// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: google/cloud/automl/v1beta1/prediction_service.proto

package com.google.cloud.automl.v1beta1;

/**
 *
 *
 * <pre>
 * Request message for [PredictionService.Predict][google.cloud.automl.v1beta1.PredictionService.Predict].
 * </pre>
 *
 * Protobuf type {@code google.cloud.automl.v1beta1.PredictRequest}
 */
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  /**
   *
   *
   * <pre>
   * Name of the model requested to serve the prediction.
   * </pre>
   *
   * <code>string name = 1;</code>
   */
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   *
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  /**
   *
   *
   * <pre>
   * Required.
   * Payload to perform a prediction on. The payload must match the
   * problem type that the model was trained to solve.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
   */
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  /**
   *
   *
   * <pre>
   * Required.
   * Payload to perform a prediction on. The payload must match the
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   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
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  /**
   *
   *
   * <pre>
   * Required.
   * Payload to perform a prediction on. The payload must match the
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   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
   */
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   *
   *
   * <pre>
   * Additional domain-specific parameters, any string must be up to 25000
   * characters long.
   * *  For Image Classification:
   *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
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   *  *  For Image Object Detection:
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   *        confidence score. Value in 0 to 1 range, default is 0.5.
   *    `max_bounding_box_count` - (int64) No more than this number of bounding
   *        boxes will be returned in the response. Default is 100, the
   *        requested value may be limited by server.
   * </pre>
   *
   * <code>map&lt;string, string&gt; params = 3;</code>
   */
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   *
   *
   * <pre>
   * Additional domain-specific parameters, any string must be up to 25000
   * characters long.
   * *  For Image Classification:
   *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
   *     makes predictions for an image, it will only produce results that have
   *     at least this confidence score. The default is 0.5.
   *  *  For Image Object Detection:
   *    `score_threshold` - (float) When Model detects objects on the image,
   *        it will only produce bounding boxes which have at least this
   *        confidence score. Value in 0 to 1 range, default is 0.5.
   *    `max_bounding_box_count` - (int64) No more than this number of bounding
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   *        requested value may be limited by server.
   * </pre>
   *
   * <code>map&lt;string, string&gt; params = 3;</code>
   */
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  /**
   *
   *
   * <pre>
   * Additional domain-specific parameters, any string must be up to 25000
   * characters long.
   * *  For Image Classification:
   *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
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   *  *  For Image Object Detection:
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   *        confidence score. Value in 0 to 1 range, default is 0.5.
   *    `max_bounding_box_count` - (int64) No more than this number of bounding
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   *        requested value may be limited by server.
   * </pre>
   *
   * <code>map&lt;string, string&gt; params = 3;</code>
   */
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   *
   *
   * <pre>
   * Additional domain-specific parameters, any string must be up to 25000
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   * *  For Image Classification:
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   *     makes predictions for an image, it will only produce results that have
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   *  *  For Image Object Detection:
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   *        confidence score. Value in 0 to 1 range, default is 0.5.
   *    `max_bounding_box_count` - (int64) No more than this number of bounding
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   *
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   *
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    public final boolean isInitialized() {
      return true;
    }

    @java.lang.Override
    public Builder mergeFrom(
        com.google.protobuf.CodedInputStream input,
        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
        throws java.io.IOException {
      com.google.cloud.automl.v1beta1.PredictRequest parsedMessage = null;
      try {
        parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
      } catch (com.google.protobuf.InvalidProtocolBufferException e) {
        parsedMessage = (com.google.cloud.automl.v1beta1.PredictRequest) e.getUnfinishedMessage();
        throw e.unwrapIOException();
      } finally {
        if (parsedMessage != null) {
          mergeFrom(parsedMessage);
        }
      }
      return this;
    }

    private int bitField0_;

    private java.lang.Object name_ = "";
    /**
     *
     *
     * <pre>
     * Name of the model requested to serve the prediction.
     * </pre>
     *
     * <code>string name = 1;</code>
     */
    public java.lang.String getName() {
      java.lang.Object ref = name_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        name_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Name of the model requested to serve the prediction.
     * </pre>
     *
     * <code>string name = 1;</code>
     */
    public com.google.protobuf.ByteString getNameBytes() {
      java.lang.Object ref = name_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        name_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Name of the model requested to serve the prediction.
     * </pre>
     *
     * <code>string name = 1;</code>
     */
    public Builder setName(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }

