# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

# RUN: %p/basic | FileCheck %s

# pylint: disable=missing-docstring,line-too-long
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow.compat.v2 as tf
from tensorflow.compiler.mlir.tensorflow.tests.tf_saved_model import common


# Verify that the tf.versions attribute exists. It is difficult to enforce
# contents, since the version numbers change over time. The conversion logic
# itself is verified in the common graphdef converter, so here just assert
# it is being invoked.
# CHECK: module
# CHECK-SAME: tf.versions
# CHECK-SAME: bad_consumers
# CHECK-SAME: min_consumer
# CHECK-SAME: producer


class TestModule(tf.Module):

  def __init__(self):
    super(TestModule, self).__init__()
    self.v42 = tf.Variable(42.0)
    self.c43 = tf.constant(43.0)

  # CHECK: "tf_saved_model.global_tensor"() {is_mutable, sym_name = "[[VAR:[a-zA-Z_0-9]+]]", tf_saved_model.exported_names = ["v42"], type = tensor<f32>, value = dense<4.200000e+01> : tensor<f32>} : () -> ()
  # CHECK: "tf_saved_model.global_tensor"() {sym_name = "[[CONST:[a-zA-Z_0-9]+]]", tf_saved_model.exported_names = [], type = tensor<f32>, value = dense<4.300000e+01> : tensor<f32>} : () -> ()
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<f32> {tf_saved_model.index_path = [0]},
  # CHECK-SAME:   %arg1: tensor<*x!tf.resource> {tf_saved_model.bound_input = @[[VAR]]},
  # CHECK-SAME:   %arg2: tensor<f32> {tf_saved_model.bound_input = @[[CONST]]}) -> (
  # CHECK-SAME:   tensor<f32> {tf_saved_model.index_path = []})
  # CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["some_function"]
  @tf.function(input_signature=[tf.TensorSpec([], tf.float32)])
  def some_function(self, x):
    return x + self.v42 + self.c43


if __name__ == '__main__':
  common.do_test(TestModule)
