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// Generated by the Scala Plugin for the Protocol Buffer Compiler.
// Do not edit!
//
// Protofile syntax: PROTO3

package io.opentelemetry.proto.metrics.v1

/** Defines a Metric which has one or more timeseries.  The following is a
  * brief summary of the Metric data model.  For more details, see:
  *
  *   https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/datamodel.md
  *
  *
  * The data model and relation between entities is shown in the
  * diagram below. Here, "DataPoint" is the term used to refer to any
  * one of the specific data point value types, and "points" is the term used
  * to refer to any one of the lists of points contained in the Metric.
  *
  * - Metric is composed of a metadata and data.
  * - Metadata part contains a name, description, unit.
  * - Data is one of the possible types (Sum, Gauge, Histogram, Summary).
  * - DataPoint contains timestamps, attributes, and one of the possible value type
  *   fields.
  *
  *     Metric
  *  +------------+
  *  |name        |
  *  |description |
  *  |unit        |     +------------------------------------+
  *  |data        |---> |Gauge, Sum, Histogram, Summary, ... |
  *  +------------+     +------------------------------------+
  *
  *    Data [One of Gauge, Sum, Histogram, Summary, ...]
  *  +-----------+
  *  |...        |  // Metadata about the Data.
  *  |points     |--+
  *  +-----------+  |
  *                 |      +---------------------------+
  *                 |      |DataPoint 1                |
  *                 v      |+------+------+   +------+ |
  *              +-----+   ||label |label |...|label | |
  *              |  1  |-->||value1|value2|...|valueN| |
  *              +-----+   |+------+------+   +------+ |
  *              |  .  |   |+-----+                    |
  *              |  .  |   ||value|                    |
  *              |  .  |   |+-----+                    |
  *              |  .  |   +---------------------------+
  *              |  .  |                   .
  *              |  .  |                   .
  *              |  .  |                   .
  *              |  .  |   +---------------------------+
  *              |  .  |   |DataPoint M                |
  *              +-----+   |+------+------+   +------+ |
  *              |  M  |-->||label |label |...|label | |
  *              +-----+   ||value1|value2|...|valueN| |
  *                        |+------+------+   +------+ |
  *                        |+-----+                    |
  *                        ||value|                    |
  *                        |+-----+                    |
  *                        +---------------------------+
  *
  * Each distinct type of DataPoint represents the output of a specific
  * aggregation function, the result of applying the DataPoint's
  * associated function of to one or more measurements.
  *
  * All DataPoint types have three common fields:
  * - Attributes includes key-value pairs associated with the data point
  * - TimeUnixNano is required, set to the end time of the aggregation
  * - StartTimeUnixNano is optional, but strongly encouraged for DataPoints
  *   having an AggregationTemporality field, as discussed below.
  *
  * Both TimeUnixNano and StartTimeUnixNano values are expressed as
  * UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January 1970.
  *
  * # TimeUnixNano
  *
  * This field is required, having consistent interpretation across
  * DataPoint types.  TimeUnixNano is the moment corresponding to when
  * the data point's aggregate value was captured.
  *
  * Data points with the 0 value for TimeUnixNano SHOULD be rejected
  * by consumers.
  *
  * # StartTimeUnixNano
  *
  * StartTimeUnixNano in general allows detecting when a sequence of
  * observations is unbroken.  This field indicates to consumers the
  * start time for points with cumulative and delta
  * AggregationTemporality, and it should be included whenever possible
  * to support correct rate calculation.  Although it may be omitted
  * when the start time is truly unknown, setting StartTimeUnixNano is
  * strongly encouraged.
  *
  * @param name
  *   name of the metric, including its DNS name prefix. It must be unique.
  * @param description
  *   description of the metric, which can be used in documentation.
  * @param unit
  *   unit in which the metric value is reported. Follows the format
  *   described by http://unitsofmeasure.org/ucum.html.
