// CHECK: %[[quant:.*]] = "tfr.cast"(%arg0) : (tensor<1x10xi8>) -> !tfr.tensor
// CHECK: %[[scale_cast:.*]] = "tfr.cast"(%[[scale]])
// CHECK: %[[zp_cast:.*]] = "tfr.cast"(%[[zp]])
// CHECK: %[[requant:.*]] = tfr.call @tf__requantize(%[[quant]], %[[scale_cast]], %[[zp_cast]])
// CHECK: %[[result:.*]] = "tfr.cast"(%[[requant]])
// CHECK-NOT: quant.uniform
// CHECK: return %[[result]], %[[float_resunt:.*]] : tensor<1x10xi8>, tensor<1x5xf32>
}

func.func @cast_op_qint8_int8(%arg0: tensor<1x!tf_type.qint8>) -> tensor<1xi8> {
  // CHECK: "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<1xi8>) -> tensor<1xi8>
  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x!tf_type.qint8>) -> tensor<1xi8>
  func.return %0: tensor<1xi8>
}

// -----

// CHECK-LABEL: func @cast_op_int8_qint8
func.func @cast_op_int8_qint8(%arg0: tensor<1xi8>) -> tensor<1x!tf_type.qint8> {

  %0 = "tfl.pseudo_sparse_qconst"() {compressed_data = dense<[1, 2, 3, 4, -1, -2, -3, -4]> : tensor<8xi8>, qtype = tensor<3x2x!quant.uniform<u8<1:255>:f32, 1.0>>, s_param = #tfl.sparsity_parameter<traversal_order = [0, 1, 2, 3], block_map...

  func.return %0 : tensor<i32>
}

// -----

// CHECK-LABEL: constant_tensor_vector
func.func @constant_tensor_vector(%arg0: vector<1x2xi32>) -> tensor<1x2xi32> {
  %0 = "tfr.constant_tensor"(%arg0) : (vector<1x2xi32>) -> tensor<1x2xi32>
  func.return %0 : tensor<1x2xi32>
}

// -----

// CHECK-LABEL: constant_tensor_array
func.func @constant_tensor_array() -> !tfr.tensor {

    %1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
    %2 = "tf.Const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
    %3 = "tf.Const"() {value = dense<[[0, 1]]> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
    %4 = "tf.Const"() {value = dense<> : tensor<0xi32>} : () -> tensor<0xi32>
    %5 = "tf.Const"() {value = dense<2.500000e-01> : tensor<f32>} : () -> tensor<f32>

  %dot = stablehlo.dot %q1, %q2 : (tensor<1x3x!quant.uniform<i8:f32, 6.000000e-03:-128>>, tensor<3x2x!quant.uniform<i8<-127:127>:f32, 5.000000e-03>>) -> tensor<1x2x!quant.uniform<i8:f32, 1.000000e-03:-3>>
  %dq = "quantfork.dcast"(%dot) : (tensor<1x2x!quant.uniform<i8:f32, 1.000000e-03:-3>>) -> tensor<1x2xf32>
  func.return %dq : tensor<1x2xf32>
}

// -----

// CHECK-LABEL: @convert_quantfork_qdq_to_stablehlo_uniform_qdq

    %cst_1 = "tfl.pseudo_qconst"() {qtype = tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<1x20xi8>} : () -> tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>
    %cst_2 = "tfl.no_value"() {value = unit} : () -> none

// CHECK-SAME: tensor<1x3xf32>, tensor<1x3xf32>,
// CHECK-SAME: none, none, none, none)
// CHECK-SAME: -> tensor<1x2x3xf32>
// CHECK: "tfl.quantize"(%[[lstm]]) <{qtype = tensor<1x2x3x!quant.uniform<i16:f32, {{.*}}>>}> {volatile} : (tensor<1x2x3xf32>) -> tensor<1x2x3x!quant.uniform<i16:f32, {{.*}}>>

}

// CHECK-LABEL: QuantizeUnidirectionalLstmFullPerAxis

// TF-DAG: %[[cc_0:.*]] = "tf.ConcatV2"(%[[pc_1]], %[[pc_5]], %[[cst_0]]) : (tensor<2x2xi8>, tensor<2x2xi8>, tensor<i32>) -> tensor<2x4xi8>
// TF-DAG: %[[pc_7:.*]] = "tf.PartitionedCall"(%[[cc_0]], %[[cst_6]], %[[cst_5]]) <{config = "", config_proto = "", executor_type = "", f = @dequantize_i8}
// TF-DAG: return %[[pc_7]]

    return %0 : tensor<1x3xf32>
  }
}

// CHECK-LABEL: quantize_dot_general_per_tensor
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x2xf32>
// CHECK: %[[CST:.+]] = stablehlo.constant() <{value = dense<127> : tensor<2x3xi8>}> : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>