# Copyright 2020 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.
# ==============================================================================
r"""Randomize all weights in a tflite file."""

from absl import app
from absl import flags

from tensorflow.lite.tools import flatbuffer_utils

FLAGS = flags.FLAGS

flags.DEFINE_string('input_tflite_file', None,
                    'Full path name to the input TFLite file.')
flags.DEFINE_string('output_tflite_file', None,
                    'Full path name to the output randomized TFLite file.')
flags.DEFINE_multi_integer(
    'buffers_to_skip', [], 'Buffer indices in the TFLite model to be skipped, '
    'i.e., to be left unmodified.')
flags.DEFINE_multi_string(
    'ops_to_skip', [], 'Ops in the TFLite model to be skipped / unmodified.')
flags.DEFINE_multi_string(
    'ops_operands_to_skip',
    [],
    'Op operand indices in the TFLite model to be skipped / unmodified. It'
    ' should be specified in the format'
    ' <op_name>:<operand_index>[,<operand_index>]. For example,'
    ' TRANSPOSE_CONV:0,2 stands for skipping the TRANSPOSE_CONV operands'
    ' indexed 0 and 2',
)
flags.DEFINE_integer('random_seed', 0, 'Input to the random number generator.')

flags.mark_flag_as_required('input_tflite_file')
flags.mark_flag_as_required('output_tflite_file')


def main(_):
  buffers_to_skip = FLAGS.buffers_to_skip
  ops_to_skip = [op.upper() for op in FLAGS.ops_to_skip]
  ops_operands_to_skip = {}
  for op_operands_to_skip in FLAGS.ops_operands_to_skip:
    op_name, indices = op_operands_to_skip.split(':')
    op_name_upper = op_name.upper()
    if op_name_upper in ops_operands_to_skip:
      raise ValueError(
          'Indices for the same op must be specified only once multiple'
          f' specification for op {op_name}.'
      )
    ops_operands_to_skip[op_name_upper] = list(map(int, indices.split(',')))

  model = flatbuffer_utils.read_model(FLAGS.input_tflite_file)

  # Add in buffers for ops in ops_to_skip or ops_operands_to_skip to the list of
  # skipped buffers.
  for graph in model.subgraphs:
    for op in graph.operators:
      op_name = flatbuffer_utils.opcode_to_name(model, op.opcodeIndex)
      op_name_upper = op_name.upper()
      if op_name_upper in ops_to_skip:
        for input_idx in op.inputs:
          buffers_to_skip.append(graph.tensors[input_idx].buffer)
      if op_name_upper in ops_operands_to_skip:
        for operand_idx in ops_operands_to_skip[op_name_upper]:
          buffers_to_skip.append(graph.tensors[op.inputs[operand_idx]].buffer)

  flatbuffer_utils.randomize_weights(model, FLAGS.random_seed,
                                     FLAGS.buffers_to_skip)
  flatbuffer_utils.write_model(model, FLAGS.output_tflite_file)


if __name__ == '__main__':
  app.run(main)