      name_ = value;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Name of the model requested to serve the prediction.
     * </pre>
     *
     * <code>string name = 1;</code>
     */
    public Builder clearName() {

      name_ = getDefaultInstance().getName();
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Name of the model requested to serve the prediction.
     * </pre>
     *
     * <code>string name = 1;</code>
     */
    public Builder setNameBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);

      name_ = value;
      onChanged();
      return this;
    }

    private com.google.cloud.automl.v1beta1.ExamplePayload payload_;
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.ExamplePayload,
            com.google.cloud.automl.v1beta1.ExamplePayload.Builder,
            com.google.cloud.automl.v1beta1.ExamplePayloadOrBuilder>
        payloadBuilder_;
    /**
     *
     *
     * <pre>
     * Required.
     * Payload to perform a prediction on. The payload must match the
     * problem type that the model was trained to solve.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
     */
    public boolean hasPayload() {
      return payloadBuilder_ != null || payload_ != null;
    }
    /**
     *
     *
     * <pre>
     * Required.
     * Payload to perform a prediction on. The payload must match the
     * problem type that the model was trained to solve.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
     */
    public com.google.cloud.automl.v1beta1.ExamplePayload getPayload() {
      if (payloadBuilder_ == null) {
        return payload_ == null
            ? com.google.cloud.automl.v1beta1.ExamplePayload.getDefaultInstance()
            : payload_;
      } else {
        return payloadBuilder_.getMessage();
      }
    }
    /**
     *
     *
     * <pre>
     * Required.
     * Payload to perform a prediction on. The payload must match the
     * problem type that the model was trained to solve.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
     */
    public Builder setPayload(com.google.cloud.automl.v1beta1.ExamplePayload value) {
      if (payloadBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        payload_ = value;
        onChanged();
      } else {
        payloadBuilder_.setMessage(value);
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * Required.
     * Payload to perform a prediction on. The payload must match the
     * problem type that the model was trained to solve.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
     */
    public Builder setPayload(
        com.google.cloud.automl.v1beta1.ExamplePayload.Builder builderForValue) {
      if (payloadBuilder_ == null) {
        payload_ = builderForValue.build();
        onChanged();
      } else {
        payloadBuilder_.setMessage(builderForValue.build());
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * Required.
     * Payload to perform a prediction on. The payload must match the
     * problem type that the model was trained to solve.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
     */
    public Builder mergePayload(com.google.cloud.automl.v1beta1.ExamplePayload value) {
      if (payloadBuilder_ == null) {
        if (payload_ != null) {
          payload_ =
              com.google.cloud.automl.v1beta1.ExamplePayload.newBuilder(payload_)
                  .mergeFrom(value)
                  .buildPartial();
        } else {
          payload_ = value;
        }
        onChanged();
      } else {
        payloadBuilder_.mergeFrom(value);
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * Required.
     * Payload to perform a prediction on. The payload must match the
     * problem type that the model was trained to solve.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
     */
    public Builder clearPayload() {
      if (payloadBuilder_ == null) {
        payload_ = null;
        onChanged();
      } else {
        payload_ = null;
        payloadBuilder_ = null;
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * Required.
     * Payload to perform a prediction on. The payload must match the
     * problem type that the model was trained to solve.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
     */
    public com.google.cloud.automl.v1beta1.ExamplePayload.Builder getPayloadBuilder() {

      onChanged();
      return getPayloadFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * Required.
     * Payload to perform a prediction on. The payload must match the
     * problem type that the model was trained to solve.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
     */
    public com.google.cloud.automl.v1beta1.ExamplePayloadOrBuilder getPayloadOrBuilder() {
      if (payloadBuilder_ != null) {
        return payloadBuilder_.getMessageOrBuilder();
      } else {
        return payload_ == null
            ? com.google.cloud.automl.v1beta1.ExamplePayload.getDefaultInstance()
            : payload_;
      }
    }
    /**
     *
     *
     * <pre>
     * Required.
     * Payload to perform a prediction on. The payload must match the
     * problem type that the model was trained to solve.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ExamplePayload payload = 2;</code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.ExamplePayload,
            com.google.cloud.automl.v1beta1.ExamplePayload.Builder,
            com.google.cloud.automl.v1beta1.ExamplePayloadOrBuilder>
        getPayloadFieldBuilder() {
      if (payloadBuilder_ == null) {
        payloadBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.cloud.automl.v1beta1.ExamplePayload,
                com.google.cloud.automl.v1beta1.ExamplePayload.Builder,
                com.google.cloud.automl.v1beta1.ExamplePayloadOrBuilder>(
                getPayload(), getParentForChildren(), isClean());
        payload_ = null;
      }
      return payloadBuilder_;
    }

