# 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.
# ==============================================================================
"""Serves as a common "main" function for all the SavedModel tests.

There is a fair amount of setup needed to initialize tensorflow and get it
into a proper TF2 execution mode. This hides that boilerplate.
"""

import tempfile

from absl import app
from absl import flags
from absl import logging
import tensorflow.compat.v2 as tf

from tensorflow.python import pywrap_mlir  # pylint: disable=g-direct-tensorflow-import

# Use /tmp to make debugging the tests easier (see README.md)
flags.DEFINE_string('save_model_path', '',
                    'Path to save the model to.')
FLAGS = flags.FLAGS


# This function needs to take a "create_module_fn", as opposed to just the
# module itself, because the creation of the module has to be delayed until
# after absl and tensorflow have run various initialization steps.
def do_test(create_module_fn, exported_names=None, show_debug_info=False):
  """Runs test.

  1. Performs absl and tf "main"-like initialization that must run before almost
     anything else.
  2. Converts `tf.Module` to SavedModel
  3. Converts SavedModel to MLIR
  4. Prints the textual MLIR to stdout (it is expected that the caller will have
     FileCheck checks in its file to check this output).

  This is only for use by the MLIR SavedModel importer tests.

  Args:
    create_module_fn: A callable taking no arguments, which returns the
      `tf.Module` to be converted and printed.
    exported_names: A set of exported names for the MLIR converter (default is
      "export all").
    show_debug_info: If true, shows debug locations in the resulting MLIR.
  """
  if exported_names is None:
    exported_names = []

  # Make LOG(ERROR) in C++ code show up on the console.
  # All `Status` passed around in the C++ API seem to eventually go into
  # `LOG(ERROR)`, so this makes them print out by default.
  logging.set_stderrthreshold('error')

  # In true TF2 releases, v2 behavior is enabled as part of module __init__. In
  # TF1 builds, it must be enabled manually. If you get an error here,
  # it means that TF was used in V1 mode prior to calling this.
  tf.enable_v2_behavior()

  def app_main(argv):
    """Function passed to absl.app.run."""
    if len(argv) > 1:
      raise app.UsageError('Too many command-line arguments.')
    if FLAGS.save_model_path:
      save_model_path = FLAGS.save_model_path
    else:
      save_model_path = tempfile.mkdtemp(suffix='.saved_model')
    save_options = tf.saved_model.SaveOptions(save_debug_info=show_debug_info)
    tf.saved_model.save(
        create_module_fn(), save_model_path, options=save_options)
    logging.info('Saved model to: %s', save_model_path)
    mlir = pywrap_mlir.experimental_convert_saved_model_to_mlir(
        save_model_path, ','.join(exported_names), show_debug_info)
    # We don't strictly need this, but it serves as a handy sanity check
    # for that API, which is otherwise a bit annoying to test.
    # The canonicalization shouldn't affect these tests in any way.
    mlir = pywrap_mlir.experimental_run_pass_pipeline(mlir, 'canonicalize',
                                                      show_debug_info)
    print(mlir)

  app.run(app_main)
