# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false -mlir-print-debuginfo %s -o - | FileCheck %s

node {
  name: "x"
  op: "Const"
  device: "/device:CPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 3
          }

// Test to verify translation & export work as intended with runtime.

// RUN: tf-opt --mlir-print-debuginfo --canonicalize --tfl-while-loop-outline %s | mlir-tflite-runner --dump-interpreter-state 2>&1 | FileCheck %s

// Verify value computed:
// ----------------------
// CHECK: result: Tensor<type: FLOAT32, shape: 1, values: 96>
// CHECK: pconst: Tensor<type: INT32, shape: , values: 1>

// Verify tensors in interpreter state:

# CHECK: {{.*tensorflow/python/ops/gen_math_ops.py:[0-9]+:[0-9]+: note: called from}}
# CHECK:         "Betainc", a=a, b=b, x=x, name=name)
# CHECK:         ^
# CHECK: {{.*tensorflow/compiler/mlir/lite/tests/debuginfo/concrete_function_error.py:[0-9]+:[0-9]+: note: called from}}
# CHECK:     y = tf.math.betainc(x, 0.5, 1.0)  # Not supported
# CHECK:     ^
# CHECK: <unknown>:0: error: failed while converting: 'main'

// RUN: tf-quant-opt %s -mlir-print-debuginfo -mlir-print-local-scope -quant-add-quantization-unit-loc | FileCheck %s

func.func @conv2d_unmatching_loc_pattern(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x3x2x2xf32>) {
  %cst = "tf.Const"() {device = "", value = dense_resource<__elided__> : tensor<2x3x3x2xbf16>} : () -> tensor<2x3x3x2xbf16>
  %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xbf16>

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false -mlir-print-debuginfo %s -o - | FileCheck %s

# Verify that importing a Graph with a backedge leads to two NextIteration nodes
# to break the cycle.

# CHECK-LABEL: func @main()
# CHECK:    %[[NEXTITERATION:[a-z0-9]+]], %[[NEXTITERATION_token:[a-z0-9]+]], {{.*}} = tf_executor.NextIteration.Source
# CHECK:    tf_executor.Merge {{.*}} %[[NEXTITERATION]]

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=a,b -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-input-shapes=':' -tf-output-arrays=StatefulIf,StatelessIf -o - -mlir-print-debuginfo -mlir-print-local-scope | FileCheck %s

# Verify that TensorFlow If and StatelessIf ops are mapped to the
# composite If op in MLIR with is_stateless attribute set accordingly to
# distinguish between them.

# CHECK: {{.*tensorflow/python/ops/gen_math_ops.py:[0-9]+:[0-9]+: note: called from}}
# CHECK:         "Betainc", a=a, b=b, x=x, name=name)
# CHECK:         ^
# CHECK: {{.*tensorflow/compiler/mlir/lite/tests/debuginfo/saved_model_error.py:[0-9]+:[0-9]+: note: called from}}
# CHECK:     y = tf.math.betainc(x, 0.5, 1.0)  # Not supported
# CHECK:     ^
# CHECK: <unknown>:0: error: failed while converting: 'main'

# RUN: not tf_tfl_translate -tf-upgrade-legacy=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=1,2:1 -tf-output-arrays=cond/Merge -tf-enable-shape-inference-on-import=false -mlir-print-debuginfo -output-mlir %s -o - 2>&1 | FileCheck %s

# CHECK: error: The graph has Control Flow V1 ops. TFLite converter doesn't support Control Flow V1 ops. Consider using Control Flow V2 ops instead.

node {
  name: "Const"
  op: "Const"
  attr {
    key: "dtype"

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false -tf-graph-as-function %s -o - -mlir-print-debuginfo -mlir-print-local-scope | FileCheck %s

node {
  name: "args_0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "args_1"
  op: "_Arg"
  attr {
    key: "T"

# RUN: tf-mlir-translate -graphdef-to-splatted-mlir %s -o - -mlir-print-debuginfo -mlir-print-local-scope | FileCheck %s

# CHECK: tf_executor._SwitchN
# CHECK-SAME: of 3 : tensor<*xi32>
# CHECK-SAME: T = i32
# CHECK-SAME: loc(fused["_SwitchN:", "Case/branch_index/_3"])
# CHECK: tf_executor._SwitchN
# CHECK-SAME: of 2 : tensor<*xf32>
# CHECK-SAME: T = f32
# CHECK-SAME: loc(fused["_SwitchN:", "Case/Case/input_0/_7"])

node {
  name: "Case/branch_index"