    private com.google.protobuf.MapField<java.lang.String, java.lang.String> params_;

    private com.google.protobuf.MapField<java.lang.String, java.lang.String> internalGetParams() {
      if (params_ == null) {
        return com.google.protobuf.MapField.emptyMapField(ParamsDefaultEntryHolder.defaultEntry);
      }
      return params_;
    }

    private com.google.protobuf.MapField<java.lang.String, java.lang.String>
        internalGetMutableParams() {
      onChanged();
      ;
      if (params_ == null) {
        params_ = com.google.protobuf.MapField.newMapField(ParamsDefaultEntryHolder.defaultEntry);
      }
      if (!params_.isMutable()) {
        params_ = params_.copy();
      }
      return params_;
    }

    public int getParamsCount() {
      return internalGetParams().getMap().size();
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters, any string must be up to 25000
     * characters long.
     * *  For Image Classification:
     *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
     *     makes predictions for an image, it will only produce results that have
     *     at least this confidence score. The default is 0.5.
     *  *  For Image Object Detection:
     *    `score_threshold` - (float) When Model detects objects on the image,
     *        it will only produce bounding boxes which have at least this
     *        confidence score. Value in 0 to 1 range, default is 0.5.
     *    `max_bounding_box_count` - (int64) No more than this number of bounding
     *        boxes will be returned in the response. Default is 100, the
     *        requested value may be limited by server.
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 3;</code>
     */
    public boolean containsParams(java.lang.String key) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      return internalGetParams().getMap().containsKey(key);
    }
    /** Use {@link #getParamsMap()} instead. */
    @java.lang.Deprecated
    public java.util.Map<java.lang.String, java.lang.String> getParams() {
      return getParamsMap();
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters, any string must be up to 25000
     * characters long.
     * *  For Image Classification:
     *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
     *     makes predictions for an image, it will only produce results that have
     *     at least this confidence score. The default is 0.5.
     *  *  For Image Object Detection:
     *    `score_threshold` - (float) When Model detects objects on the image,
     *        it will only produce bounding boxes which have at least this
     *        confidence score. Value in 0 to 1 range, default is 0.5.
     *    `max_bounding_box_count` - (int64) No more than this number of bounding
     *        boxes will be returned in the response. Default is 100, the
     *        requested value may be limited by server.
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 3;</code>
     */
    public java.util.Map<java.lang.String, java.lang.String> getParamsMap() {
      return internalGetParams().getMap();
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters, any string must be up to 25000
     * characters long.
     * *  For Image Classification:
     *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
     *     makes predictions for an image, it will only produce results that have
     *     at least this confidence score. The default is 0.5.
     *  *  For Image Object Detection:
     *    `score_threshold` - (float) When Model detects objects on the image,
     *        it will only produce bounding boxes which have at least this
     *        confidence score. Value in 0 to 1 range, default is 0.5.
     *    `max_bounding_box_count` - (int64) No more than this number of bounding
     *        boxes will be returned in the response. Default is 100, the
     *        requested value may be limited by server.
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 3;</code>
     */
    public java.lang.String getParamsOrDefault(
        java.lang.String key, java.lang.String defaultValue) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      java.util.Map<java.lang.String, java.lang.String> map = internalGetParams().getMap();
      return map.containsKey(key) ? map.get(key) : defaultValue;
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters, any string must be up to 25000
     * characters long.
     * *  For Image Classification:
     *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
     *     makes predictions for an image, it will only produce results that have
     *     at least this confidence score. The default is 0.5.
     *  *  For Image Object Detection:
     *    `score_threshold` - (float) When Model detects objects on the image,
     *        it will only produce bounding boxes which have at least this
     *        confidence score. Value in 0 to 1 range, default is 0.5.
     *    `max_bounding_box_count` - (int64) No more than this number of bounding
     *        boxes will be returned in the response. Default is 100, the
     *        requested value may be limited by server.
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 3;</code>
     */
    public java.lang.String getParamsOrThrow(java.lang.String key) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      java.util.Map<java.lang.String, java.lang.String> map = internalGetParams().getMap();
      if (!map.containsKey(key)) {
        throw new java.lang.IllegalArgumentException();
      }
      return map.get(key);
    }

