# Copyright 2017 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.
# =============================================================================
"""A tool for cost analysis."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

from google.protobuf import message
from google.protobuf import text_format
from tensorflow.contrib.fused_conv.ops import gen_fused_conv2d_bias_activation_op  # pylint: disable=unused-import
from tensorflow.core.framework import graph_pb2
from tensorflow.core.protobuf import meta_graph_pb2
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.core.protobuf import saved_model_pb2
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.grappler import cost_analyzer
from tensorflow.python.grappler import tf_optimizer
from tensorflow.python.platform import app
from tensorflow.python.platform import gfile
from tensorflow.python.training import saver


def get_metagraph():
  """Constructs and returns a MetaGraphDef from the input file."""
  with gfile.GFile(FLAGS.input) as input_file:
    input_data = input_file.read()
    try:
      saved_model = saved_model_pb2.SavedModel()
      text_format.Merge(input_data, saved_model)
      meta_graph = saved_model.meta_graphs[0]
    except text_format.ParseError:
      try:
        saved_model.ParseFromString(input_data)
        meta_graph = saved_model.meta_graphs[0]
      except message.DecodeError:
        try:
          meta_graph = meta_graph_pb2.MetaGraphDef()
          text_format.Merge(input_data, meta_graph)
        except text_format.ParseError:
          try:
            meta_graph.ParseFromString(input_data)
          except message.DecodeError:
            try:
              graph_def = graph_pb2.GraphDef()
              text_format.Merge(input_data, graph_def)
            except text_format.ParseError:
              try:
                graph_def.ParseFromString(input_data)
              except message.DecodeError:
                raise ValueError("Invalid input file.")
            importer.import_graph_def(graph_def, name="")
            graph = ops.get_default_graph()
            meta_graph = saver.export_meta_graph(
                graph_def=graph.as_graph_def(), graph=graph)
  if FLAGS.fetch is not None:
    fetch_collection = meta_graph_pb2.CollectionDef()
    for fetch in FLAGS.fetch.split(","):
      fetch_collection.node_list.value.append(fetch)
    meta_graph.collection_def["train_op"].CopyFrom(fetch_collection)
  return meta_graph


def main(_):
  metagraph = get_metagraph()
  rewriter_config = rewriter_config_pb2.RewriterConfig()
  if FLAGS.rewriter_config is not None:
    text_format.Merge(FLAGS.rewriter_config, rewriter_config)
  optimized_graph = tf_optimizer.OptimizeGraph(rewriter_config, metagraph)
  metagraph.graph_def.CopyFrom(optimized_graph)

  report = cost_analyzer.GenerateCostReport(metagraph, FLAGS.per_node_report,
                                            FLAGS.verbose)
  print(report)
  if FLAGS.memory_report:
    report = cost_analyzer.GenerateMemoryReport(metagraph)
    print(report)


if __name__ == "__main__":
  parser = argparse.ArgumentParser()
  parser.add_argument(
      "--input",
      type=str,
      default=None,
      help="Input file path. Accept SavedModel, MetaGraphDef, and GraphDef in "
      "either binary or text format.")
  parser.add_argument(
      "--fetch",
      type=str,
      default=None,
      help="The names of the fetch node delimited by comma.")
  parser.add_argument(
      "--rewriter_config",
      type=str,
      default=None,
      help="Configuration for the grappler optimizers, described as a "
      "RewriterConfig protocol buffer. Usage example 1: "
      "--rewriter_config='optimize_tensor_layout: true "
      "disable_model_pruning: true'. Usage example 2: "
      "--rewriter_config='optimizers: \"constfold\" optimizers: \"layout\"'")
  parser.add_argument(
      "--per_node_report",
      action="store_true",
      help="Generate per-node report. By default the report contains stats "
      "aggregated on a per op type basis, per_node_report adds results "
      "for each individual node to the report.")
  parser.add_argument(
      "--memory_report",
      action="store_true",
      help="Generate memory usage report.")
  parser.add_argument(
      "--verbose",
      action="store_true",
      help="Generate verbose reports. By default, succinct reports are used.")
  FLAGS, unparsed = parser.parse_known_args()
  app.run(main=main, argv=[sys.argv[0]] + unparsed)