  */
@SerialVersionUID(0L)
final case class Metric(
    name: _root_.scala.Predef.String = "",
    description: _root_.scala.Predef.String = "",
    unit: _root_.scala.Predef.String = "",
    data: io.opentelemetry.proto.metrics.v1.Metric.Data = io.opentelemetry.proto.metrics.v1.Metric.Data.Empty,
    unknownFields: _root_.scalapb.UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty
    ) extends scalapb.GeneratedMessage with scalapb.lenses.Updatable[Metric] {
    @transient
    private[this] var __serializedSizeMemoized: _root_.scala.Int = 0
    private[this] def __computeSerializedSize(): _root_.scala.Int = {
      var __size = 0
      
      {
        val __value = name
        if (!__value.isEmpty) {
          __size += _root_.com.google.protobuf.CodedOutputStream.computeStringSize(1, __value)
        }
      };
      
      {
        val __value = description
        if (!__value.isEmpty) {
          __size += _root_.com.google.protobuf.CodedOutputStream.computeStringSize(2, __value)
        }
      };
      
      {
        val __value = unit
        if (!__value.isEmpty) {
          __size += _root_.com.google.protobuf.CodedOutputStream.computeStringSize(3, __value)
        }
      };
      if (data.gauge.isDefined) {
        val __value = data.gauge.get
        __size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__value.serializedSize) + __value.serializedSize
      };
      if (data.sum.isDefined) {
        val __value = data.sum.get
        __size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__value.serializedSize) + __value.serializedSize
      };
      if (data.histogram.isDefined) {
        val __value = data.histogram.get
        __size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__value.serializedSize) + __value.serializedSize
      };
      if (data.exponentialHistogram.isDefined) {
        val __value = data.exponentialHistogram.get
        __size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__value.serializedSize) + __value.serializedSize
      };
      if (data.summary.isDefined) {
        val __value = data.summary.get
        __size += 1 + _root_.com.google.protobuf.CodedOutputStream.computeUInt32SizeNoTag(__value.serializedSize) + __value.serializedSize
      };
      __size += unknownFields.serializedSize
      __size
    }
    override def serializedSize: _root_.scala.Int = {
      var __size = __serializedSizeMemoized
      if (__size == 0) {
        __size = __computeSerializedSize() + 1
        __serializedSizeMemoized = __size
      }
      __size - 1
      
    }
    def writeTo(`_output__`: _root_.com.google.protobuf.CodedOutputStream): _root_.scala.Unit = {
      {
        val __v = name
        if (!__v.isEmpty) {
          _output__.writeString(1, __v)
        }
      };
      {
        val __v = description
        if (!__v.isEmpty) {
          _output__.writeString(2, __v)
        }
      };
      {
        val __v = unit
        if (!__v.isEmpty) {
          _output__.writeString(3, __v)
        }
      };
      data.gauge.foreach { __v =>
        val __m = __v
        _output__.writeTag(5, 2)
        _output__.writeUInt32NoTag(__m.serializedSize)
        __m.writeTo(_output__)
      };
      data.sum.foreach { __v =>
        val __m = __v
        _output__.writeTag(7, 2)
        _output__.writeUInt32NoTag(__m.serializedSize)
        __m.writeTo(_output__)
      };
      data.histogram.foreach { __v =>
        val __m = __v
        _output__.writeTag(9, 2)
        _output__.writeUInt32NoTag(__m.serializedSize)
        __m.writeTo(_output__)
      };
      data.exponentialHistogram.foreach { __v =>
        val __m = __v
        _output__.writeTag(10, 2)
        _output__.writeUInt32NoTag(__m.serializedSize)
        __m.writeTo(_output__)
      };
      data.summary.foreach { __v =>
        val __m = __v
        _output__.writeTag(11, 2)
        _output__.writeUInt32NoTag(__m.serializedSize)
        __m.writeTo(_output__)
      };
      unknownFields.writeTo(_output__)
    }
    def withName(__v: _root_.scala.Predef.String): Metric = copy(name = __v)
