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

#include "tensorflow/compiler/mlir/tfrt/function/function.h"

#include "absl/log/log.h"
#include "absl/strings/match.h"
#include "mlir/IR/OperationSupport.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project

  // to proactively transfer the result to host since the consumer op (or
  // function) of the result may still be on TPU.
  // TODO(b/194081364): remove this option once we unify servo TPU serving
  // result transfer behavior.
  bool tpu_transfer_result_to_host = false;
};

// Compile MLIR generated by tf.function in TF dialect into BEF.
Status CompileTFMLIRToBEF(const TfrtFunctionCompileOptions& options,

    tf_executor.fetch
  }
  func.return
}

// CHECK:      library {
// CHECK:        function {
// CHECK-NEXT:     signature {
// CHECK-NEXT:       name: "bar"
// CHECK-NEXT:     }
// CHECK:          node_def {
// CHECK-NEXT:       name: "tf.Const"
// CHECK-NEXT:       op: "Const"
// CHECK-NEXT:       attr {

}
node {
  name: "custom_embedding_matmul_func_call"
  op: "custom_embedding_matmul"
}
library {
  function {
    signature {
      name: "custom_relu"
    }
    attr {
      key: "_implements"
      value {
        func {
          name: "tensorflow.relu"
        }
      }
    }
  }
  function {
    signature {
      name: "custom_embedding_matmul"
    }
    attr {
      key: "_implements"

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-graph-as-function -tf-xla-compile-device-type="GPU" -tf-enable-soft-placement-on-import=true -o - | FileCheck %s

# Verify main graph was converted to a function, args/rets are mapped correctly,
# and ops in the main graph are retained. In addition, check if subsequent
# functions are converted.

// RUN: tf-mlir-translate -mlir-to-graphdef %s -tf-graph-as-function -o - | FileCheck %s

func.func @main(%arg0: tensor<*x!tf_type.resource>, %arg1: tensor<*x!tf_type.resource<tensor<3x3x1x32xf32>>>, %arg2: tensor<*xf32>, %arg3: tensor<2x4x6x8xi32>) -> (tensor<f32>, tensor<f32>)
attributes {tf.entry_function = {inputs = "args_0,args_1,args_2,args_3", outputs = "rets_0_RetVal,rets_1_RetVal"}} {
  %graph:2 = tf_executor.graph {

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

# Verify that we properly import call site function attributes.
# CHECK: tf.If
# CHECK-SAME: then_branch = @
# CHECK-SAME: then_branch.how_many = 32
# CHECK-SAME: then_branch.ping = "ack"

node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {

  attr {
    key: "_disable_call_shape_inference"
    value {
      b: true
    }
  }
  attr {
    key: "_tpu_replicate"
    value {
      s: "cluster"
    }
  }
}
library {
  function {
    signature {
      name: "test_func_name"
      input_arg {
        name: "a_0"
        type: DT_INT32
      }
      output_arg {
        name: "a"
        type: DT_INT32
      }
    }

    signature {
      name: "foo3"
    }
  }
  function {
    signature {
      name: "foo4"
    }
  }
  function {
    signature {
      name: "foo5"
    }
  }
  function {
    signature {
      name: "foo6"
    }
  }
  function {
    signature {
      name: "foo7"
    }
  }
  function {
    signature {
      name: "foo8"
    }
  }
  function {
    signature {

// resource_arg_unique_id.

// CHECK:      name: "x"
// CHECK:      op: "VarHandleOp"

// CHECK:      name: "func_call"
// CHECK:      input: "called"

// CHECK:      library
// CHECK:        function
// CHECK:          signature
// CHECK:            input_arg
// CHECK:              type: DT_RESOURCE
// CHECK:            input_arg
// CHECK:              type: DT_RESOURCE
// CHECK:            output_arg