// RUN: tf-quant-opt %s -split-input-file -quant-preprocess-op='target-opset=XLA quantization-method=weight_only enable-per-channel-quantization=true' | FileCheck --check-prefix PerChannel %s

module {
  // For XLA weight-only per-channel depthwise convolution, tensor shape should have

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "llvm/FileCheck/FileCheck.h"
#include "mlir/IR/Block.h"  // from @llvm-project
#include "mlir/IR/Location.h"  // from @llvm-project
#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/IR/Operation.h"  // from @llvm-project

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-unconditionally-use-set-output-shapes-on-import -tf-enable-shape-inference-on-import=true -tf-graph-as-function %s -o - | FileCheck %s

# Verify importing with _output_shapes enabled works as expected.

node {
  name: "_Arg"
  op: "_Arg"
  attr {

# limitations under the License.
# ==============================================================================
"""Test file to display the error message and verify it with FileCheck."""

# RUN: %p/saved_model_error | FileCheck %s

import sys

from absl import app

import tensorflow.compat.v2 as tf
if hasattr(tf, 'enable_v2_behavior'):
  tf.enable_v2_behavior()


class TestModule(tf.Module):

# RUN: not tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - 2>&1 | FileCheck %s

this is not a valid graph def

# CHECK: Error parsing Protobuf

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input -tf-input-data-types=DT_INT32 -tf-input-shapes=8 -tf-output-arrays=input -o - | FileCheck %s

node {
  name: "input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
}
versions {
  producer: 27
}

# CHECK-LABEL: func @main

// RUN: tf-opt -tfl-trim-funcs-tf="trim-funcs-allowlist=bar,foobar" %s | FileCheck %s

func.func @foo(%arg0: tensor<1x4xf32>, %arg1: tensor<1x4xf32>) -> tensor<1x4xf32> {
  func.return %arg0 : tensor<1x4xf32>
}

func.func @bar(%arg0: tensor<2x4xf32>, %arg1: tensor<2x4xf32>) -> tensor<2x4xf32> {
  func.return %arg0 : tensor<2x4xf32>
}

func.func @foobar(%arg0: tensor<1x4xf32>, %arg1: tensor<1x4xf32>) -> tensor<1x4xf32> {

// RUN: tf-opt %s -tfl-prepare-tf -tfl-legalize-tf='run-tfl-runtime-verification=false'  -tfl-optimize | FileCheck %s

func.func @broadcast_to_bf16(%arg0: tensor<3xbf16>, %arg1: tensor<2xi64>) -> tensor<3x3xbf16> {
  %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xbf16>, tensor<2xi64>) -> tensor<3x3xbf16>
  func.return %0: tensor<3x3xbf16>

// CHECK-LABEL: broadcast_to_bf16
// CHECK:  [[CST:%.*]] = arith.constant dense<1.000000e+00> : tensor<3x3xbf16>

// RUN: tf-opt %s --tf-print | FileCheck %s

module {
// Smoke test. We don't expect any modifications of the MLIR.

// CHECK-LABEL: foo
// CHECK: return
func.func @foo(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  return %arg0 : tensor<f32>
}

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir --mlir-print-debuginfo -o - | FileCheck %s

// CHECK: #[[LOC0:.*]] = loc("<stdin>":0:0)
// CHECK: "tfl.split"
// CHECK-SAME: loc(#[[SPLIT_LOC:.*]])
// CHECK: #[[LOC1:.*]] = loc("output0"(#[[LOC0]]))
// CHECK: #[[LOC2:.*]] = loc("output1"(#[[LOC0]]))
// CHECK: #[[SPLIT_LOC]] = loc(fused[#[[LOC1]], #[[LOC2]]])

{
  "version": 3,
  "operator_codes": [
    {