    def withDescription(__v: _root_.scala.Predef.String): Metric = copy(description = __v)
    def withUnit(__v: _root_.scala.Predef.String): Metric = copy(unit = __v)
    def getGauge: io.opentelemetry.proto.metrics.v1.Gauge = data.gauge.getOrElse(io.opentelemetry.proto.metrics.v1.Gauge.defaultInstance)
    def withGauge(__v: io.opentelemetry.proto.metrics.v1.Gauge): Metric = copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.Gauge(__v))
    def getSum: io.opentelemetry.proto.metrics.v1.Sum = data.sum.getOrElse(io.opentelemetry.proto.metrics.v1.Sum.defaultInstance)
    def withSum(__v: io.opentelemetry.proto.metrics.v1.Sum): Metric = copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.Sum(__v))
    def getHistogram: io.opentelemetry.proto.metrics.v1.Histogram = data.histogram.getOrElse(io.opentelemetry.proto.metrics.v1.Histogram.defaultInstance)
    def withHistogram(__v: io.opentelemetry.proto.metrics.v1.Histogram): Metric = copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.Histogram(__v))
    def getExponentialHistogram: io.opentelemetry.proto.metrics.v1.ExponentialHistogram = data.exponentialHistogram.getOrElse(io.opentelemetry.proto.metrics.v1.ExponentialHistogram.defaultInstance)
    def withExponentialHistogram(__v: io.opentelemetry.proto.metrics.v1.ExponentialHistogram): Metric = copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.ExponentialHistogram(__v))
    def getSummary: io.opentelemetry.proto.metrics.v1.Summary = data.summary.getOrElse(io.opentelemetry.proto.metrics.v1.Summary.defaultInstance)
    def withSummary(__v: io.opentelemetry.proto.metrics.v1.Summary): Metric = copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.Summary(__v))
    def clearData: Metric = copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.Empty)
    def withData(__v: io.opentelemetry.proto.metrics.v1.Metric.Data): Metric = copy(data = __v)
    def withUnknownFields(__v: _root_.scalapb.UnknownFieldSet) = copy(unknownFields = __v)
    def discardUnknownFields = copy(unknownFields = _root_.scalapb.UnknownFieldSet.empty)
    def getFieldByNumber(__fieldNumber: _root_.scala.Int): _root_.scala.Any = {
      (__fieldNumber: @_root_.scala.unchecked) match {
        case 1 => {
          val __t = name
          if (__t != "") __t else null
        }
        case 2 => {
          val __t = description
          if (__t != "") __t else null
        }
        case 3 => {
          val __t = unit
          if (__t != "") __t else null
        }
        case 5 => data.gauge.orNull
        case 7 => data.sum.orNull
        case 9 => data.histogram.orNull
        case 10 => data.exponentialHistogram.orNull
        case 11 => data.summary.orNull
      }
    }
    def getField(__field: _root_.scalapb.descriptors.FieldDescriptor): _root_.scalapb.descriptors.PValue = {
      _root_.scala.Predef.require(__field.containingMessage eq companion.scalaDescriptor)
      (__field.number: @_root_.scala.unchecked) match {
        case 1 => _root_.scalapb.descriptors.PString(name)
        case 2 => _root_.scalapb.descriptors.PString(description)
        case 3 => _root_.scalapb.descriptors.PString(unit)
        case 5 => data.gauge.map(_.toPMessage).getOrElse(_root_.scalapb.descriptors.PEmpty)
        case 7 => data.sum.map(_.toPMessage).getOrElse(_root_.scalapb.descriptors.PEmpty)
        case 9 => data.histogram.map(_.toPMessage).getOrElse(_root_.scalapb.descriptors.PEmpty)
        case 10 => data.exponentialHistogram.map(_.toPMessage).getOrElse(_root_.scalapb.descriptors.PEmpty)
        case 11 => data.summary.map(_.toPMessage).getOrElse(_root_.scalapb.descriptors.PEmpty)
      }
    }
    def toProtoString: _root_.scala.Predef.String = _root_.scalapb.TextFormat.printToUnicodeString(this)
    def companion: io.opentelemetry.proto.metrics.v1.Metric.type = io.opentelemetry.proto.metrics.v1.Metric
    // @@protoc_insertion_point(GeneratedMessage[opentelemetry.proto.metrics.v1.Metric])
}

object Metric extends scalapb.GeneratedMessageCompanion[io.opentelemetry.proto.metrics.v1.Metric] {
  implicit def messageCompanion: scalapb.GeneratedMessageCompanion[io.opentelemetry.proto.metrics.v1.Metric] = this