    public Builder clearParams() {
      internalGetMutableParams().getMutableMap().clear();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters, any string must be up to 25000
     * characters long.
     * *  For Image Classification:
     *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
     *     makes predictions for an image, it will only produce results that have
     *     at least this confidence score. The default is 0.5.
     *  *  For Image Object Detection:
     *    `score_threshold` - (float) When Model detects objects on the image,
     *        it will only produce bounding boxes which have at least this
     *        confidence score. Value in 0 to 1 range, default is 0.5.
     *    `max_bounding_box_count` - (int64) No more than this number of bounding
     *        boxes will be returned in the response. Default is 100, the
     *        requested value may be limited by server.
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 3;</code>
     */
    public Builder removeParams(java.lang.String key) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      internalGetMutableParams().getMutableMap().remove(key);
      return this;
    }
    /** Use alternate mutation accessors instead. */
    @java.lang.Deprecated
    public java.util.Map<java.lang.String, java.lang.String> getMutableParams() {
      return internalGetMutableParams().getMutableMap();
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters, any string must be up to 25000
     * characters long.
     * *  For Image Classification:
     *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
     *     makes predictions for an image, it will only produce results that have
     *     at least this confidence score. The default is 0.5.
     *  *  For Image Object Detection:
     *    `score_threshold` - (float) When Model detects objects on the image,
     *        it will only produce bounding boxes which have at least this
     *        confidence score. Value in 0 to 1 range, default is 0.5.
     *    `max_bounding_box_count` - (int64) No more than this number of bounding
     *        boxes will be returned in the response. Default is 100, the
     *        requested value may be limited by server.
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 3;</code>
     */
    public Builder putParams(java.lang.String key, java.lang.String value) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      if (value == null) {
        throw new java.lang.NullPointerException();
      }
      internalGetMutableParams().getMutableMap().put(key, value);
      return this;
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters, any string must be up to 25000
     * characters long.
     * *  For Image Classification:
     *    `score_threshold` - (float) A value from 0.0 to 1.0. When the model
     *     makes predictions for an image, it will only produce results that have
     *     at least this confidence score. The default is 0.5.
     *  *  For Image Object Detection:
     *    `score_threshold` - (float) When Model detects objects on the image,
     *        it will only produce bounding boxes which have at least this
     *        confidence score. Value in 0 to 1 range, default is 0.5.
     *    `max_bounding_box_count` - (int64) No more than this number of bounding
     *        boxes will be returned in the response. Default is 100, the
     *        requested value may be limited by server.
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 3;</code>
     */
    public Builder putAllParams(java.util.Map<java.lang.String, java.lang.String> values) {
      internalGetMutableParams().getMutableMap().putAll(values);
      return this;
    }

    @java.lang.Override
    public final Builder setUnknownFields(final com.google.protobuf.UnknownFieldSet unknownFields) {
      return super.setUnknownFields(unknownFields);
    }

    @java.lang.Override
    public final Builder mergeUnknownFields(
        final com.google.protobuf.UnknownFieldSet unknownFields) {
      return super.mergeUnknownFields(unknownFields);
    }

    // @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.PredictRequest)
  }

  // @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.PredictRequest)
  private static final com.google.cloud.automl.v1beta1.PredictRequest DEFAULT_INSTANCE;

  static {
    DEFAULT_INSTANCE = new com.google.cloud.automl.v1beta1.PredictRequest();
  }

  public static com.google.cloud.automl.v1beta1.PredictRequest getDefaultInstance() {
    return DEFAULT_INSTANCE;
  }

  private static final com.google.protobuf.Parser<PredictRequest> PARSER =
      new com.google.protobuf.AbstractParser<PredictRequest>() {
        @java.lang.Override
        public PredictRequest parsePartialFrom(
            com.google.protobuf.CodedInputStream input,
            com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws com.google.protobuf.InvalidProtocolBufferException {
          return new PredictRequest(input, extensionRegistry);
        }
      };

  public static com.google.protobuf.Parser<PredictRequest> parser() {
    return PARSER;
  }

  @java.lang.Override
  public com.google.protobuf.Parser<PredictRequest> getParserForType() {
    return PARSER;
  }

  @java.lang.Override
  public com.google.cloud.automl.v1beta1.PredictRequest getDefaultInstanceForType() {
    return DEFAULT_INSTANCE;
  }
}
