input produces

TEST(ConstOpTest, Names) {
  Scope root = Scope::NewRootScope();
  auto c = ops::Const(root, {{2.0}, {3.0}});
  EXPECT_EQ(c.node()->name(), "Const");
  auto c_1 = ops::Const(root, {{2.0}, {3.0}});
  EXPECT_EQ(c_1.node()->name(), "Const_1");

  auto x = ops::Const(root.WithOpName("x"), 1);
  EXPECT_EQ(x.node()->name(), "x");
  auto x_1 = ops::Const(root.WithOpName("x"), 1);
  EXPECT_EQ(x_1.node()->name(), "x_1");

    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<[1.0, 2.0]> : tensor<2xf16>} : () -> tensor<2xf16> loc("const2")
    tf_executor.fetch %0#0, %1#0 : tensor<1x2xf16>, tensor<2xf16>
  }
  func.return %graph#0, %graph#1 : tensor<1x2xf16>, tensor<2xf16>
})";
  std::string result;

  auto status = Translate(kMlirSource, result);

  func.return %8 : !tfr.tensor

// CHECK: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<1> : tensor<i64>}> : () -> tensor<i64>
// CHECK: %[[BUILD_LIST_0:.*]] = "tfr.build_list"(%arg1, %arg0) : (!tfr.tensor, !tfr.tensor) -> !tfr.tensor_list
// CHECK: %[[CAST_0:.*]] = "tfr.cast"(%[[CONST_0]]) : (tensor<i64>) -> !tfr.tensor

  name: "bn_conv1_1/FusedBatchNorm"
  op: "FusedBatchNorm"
  input: "conv1_1/Conv2D"
  input: "bn_conv1_1/ReadVariableOp"
  input: "bn_conv1_1/ReadVariableOp_1"
  input: "bn5a_branch1_1/Const_1"
  input: "bn5a_branch1_1/Const_1"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "data_format"

// CHECK:   %[[CONST_0:.*]] = "tf.Const"() <{value = dense<2.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK:   %[[MUL_1:.*]] = "tf.Mul"(%arg1, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
// CHECK:   %[[MUL_2:.*]] = "tf.Mul"(%[[MUL_1]], %[[CONST_0]]) : (tensor<1xf32>, tensor<f32>) -> tensor<1xf32>
// CHECK:   return %[[MUL_2]] : tensor<1xf32>
// CHECK: }

// CHECK: "tf._TfrtSetResource"([[x]]) <{index = 0 : i64}> {device = "/CPU:0"} : (tensor<i32>) -> ()
// CHECK: [[const_1:%.*]] = "tf.Const"() <{value = dense<1> : tensor<i32>}> {device = "/CPU:0"} : () -> tensor<i32>
// CHECK: "tf._TfrtSetResource"([[const_1]]) <{index = 1 : i64}> {device = "/CPU:0"} : (tensor<i32>) -> ()

// CHECK-LABEL: func @test_hoist_const

// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x5x5x2x!quant.uniform<i8:f32, 2.000000e+00>>) -> tensor<1x14x14x4x!quant.uniform<i8:f32, 8.000000e+00:-128>>
// CHECK-DAG: %[[CONST_0:.+]] = arith.constant dense<[1, 14, 14, 4]> : tensor<4xi32>
// CHECK-DAG: %[[CONST_1:.*]] = "tfl.pseudo_const"() <{value = dense<{{\[\[0, 0\], \[1, 1\], \[1, 1\], \[0, 0\]\]}}> : tensor<4x2xi32>}> : () -> tensor<4x2xi32>

  name: "bn_conv1_1/FusedBatchNormV2"
  op: "FusedBatchNormV2"
  input: "conv1_1/Conv2D"
  input: "bn_conv1_1/ReadVariableOp"
  input: "bn_conv1_1/ReadVariableOp_1"
  input: "bn5a_branch1_1/Const_1"
  input: "bn5a_branch1_1/Const_1"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_HALF
    }
  }
  attr {
    key: "U"

  }
}

TEST(DeadnessAnalysisTest, Constant1_SwitchN_2Branches_DoesNotFail) {
  Scope root = Scope::NewRootScope().ExitOnError();

  Output constant_1 = ops::Const(root.WithOpName("const_1"), 1);
  Output value = ops::Placeholder(root.WithOpName("value"), DT_FLOAT);
  OutputList outputs;
  CreateSwitchN(root.WithOpName("switchn"), value, constant_1, 2, &outputs);