  def parseFrom(`_input__`: _root_.com.google.protobuf.CodedInputStream): io.opentelemetry.proto.metrics.v1.Metric = {
    var __name: _root_.scala.Predef.String = ""
    var __description: _root_.scala.Predef.String = ""
    var __unit: _root_.scala.Predef.String = ""
    var __data: io.opentelemetry.proto.metrics.v1.Metric.Data = io.opentelemetry.proto.metrics.v1.Metric.Data.Empty
    var `_unknownFields__`: _root_.scalapb.UnknownFieldSet.Builder = null
    var _done__ = false
    while (!_done__) {
      val _tag__ = _input__.readTag()
      _tag__ match {
        case 0 => _done__ = true
        case 10 =>
          __name = _input__.readStringRequireUtf8()
        case 18 =>
          __description = _input__.readStringRequireUtf8()
        case 26 =>
          __unit = _input__.readStringRequireUtf8()
        case 42 =>
          __data = io.opentelemetry.proto.metrics.v1.Metric.Data.Gauge(__data.gauge.fold(_root_.scalapb.LiteParser.readMessage[io.opentelemetry.proto.metrics.v1.Gauge](_input__))(_root_.scalapb.LiteParser.readMessage(_input__, _)))
        case 58 =>
          __data = io.opentelemetry.proto.metrics.v1.Metric.Data.Sum(__data.sum.fold(_root_.scalapb.LiteParser.readMessage[io.opentelemetry.proto.metrics.v1.Sum](_input__))(_root_.scalapb.LiteParser.readMessage(_input__, _)))
        case 74 =>
          __data = io.opentelemetry.proto.metrics.v1.Metric.Data.Histogram(__data.histogram.fold(_root_.scalapb.LiteParser.readMessage[io.opentelemetry.proto.metrics.v1.Histogram](_input__))(_root_.scalapb.LiteParser.readMessage(_input__, _)))
        case 82 =>
          __data = io.opentelemetry.proto.metrics.v1.Metric.Data.ExponentialHistogram(__data.exponentialHistogram.fold(_root_.scalapb.LiteParser.readMessage[io.opentelemetry.proto.metrics.v1.ExponentialHistogram](_input__))(_root_.scalapb.LiteParser.readMessage(_input__, _)))
        case 90 =>
          __data = io.opentelemetry.proto.metrics.v1.Metric.Data.Summary(__data.summary.fold(_root_.scalapb.LiteParser.readMessage[io.opentelemetry.proto.metrics.v1.Summary](_input__))(_root_.scalapb.LiteParser.readMessage(_input__, _)))
        case tag =>
          if (_unknownFields__ == null) {
            _unknownFields__ = new _root_.scalapb.UnknownFieldSet.Builder()
          }
          _unknownFields__.parseField(tag, _input__)
      }
    }
    io.opentelemetry.proto.metrics.v1.Metric(
        name = __name,
        description = __description,
        unit = __unit,
        data = __data,
        unknownFields = if (_unknownFields__ == null) _root_.scalapb.UnknownFieldSet.empty else _unknownFields__.result()
    )
  }
  implicit def messageReads: _root_.scalapb.descriptors.Reads[io.opentelemetry.proto.metrics.v1.Metric] = _root_.scalapb.descriptors.Reads{
    case _root_.scalapb.descriptors.PMessage(__fieldsMap) =>
      _root_.scala.Predef.require(__fieldsMap.keys.forall(_.containingMessage eq scalaDescriptor), "FieldDescriptor does not match message type.")
      io.opentelemetry.proto.metrics.v1.Metric(
        name = __fieldsMap.get(scalaDescriptor.findFieldByNumber(1).get).map(_.as[_root_.scala.Predef.String]).getOrElse(""),
        description = __fieldsMap.get(scalaDescriptor.findFieldByNumber(2).get).map(_.as[_root_.scala.Predef.String]).getOrElse(""),
        unit = __fieldsMap.get(scalaDescriptor.findFieldByNumber(3).get).map(_.as[_root_.scala.Predef.String]).getOrElse(""),
        data = __fieldsMap.get(scalaDescriptor.findFieldByNumber(5).get).flatMap(_.as[_root_.scala.Option[io.opentelemetry.proto.metrics.v1.Gauge]]).map(io.opentelemetry.proto.metrics.v1.Metric.Data.Gauge(_))
            .orElse[io.opentelemetry.proto.metrics.v1.Metric.Data](__fieldsMap.get(scalaDescriptor.findFieldByNumber(7).get).flatMap(_.as[_root_.scala.Option[io.opentelemetry.proto.metrics.v1.Sum]]).map(io.opentelemetry.proto.metrics.v1.Metric.Data.Sum(_)))
            .orElse[io.opentelemetry.proto.metrics.v1.Metric.Data](__fieldsMap.get(scalaDescriptor.findFieldByNumber(9).get).flatMap(_.as[_root_.scala.Option[io.opentelemetry.proto.metrics.v1.Histogram]]).map(io.opentelemetry.proto.metrics.v1.Metric.Data.Histogram(_)))
