2017-06-19 21:23:58 -04:00
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[role="xpack"]
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2018-08-31 19:49:24 -04:00
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[testenv="platinum"]
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2017-04-04 18:26:39 -04:00
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[[ml-get-bucket]]
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2018-12-20 13:23:28 -05:00
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=== Get buckets API
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2017-12-14 13:52:49 -05:00
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++++
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2018-12-20 13:23:28 -05:00
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<titleabbrev>Get buckets</titleabbrev>
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2017-12-14 13:52:49 -05:00
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++++
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2017-04-04 18:26:39 -04:00
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2019-07-30 13:52:23 -04:00
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Retrieves {anomaly-job} results for one or more buckets.
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2017-04-04 18:26:39 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-get-bucket-request]]
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==== {api-request-title}
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2017-04-04 18:26:39 -04:00
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2018-12-07 15:34:11 -05:00
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`GET _ml/anomaly_detectors/<job_id>/results/buckets` +
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2017-04-04 18:26:39 -04:00
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2018-12-07 15:34:11 -05:00
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`GET _ml/anomaly_detectors/<job_id>/results/buckets/<timestamp>`
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2017-04-10 19:14:26 -04:00
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2019-06-27 16:58:42 -04:00
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[[ml-get-bucket-prereqs]]
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==== {api-prereq-title}
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* If the {es} {security-features} are enabled, you must have `monitor_ml`,
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`monitor`, `manage_ml`, or `manage` cluster privileges to use this API. You also
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need `read` index privilege on the index that stores the results. The
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`machine_learning_admin` and `machine_learning_user` roles provide these
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privileges. For more information, see
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2019-10-07 18:23:19 -04:00
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<<security-privileges>> and
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<<built-in-roles>>.
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2019-06-27 16:58:42 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-get-bucket-desc]]
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==== {api-description-title}
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2017-04-04 18:26:39 -04:00
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2018-01-15 11:44:08 -05:00
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The get buckets API presents a chronological view of the records, grouped by
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bucket.
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2017-04-10 19:14:26 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-get-bucket-path-parms]]
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==== {api-path-parms-title}
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2017-04-04 18:26:39 -04:00
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2019-07-12 11:26:31 -04:00
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`<job_id>`::
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2019-12-27 16:30:26 -05:00
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(Required, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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2017-04-04 18:26:39 -04:00
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2019-07-12 11:26:31 -04:00
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`<timestamp>`::
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(Optional, string) The timestamp of a single bucket result. If you do not
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specify this parameter, the API returns information about all buckets.
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2017-04-04 18:26:39 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-get-bucket-request-body]]
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==== {api-request-body-title}
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2017-04-04 18:26:39 -04:00
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2019-07-12 11:26:31 -04:00
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`anomaly_score`::
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(Optional, double) Returns buckets with anomaly scores greater or equal than
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this value.
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2017-04-04 18:26:39 -04:00
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2019-07-12 11:26:31 -04:00
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`desc`::
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(Optional, boolean) If true, the buckets are sorted in descending order.
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2017-05-18 11:40:54 -04:00
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2019-07-12 11:26:31 -04:00
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`end`::
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(Optional, string) Returns buckets with timestamps earlier than this time.
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2017-04-04 18:26:39 -04:00
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2019-07-12 11:26:31 -04:00
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`exclude_interim`::
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(Optional, boolean) If true, the output excludes interim results. By default,
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interim results are included.
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2017-04-24 13:46:17 -04:00
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2019-07-12 11:26:31 -04:00
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`expand`::
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(Optional, boolean) If true, the output includes anomaly records.
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2017-04-04 18:26:39 -04:00
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2019-07-12 11:26:31 -04:00
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`page`::
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2017-05-08 09:53:04 -04:00
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`from`:::
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2019-07-12 11:26:31 -04:00
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(Optional, integer) Skips the specified number of buckets.
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2017-05-08 09:53:04 -04:00
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`size`:::
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2019-07-12 11:26:31 -04:00
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(Optional, integer) Specifies the maximum number of buckets to obtain.
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2017-04-10 19:14:26 -04:00
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2019-07-12 11:26:31 -04:00
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`sort`::
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(Optional, string) Specifies the sort field for the requested buckets. By
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default, the buckets are sorted by the `timestamp` field.
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2017-05-18 11:40:54 -04:00
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2019-07-12 11:26:31 -04:00
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`start`::
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(Optional, string) Returns buckets with timestamps after this time.
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2017-04-10 19:14:26 -04:00
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2019-06-27 12:42:47 -04:00
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[[ml-get-bucket-results]]
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==== {api-response-body-title}
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2017-04-10 19:14:26 -04:00
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The API returns the following information:
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`buckets`::
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2017-04-11 22:26:18 -04:00
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(array) An array of bucket objects. For more information, see
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2017-04-10 19:14:26 -04:00
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<<ml-results-buckets,Buckets>>.
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2019-06-27 12:42:47 -04:00
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[[ml-get-bucket-example]]
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==== {api-examples-title}
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2017-04-04 18:26:39 -04:00
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2017-04-10 19:14:26 -04:00
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The following example gets bucket information for the `it-ops-kpi` job:
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2019-09-06 11:31:13 -04:00
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[source,console]
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2017-04-10 19:14:26 -04:00
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--------------------------------------------------
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2018-12-07 15:34:11 -05:00
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GET _ml/anomaly_detectors/it-ops-kpi/results/buckets
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2017-04-10 19:14:26 -04:00
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{
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"anomaly_score": 80,
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"start": "1454530200001"
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}
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--------------------------------------------------
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// TEST[skip:todo]
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In this example, the API returns a single result that matches the specified
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score and time constraints:
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2017-04-21 11:23:27 -04:00
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[source,js]
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2017-04-04 18:26:39 -04:00
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----
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{
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"count": 1,
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2017-04-10 19:14:26 -04:00
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"buckets": [
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{
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2017-04-04 18:26:39 -04:00
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"job_id": "it-ops-kpi",
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2017-04-10 19:14:26 -04:00
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"timestamp": 1454943900000,
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2017-04-11 21:52:47 -04:00
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"anomaly_score": 94.1706,
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2017-04-10 19:14:26 -04:00
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"bucket_span": 300,
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2017-04-11 21:52:47 -04:00
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"initial_anomaly_score": 94.1706,
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2017-04-10 19:14:26 -04:00
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"event_count": 153,
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"is_interim": false,
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"bucket_influencers": [
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{
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"job_id": "it-ops-kpi",
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"result_type": "bucket_influencer",
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"influencer_field_name": "bucket_time",
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2017-04-11 21:52:47 -04:00
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"initial_anomaly_score": 94.1706,
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"anomaly_score": 94.1706,
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"raw_anomaly_score": 2.32119,
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"probability": 0.00000575042,
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2017-04-10 19:14:26 -04:00
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"timestamp": 1454943900000,
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"bucket_span": 300,
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"is_interim": false
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}
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],
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2017-04-11 21:52:47 -04:00
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"processing_time_ms": 2,
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2017-04-10 19:14:26 -04:00
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"partition_scores": [],
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"result_type": "bucket"
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2017-04-04 18:26:39 -04:00
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}
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]
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}
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----
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