// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions='quantization-method=weight_only target-opset=XLA' -quant-quantize-composite-functions='quantization-method=weight_only target-opset=XLA enable-per-channel-quantization=true' -symbol-dce | FileCheck --check-prefix=PerChannel %s

module {

// RUN: tac-opt-all-backends -tfl-fold-constants-to-subgraph='fold-all-constants=false' %s -split-input-file -verify-diagnostics | FileCheck --check-prefix=PARTIAL %s

// Test for fold all constants.
// RUN: tac-opt-all-backends -tfl-fold-constants-to-subgraph='fold-all-constants=true' %s -split-input-file -verify-diagnostics | FileCheck --check-prefix=ALL %s

module {

func.func @main(%arg0: tensor<4x384x32xf32>) -> tensor<1x384x32xf32> {

// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="enable-dynamic-range-per-channel-quantization=false" | FileCheck --check-prefix=PerTensor %s
// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="enable-float16-quantization" | FileCheck --check-prefix=Float16 %s
// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="enable-custom-op-quantization=CustomTestOp=1-3,CustomTestOp3=3" | FileCheck --check-prefix=CustomOp %s

// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions-drq | FileCheck %s
// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions-drq='quantization-method=weight_only' | FileCheck --check-prefix=WEIGHTONLY %s

// CHECK-LABEL: lift_float_matmul
func.func @lift_float_matmul(%arg0: tensor<1x12x12x512xf32>) -> (tensor<*xf32>, tensor<*xf32>) {

// RUN: tf-quant-opt %s -split-input-file -quant-add-dump-tensor-op='debugger_type=whole_model' | FileCheck --check-prefix=WholeModel %s
// RUN: tf-quant-opt %s -split-input-file -quant-add-dump-tensor-op='debugger_type=int_per_layer' | FileCheck --check-prefix=IntPerLayer %s
// RUN: tf-quant-opt %s -split-input-file -quant-add-dump-tensor-op='debugger_type=float_per_layer' | FileCheck --check-prefix=FloatPerLayer %s

module {

// RUN: tf-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:   -quant-insert-restore-op | FileCheck %s
// RUN: tf-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:   -mlir-print-debuginfo -mlir-print-local-scope \
// RUN:   -quant-insert-restore-op | FileCheck %s --check-prefix CHECK-LOC

// RestoreV2 op created for a single VarHandleOp.

module attributes {tf_saved_model.semantics} {

// RUN: tf-opt %s -tfl-modify-io-nodes="test-io-types=float32,float32" | FileCheck %s
// RUN: tf-opt %s -tfl-modify-io-nodes="test-io-types=int8,int8" | FileCheck --check-prefix=INT8 %s
// RUN: tf-opt %s -tfl-modify-io-nodes="test-io-types=uint8,uint8" | FileCheck --check-prefix=UINT8 %s

func.func @modified(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input", outputs = "output"}} {

# TODO(fengliuai): Figure out what to do here. A "golden" end-to-end test is overly fragile.

// RUN: tf-opt %s -tfl-prepare-quantize="post-training-quantize=true" -tfl-quantize="numeric-verify=true log-if-failed=true" | FileCheck --check-prefix=DEBUG %s
// RUN: tf-opt %s -tfl-prepare-quantize="post-training-quantize=true" -tfl-quantize="numeric-verify=true log-if-failed=true whole-model-verify=true" | FileCheck --check-prefix=MODEL-DEBUG %s

// DEBUG-LABEL: QuantizeConv2D

# TODO(fengliuai): Figure out what to do here. A "golden" end-to-end test is overly fragile.