# Copyright 2019, The TensorFlow Authors.
#
# 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.
"""Utility methods for testing private queries.

Utility methods for testing private queries.
"""

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


def run_query(query, records, global_state=None, weights=None):
  """Executes query on the given set of records as a single sample.

  Args:
    query: A PrivateQuery to run.
    records: An iterable containing records to pass to the query.
    global_state: The current global state. If None, an initial global state is
      generated.
    weights: An optional iterable containing the weights of the records.

  Returns:
    A tuple (result, new_global_state) where "result" is the result of the
      query and "new_global_state" is the updated global state.
  """
  if not global_state:
    global_state = query.initial_global_state()
  params = query.derive_sample_params(global_state)
  sample_state = query.initial_sample_state(next(iter(records)))
  if weights is None:
    for record in records:
      sample_state = query.accumulate_record(params, sample_state, record)
  else:
    for weight, record in zip(weights, records):
      sample_state = query.accumulate_record(params, sample_state, record,
                                             weight)
  result, global_state, _ = query.get_noised_result(sample_state, global_state)
  return result, global_state
