# -*- coding: utf-8 -*-
# Copyright 2024 Google LLC
#
# 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.
#
from __future__ import annotations

from typing import MutableMapping, MutableSequence

import proto  # type: ignore


__protobuf__ = proto.module(
    package="google.cloud.aiplatform.v1beta1.schema.trainingjob.definition",
    manifest={
        "AutoMlImageClassification",
        "AutoMlImageClassificationInputs",
        "AutoMlImageClassificationMetadata",
    },
)


class AutoMlImageClassification(proto.Message):
    r"""A TrainingJob that trains and uploads an AutoML Image
    Classification Model.

    Attributes:
        inputs (google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationInputs):
            The input parameters of this TrainingJob.
        metadata (google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationMetadata):
            The metadata information.
    """

    inputs: "AutoMlImageClassificationInputs" = proto.Field(
        proto.MESSAGE,
        number=1,
        message="AutoMlImageClassificationInputs",
    )
    metadata: "AutoMlImageClassificationMetadata" = proto.Field(
        proto.MESSAGE,
        number=2,
        message="AutoMlImageClassificationMetadata",
    )


class AutoMlImageClassificationInputs(proto.Message):
    r"""

    Attributes:
        model_type (google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationInputs.ModelType):

        base_model_id (str):
            The ID of the ``base`` model. If it is specified, the new
            model will be trained based on the ``base`` model.
            Otherwise, the new model will be trained from scratch. The
            ``base`` model must be in the same Project and Location as
            the new Model to train, and have the same modelType.
        budget_milli_node_hours (int):
            The training budget of creating this model, expressed in
            milli node hours i.e. 1,000 value in this field means 1 node
            hour. The actual metadata.costMilliNodeHours will be equal
            or less than this value. If further model training ceases to
            provide any improvements, it will stop without using the
            full budget and the metadata.successfulStopReason will be
            ``model-converged``. Note, node_hour = actual_hour \*
            number_of_nodes_involved. For modelType
            ``cloud``\ (default), the budget must be between 8,000 and
            800,000 milli node hours, inclusive. The default value is
            192,000 which represents one day in wall time, considering 8
            nodes are used. For model types ``mobile-tf-low-latency-1``,
            ``mobile-tf-versatile-1``, ``mobile-tf-high-accuracy-1``,
            the training budget must be between 1,000 and 100,000 milli
            node hours, inclusive. The default value is 24,000 which
            represents one day in wall time on a single node that is
            used.
        disable_early_stopping (bool):
            Use the entire training budget. This disables
            the early stopping feature. When false the early
            stopping feature is enabled, which means that
            AutoML Image Classification might stop training
            before the entire training budget has been used.
        multi_label (bool):
            If false, a single-label (multi-class) Model
            will be trained (i.e. assuming that for each
            image just up to one annotation may be
            applicable). If true, a multi-label Model will
            be trained (i.e. assuming that for each image
            multiple annotations may be applicable).
    """

    class ModelType(proto.Enum):
        r"""

        Values:
            MODEL_TYPE_UNSPECIFIED (0):
                Should not be set.
            CLOUD (1):
                A Model best tailored to be used within
                Google Cloud, and which cannot be exported.
                Default.
            MOBILE_TF_LOW_LATENCY_1 (2):
                A model that, in addition to being available
                within Google Cloud, can also be exported (see
                ModelService.ExportModel) as TensorFlow or Core
                ML model and used on a mobile or edge device
                afterwards. Expected to have low latency, but
                may have lower prediction quality than other
                mobile models.
            MOBILE_TF_VERSATILE_1 (3):
                A model that, in addition to being available
                within Google Cloud, can also be exported (see
                ModelService.ExportModel) as TensorFlow or Core
                ML model and used on a mobile or edge device
                with afterwards.
            MOBILE_TF_HIGH_ACCURACY_1 (4):
                A model that, in addition to being available
                within Google Cloud, can also be exported (see
                ModelService.ExportModel) as TensorFlow or Core
                ML model and used on a mobile or edge device
                afterwards. Expected to have a higher latency,
                but should also have a higher prediction quality
                than other mobile models.
        """
        MODEL_TYPE_UNSPECIFIED = 0
        CLOUD = 1
        MOBILE_TF_LOW_LATENCY_1 = 2
        MOBILE_TF_VERSATILE_1 = 3
        MOBILE_TF_HIGH_ACCURACY_1 = 4

    model_type: ModelType = proto.Field(
        proto.ENUM,
        number=1,
        enum=ModelType,
    )
    base_model_id: str = proto.Field(
        proto.STRING,
        number=2,
    )
    budget_milli_node_hours: int = proto.Field(
        proto.INT64,
        number=3,
    )
    disable_early_stopping: bool = proto.Field(
        proto.BOOL,
        number=4,
    )
    multi_label: bool = proto.Field(
        proto.BOOL,
        number=5,
    )


class AutoMlImageClassificationMetadata(proto.Message):
    r"""

    Attributes:
        cost_milli_node_hours (int):
            The actual training cost of creating this
            model, expressed in milli node hours, i.e. 1,000
            value in this field means 1 node hour.
            Guaranteed to not exceed
            inputs.budgetMilliNodeHours.
        successful_stop_reason (google.cloud.aiplatform.v1beta1.schema.trainingjob.definition_v1beta1.types.AutoMlImageClassificationMetadata.SuccessfulStopReason):
            For successful job completions, this is the
            reason why the job has finished.
    """

    class SuccessfulStopReason(proto.Enum):
        r"""

        Values:
            SUCCESSFUL_STOP_REASON_UNSPECIFIED (0):
                Should not be set.
            BUDGET_REACHED (1):
                The inputs.budgetMilliNodeHours had been
                reached.
            MODEL_CONVERGED (2):
                Further training of the Model ceased to
                increase its quality, since it already has
                converged.
        """
        SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0
        BUDGET_REACHED = 1
        MODEL_CONVERGED = 2

    cost_milli_node_hours: int = proto.Field(
        proto.INT64,
        number=1,
    )
    successful_stop_reason: SuccessfulStopReason = proto.Field(
        proto.ENUM,
        number=2,
        enum=SuccessfulStopReason,
    )


__all__ = tuple(sorted(__protobuf__.manifest))
