data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

    data = {
        "start_datetime": "2018-12-22T14:00:00+00:00",
        "end_datetime": "2018-12-24T15:00:00+00:00",
        "repeat_at": "15:30:00",
        "process_after": 300,
    }
    expected_response = data.copy()
    expected_response.update(
        {
            "start_process": "2018-12-22T14:05:00+00:00",
            "duration": 176_100,
            "item_id": item_id,
        }
    )

logging:
  flushFrequency: 5s
  format: text
  options:
    json:
      infoBufferSize: "0"
    text:
      infoBufferSize: "0"
  verbosity: 0
makeIPTablesUtilChains: true
maxOpenFiles: 1000000
maxPods: 110
memoryManagerPolicy: None
memorySwap: {}
memoryThrottlingFactor: 0.9
nodeLeaseDurationSeconds: 40
nodeStatusMaxImages: 50
nodeStatusReportFrequency: 5m0s
nodeStatusUpdateFrequency: 10s

package org.gradle.test.fixtures.concurrent

class Duration {
    private final long nanos

    Duration(long nanos) {
        this.nanos = nanos
    }

    long getMillis() {
        return nanos / 1000000
    }

    @Override
    String toString() {
        return "[$nanos nanos]"
    }

    model = ModelWithCustomEncoder(dt_field=datetime(2019, 1, 1, 8))
    assert jsonable_encoder(model) == {"dt_field": "2019-01-01T08:00:00+00:00"}
    subclass_model = ModelWithCustomEncoderSubclass(dt_field=datetime(2019, 1, 1, 8))
    assert jsonable_encoder(subclass_model) == {"dt_field": "2019-01-01T08:00:00+00:00"}


# TODO: remove when deprecating Pydantic v1
@needs_pydanticv1
def test_encode_custom_json_encoders_model_pydanticv1():

// because some operating systems place a limit on the number of
// distinct mapped regions per process. As of this writing:
//
//	Darwin    unlimited
//	DragonFly   1000000 (vm.max_proc_mmap)
//	FreeBSD   unlimited
//	Linux         65530 (vm.max_map_count) // TODO: query /proc/sys/vm/max_map_count?
//	NetBSD    unlimited
//	OpenBSD   unlimited
var mmapLimit int32 = 1<<31 - 1

        assertEquals("1,000,000", FessFunctions.formatNumber(1000000, "###,###"));
    }

    public void test_formatFileSize() {
        assertEquals("0", FessFunctions.formatFileSize(0));
        assertEquals("1000", FessFunctions.formatFileSize(1000));
        assertEquals("976.6K", FessFunctions.formatFileSize(1000000));
        assertEquals("953.7M", FessFunctions.formatFileSize(1000000000));