// RUN: tfr-opt %s -tfr-decompose -tfr-raise-to-tf -canonicalize -verify-diagnostics -split-input-file | FileCheck %s

//=================> User models, from GraphDef <====================

// CHECK-LABEL: my_identity
func.func @my_identity(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
  %0 = "tf.MyIdentity"(%arg0) : (tensor<2x3xf32>) -> tensor<2x3xf32>
  func.return %0 : tensor<2x3xf32>

// CHECK-NEXT: return %arg0 : tensor<2x3xf32>
}

		values.Exp.Mod(priv.D, values.Exp)

		values.R = new(big.Int).Set(r)
		values.Coeff = new(big.Int).ModInverse(r, prime)

		r.Mul(r, prime)
	}
}

const withCheck = true
const noCheck = false

// decrypt performs an RSA decryption of ciphertext into out. If check is true,
// m^e is calculated and compared with ciphertext, in order to defend against
// errors in the CRT computation.

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions | FileCheck %s

module {
  func.func @conv(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>, tensor<*xf32>) {

// RUN: tf-opt %s --tf-hoist-loop-invariant | FileCheck %s --dump-input=fail

// CHECK-LABEL: hoist_loop_invariant
// CHECK:       [[CST_0:%.*]] = "tf.Const"
// CHECK-NEXT:  [[CST_1:%.*]] = "tf.Const"
// CHECK:       [[RES_1:%.*]] = "tf.Add"([[CST_1]], [[CST_0]])
// CHECK:       [[RES_2:%.*]] = "tf.Mul"([[RES_1]], [[CST_1]])
// CHECK:       tf.WhileRegion
// CHECK:       ^bb0
// CHECK:       tf.OpA
// CHECK:       ^bb0([[ARG_2:%[a-zA-Z0-9_]+]]

// RUN: stablehlo-quant-opt %s -stablehlo-defer-activation-transpose \
// RUN:   -split-input-file -verify-diagnostics | FileCheck %s

// Tests that an `add(transpose(arg0), arg1)` pattern is converted to
// `transpose(add(arg0, transpose(arg1)))`. The transpose in the activation is
// deferred to the output of `stablehlo.add` and an extra transpose op is
// inserted to the RHS to match the shape of the operand.

// CHECK-LABEL: add_with_activation_transpose

// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-embedding-program-key | FILECHECK_OPTS="" FileCheck %s

// CHECK-LABEL: func @single_op_program_key
func.func @single_op_program_key() {
  // CHECK: %[[COMPILE_LAUNCH:[0-9]*]]:2 = "tf_device.launch"
  // CHECK: TPUCompileMlir
  // CHECK: "tf.OpA"(%[[COMPILE_LAUNCH]]#1
  %0:2 = "tf_device.launch"() ({

		// Ignore Args since they can't be autos.
		if v.Op.SymEffect() != SymNone && v.Op != OpArg {
			panic("unhandled op with sym effect")
		}

		if v.Uses == 0 && v.Op != OpNilCheck && !v.Op.IsCall() && !v.Op.HasSideEffects() || len(args) == 0 {
			// We need to keep nil checks even if they have no use.
			// Also keep calls and values that have side effects.
			return
		}

// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-tpu-resource-partition | FileCheck %s

func.func private @computation(%arg0: tensor<i32>) -> tensor<i32>

// CHECK-LABEL: func @read_write_resource
// CHECK-SAME: ([[ARG0:%.+]]: tensor<!tf_type.resource<tensor<i32>>>, [[ARG1:%.+]]: tensor<!tf_type.resource<tensor<i32>>>)
func.func @read_write_resource(%arg0: tensor<!tf_type.resource<tensor<i32>>>, %arg1: tensor<!tf_type.resource<tensor<i32>>>) {

# CHECK: fake/user/code/file_C.py:27:1: error: 'tf.Conv2D' op attribute 'data_format' failed to satisfy constraint: 'NHWC' or 'NCHW' convnet data format
# CHECK: fake/user/code/file_D.py:28:1: note: called from

// RUN: tf-opt --tfl-decompose-hybrid-quantization --verify-each %s | FileCheck %s

// CHECK-LABEL: @test_conv2d_float
func.func @test_conv2d_float(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x16xf32> {
  // CHECK-DAG: %[[VAL0:.+]] = "tfl.pseudo_const"() <{value = dense<42> : tensor<16x1x1x8xi8>}>
  // CHECK-DAG: %[[VAL1:.+]] = "tfl.pseudo_const"() <{value = dense<1> : tensor<16x1x1x8xi8>}>