// TODO(b/329300758): re-enable filecheck | FileCheck %s
// Tests -input-arrays flag.

func.func @main(%arg0: tensor<4xf32>) -> tensor<4xf32> {
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.squared_difference"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("squared_difference")

  func.return %0 : tensor<40x40xf32>

// CHECK-LABEL: func @main(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>) -> tensor<40x40xf32>
// CHECK:      %[[CONST:[0-9]+]] = "tfl.pseudo_const"() <{value = dense<1.000000e+00> : tensor<40xf32>}>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main() -> tensor<3x2xi32> {
  %0 = "tfl.pseudo_const" () {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>
}

// CHECK:      {
// CHECK-NEXT:     version: 3,
// CHECK-NEXT:     operator_codes: [ ],
// CHECK-NEXT:     subgraphs: [ {
// CHECK-NEXT:       tensors: [ {

  %1 = "tfl.batch_matmul"(%0, %arg0) {adj_x = false, adj_y = false} : (tensor<1x1x16384xi32>, tensor<1x16384x257xi32>) -> tensor<1x1x257xi32>
  func.return %1 : tensor<1x1x257xi32>
  // CHECK: %[[CONST_DIM:.*]] = "tfl.pseudo_const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK: %[[RED:.*]] = "tfl.sum"(%arg0, %[[CONST_DIM]]) <{keep_dims = true}> : (tensor<1x16384x257xi32>, tensor<1xi32>) -> tensor<1x1x257xi32>
}

  // CHECK-SAME:      <[-1.000000e+00, 1.000000e+00]> : tensor<2xf32>}>
  // CHECK-SAME:      : (tensor<40x37xf32>) -> tensor<40x37xf32>
  // CHECK-NEXT:    %[[cst:.*]] = "tfl.pseudo_const"() <{value = dense<
  // CHECK-SAME:      1.000000e+00> : tensor<40xf32>}> : () -> tensor<40xf32>
  // CHECK-NEXT:    %[[fc:.*]]:2 = "tfl.fully_connected"(%[[stat]], %arg1,

// CHECK-NEXT:   %{{.*}} = "tfl.pseudo_qconst"() <{qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678{{[0-9]*}}:151>>, value = dense<-76> : tensor<32x3x3x3xi8>}> : () -> tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.021826678{{[0-9]*}}:151>>

    For example, if the kernel does not support dynamic range quantization the
    graph will be converted into the following IR:

    %q_w = "tfl.pseudo_qconst"() {
         qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
    %w = "tfl.dequantize"(%q_w) :
         (tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) ->

    %4 = "tfl.pseudo_qconst"() {qtype = tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>, value = dense<1> : tensor<1x112x112x32xi8>} : () -> tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>