            .orElse[io.opentelemetry.proto.metrics.v1.Metric.Data](__fieldsMap.get(scalaDescriptor.findFieldByNumber(10).get).flatMap(_.as[_root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogram]]).map(io.opentelemetry.proto.metrics.v1.Metric.Data.ExponentialHistogram(_)))
            .orElse[io.opentelemetry.proto.metrics.v1.Metric.Data](__fieldsMap.get(scalaDescriptor.findFieldByNumber(11).get).flatMap(_.as[_root_.scala.Option[io.opentelemetry.proto.metrics.v1.Summary]]).map(io.opentelemetry.proto.metrics.v1.Metric.Data.Summary(_)))
            .getOrElse(io.opentelemetry.proto.metrics.v1.Metric.Data.Empty)
      )
    case _ => throw new RuntimeException("Expected PMessage")
  }
  def javaDescriptor: _root_.com.google.protobuf.Descriptors.Descriptor = MetricsProto.javaDescriptor.getMessageTypes().get(4)
  def scalaDescriptor: _root_.scalapb.descriptors.Descriptor = MetricsProto.scalaDescriptor.messages(4)
  def messageCompanionForFieldNumber(__number: _root_.scala.Int): _root_.scalapb.GeneratedMessageCompanion[_] = {
    var __out: _root_.scalapb.GeneratedMessageCompanion[_] = null
    (__number: @_root_.scala.unchecked) match {
      case 5 => __out = io.opentelemetry.proto.metrics.v1.Gauge
      case 7 => __out = io.opentelemetry.proto.metrics.v1.Sum
      case 9 => __out = io.opentelemetry.proto.metrics.v1.Histogram
      case 10 => __out = io.opentelemetry.proto.metrics.v1.ExponentialHistogram
      case 11 => __out = io.opentelemetry.proto.metrics.v1.Summary
    }
    __out
  }
  lazy val nestedMessagesCompanions: Seq[_root_.scalapb.GeneratedMessageCompanion[_ <: _root_.scalapb.GeneratedMessage]] = Seq.empty
  def enumCompanionForFieldNumber(__fieldNumber: _root_.scala.Int): _root_.scalapb.GeneratedEnumCompanion[_] = throw new MatchError(__fieldNumber)
  lazy val defaultInstance = io.opentelemetry.proto.metrics.v1.Metric(
    name = "",
    description = "",
    unit = "",
    data = io.opentelemetry.proto.metrics.v1.Metric.Data.Empty
  )
  sealed trait Data extends _root_.scalapb.GeneratedOneof {
    def isEmpty: _root_.scala.Boolean = false
    def isDefined: _root_.scala.Boolean = true
    def isGauge: _root_.scala.Boolean = false
    def isSum: _root_.scala.Boolean = false
    def isHistogram: _root_.scala.Boolean = false
    def isExponentialHistogram: _root_.scala.Boolean = false
    def isSummary: _root_.scala.Boolean = false
    def gauge: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.Gauge] = _root_.scala.None
    def sum: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.Sum] = _root_.scala.None
    def histogram: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.Histogram] = _root_.scala.None
    def exponentialHistogram: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogram] = _root_.scala.None
    def summary: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.Summary] = _root_.scala.None
  }
  object Data {
    @SerialVersionUID(0L)
    case object Empty extends io.opentelemetry.proto.metrics.v1.Metric.Data {
      type ValueType = _root_.scala.Nothing
      override def isEmpty: _root_.scala.Boolean = true
      override def isDefined: _root_.scala.Boolean = false
      override def number: _root_.scala.Int = 0
      override def value: _root_.scala.Nothing = throw new java.util.NoSuchElementException("Empty.value")
    }
  
    @SerialVersionUID(0L)
    final case class Gauge(value: io.opentelemetry.proto.metrics.v1.Gauge) extends io.opentelemetry.proto.metrics.v1.Metric.Data {
      type ValueType = io.opentelemetry.proto.metrics.v1.Gauge
      override def isGauge: _root_.scala.Boolean = true
      override def gauge: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.Gauge] = Some(value)
      override def number: _root_.scala.Int = 5
    }
    @SerialVersionUID(0L)
    final case class Sum(value: io.opentelemetry.proto.metrics.v1.Sum) extends io.opentelemetry.proto.metrics.v1.Metric.Data {
      type ValueType = io.opentelemetry.proto.metrics.v1.Sum
      override def isSum: _root_.scala.Boolean = true
      override def sum: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.Sum] = Some(value)
      override def number: _root_.scala.Int = 7
    }
    @SerialVersionUID(0L)
    final case class Histogram(value: io.opentelemetry.proto.metrics.v1.Histogram) extends io.opentelemetry.proto.metrics.v1.Metric.Data {
      type ValueType = io.opentelemetry.proto.metrics.v1.Histogram
      override def isHistogram: _root_.scala.Boolean = true
      override def histogram: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.Histogram] = Some(value)
      override def number: _root_.scala.Int = 9
    }
    @SerialVersionUID(0L)
    final case class ExponentialHistogram(value: io.opentelemetry.proto.metrics.v1.ExponentialHistogram) extends io.opentelemetry.proto.metrics.v1.Metric.Data {
      type ValueType = io.opentelemetry.proto.metrics.v1.ExponentialHistogram
      override def isExponentialHistogram: _root_.scala.Boolean = true
      override def exponentialHistogram: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.ExponentialHistogram] = Some(value)
      override def number: _root_.scala.Int = 10
    }
    @SerialVersionUID(0L)
    final case class Summary(value: io.opentelemetry.proto.metrics.v1.Summary) extends io.opentelemetry.proto.metrics.v1.Metric.Data {
      type ValueType = io.opentelemetry.proto.metrics.v1.Summary
      override def isSummary: _root_.scala.Boolean = true
      override def summary: _root_.scala.Option[io.opentelemetry.proto.metrics.v1.Summary] = Some(value)
      override def number: _root_.scala.Int = 11
    }
  }
  implicit class MetricLens[UpperPB](_l: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.Metric]) extends _root_.scalapb.lenses.ObjectLens[UpperPB, io.opentelemetry.proto.metrics.v1.Metric](_l) {
    def name: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Predef.String] = field(_.name)((c_, f_) => c_.copy(name = f_))
    def description: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Predef.String] = field(_.description)((c_, f_) => c_.copy(description = f_))
    def unit: _root_.scalapb.lenses.Lens[UpperPB, _root_.scala.Predef.String] = field(_.unit)((c_, f_) => c_.copy(unit = f_))
    def gauge: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.Gauge] = field(_.getGauge)((c_, f_) => c_.copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.Gauge(f_)))
    def sum: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.Sum] = field(_.getSum)((c_, f_) => c_.copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.Sum(f_)))
    def histogram: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.Histogram] = field(_.getHistogram)((c_, f_) => c_.copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.Histogram(f_)))
    def exponentialHistogram: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.ExponentialHistogram] = field(_.getExponentialHistogram)((c_, f_) => c_.copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.ExponentialHistogram(f_)))
    def summary: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.Summary] = field(_.getSummary)((c_, f_) => c_.copy(data = io.opentelemetry.proto.metrics.v1.Metric.Data.Summary(f_)))
    def data: _root_.scalapb.lenses.Lens[UpperPB, io.opentelemetry.proto.metrics.v1.Metric.Data] = field(_.data)((c_, f_) => c_.copy(data = f_))
  }
  final val NAME_FIELD_NUMBER = 1
  final val DESCRIPTION_FIELD_NUMBER = 2
  final val UNIT_FIELD_NUMBER = 3
  final val GAUGE_FIELD_NUMBER = 5
  final val SUM_FIELD_NUMBER = 7
  final val HISTOGRAM_FIELD_NUMBER = 9
  final val EXPONENTIAL_HISTOGRAM_FIELD_NUMBER = 10
  final val SUMMARY_FIELD_NUMBER = 11
  def of(
    name: _root_.scala.Predef.String,
    description: _root_.scala.Predef.String,
    unit: _root_.scala.Predef.String,
    data: io.opentelemetry.proto.metrics.v1.Metric.Data
  ): _root_.io.opentelemetry.proto.metrics.v1.Metric = _root_.io.opentelemetry.proto.metrics.v1.Metric(
    name,
    description,
    unit,
    data
  )
  // @@protoc_insertion_point(GeneratedMessageCompanion[opentelemetry.proto.metrics.v1.Metric])
}




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