# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Optional

from google_cloud_pipeline_components import _image
from google_cloud_pipeline_components.types.artifact_types import VertexDataset
from google_cloud_pipeline_components.types.artifact_types import VertexModel
from kfp import dsl
from kfp.dsl import Input
from kfp.dsl import Output


@dsl.container_component
def automl_image_training_job(
    project: str,
    display_name: str,
    dataset: Input[VertexDataset],
    model: Output[VertexModel],
    location: Optional[str] = 'us-central1',
    prediction_type: Optional[str] = 'classification',
    multi_label: Optional[bool] = False,
    model_type: Optional[str] = 'CLOUD',
    base_model: Optional[Input[VertexModel]] = None,
    labels: Optional[dict] = {},
    training_encryption_spec_key_name: Optional[str] = None,
    model_encryption_spec_key_name: Optional[str] = None,
    training_fraction_split: Optional[float] = None,
    validation_fraction_split: Optional[float] = None,
    test_fraction_split: Optional[float] = None,
    training_filter_split: Optional[str] = None,
    validation_filter_split: Optional[str] = None,
    test_filter_split: Optional[str] = None,
    budget_milli_node_hours: Optional[int] = None,
    model_display_name: Optional[str] = None,
    model_labels: Optional[dict] = None,
    disable_early_stopping: Optional[bool] = False,
):
  # fmt: off
  """Runs the AutoML Image training job and returns a model.

  If training on a Vertex AI dataset, you can use one of the following split configurations:

  Data fraction splits:
  Any of ``training_fraction_split``, ``validation_fraction_split`` and
  ``test_fraction_split`` may optionally be provided, they must sum to up to 1. If
  the provided ones sum to less than 1, the remainder is assigned to sets as
  decided by Vertex AI. If none of the fractions are set, by default roughly 80%
  of data will be used for training, 10% for validation, and 10% for test.
  Data filter splits:
  Assigns input data to training, validation, and test sets
  based on the given filters, data pieces not matched by any
  filter are ignored. Currently only supported for Datasets
  containing DataItems.
  If any of the filters in this message are to match nothing, then
  they can be set as '-' (the minus sign).
  If using filter splits, all of ``training_filter_split``, ``validation_filter_split`` and
  ``test_filter_split`` must be provided.

  Supported only for unstructured Datasets.

  Args:
      dataset: The dataset within the same Project from which data will be used to train the Model. The
          Dataset must use schema compatible with Model being trained,
          and what is compatible should be described in the used
          TrainingPipeline's [training_task_definition]
          [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].
          For tabular Datasets, all their data is exported to
          training, to pick and choose from.
      training_fraction_split: The fraction of the input data that is to be used to train
          the Model. This is ignored if Dataset is not provided.
      validation_fraction_split: The fraction of the input data that is to be used to validate
          the Model. This is ignored if Dataset is not provided.
      test_fraction_split: The fraction of the input data that is to be used to evaluate
          the Model. This is ignored if Dataset is not provided.
      training_filter_split: A filter on DataItems of the Dataset. DataItems that match
          this filter are used to train the Model. A filter with same syntax
          as the one used in DatasetService.ListDataItems may be used. If a
          single DataItem is matched by more than one of the FilterSplit filters,
          then it is assigned to the first set that applies to it in the training,
          validation, test order. This is ignored if Dataset is not provided.
          Example usage: training_filter_split="labels.aiplatform.googleapis.com/ml_use=training".
      validation_filter_split: A filter on DataItems of the Dataset. DataItems that match
          this filter are used to validate the Model. A filter with same syntax
          as the one used in DatasetService.ListDataItems may be used. If a
          single DataItem is matched by more than one of the FilterSplit filters,
          then it is assigned to the first set that applies to it in the training,
          validation, test order. This is ignored if Dataset is not provided.
          Example usage: validation_filter_split= "labels.aiplatform.googleapis.com/ml_use=validation".
      test_filter_split: A filter on DataItems of the Dataset. DataItems that match
          this filter are used to test the Model. A filter with same syntax
          as the one used in DatasetService.ListDataItems may be used. If a
          single DataItem is matched by more than one of the FilterSplit filters,
          then it is assigned to the first set that applies to it in the training,
          validation, test order. This is ignored if Dataset is not provided.
          Example usage: test_filter_split= "labels.aiplatform.googleapis.com/ml_use=test".
      budget_milli_node_hours: The train budget of creating this Model, expressed in milli node
          hours i.e. 1,000 value in this field means 1 node hour.
          Defaults by `prediction_type`:
              `classification` - For Cloud models the budget must be: 8,000 - 800,000
              milli node hours (inclusive). The default value is 192,000 which
              represents one day in wall time, assuming 8 nodes are used.
              `object_detection` - For Cloud models the budget must be: 20,000 - 900,000
              milli node hours (inclusive). The default value is 216,000 which represents
              one day in wall time, assuming 9 nodes are used.
          The training cost of the model will not exceed this budget. The final
          cost will be attempted to be close to the budget, though may end up
          being (even) noticeably smaller - at the backend's discretion. This
          especially may happen when further model training ceases to provide
          any improvements. If the budget is set to a value known to be insufficient to
          train a Model for the given training set, the training won't be attempted and
          will error.
      model_display_name: The display name of the managed Vertex AI Model. The name
          can be up to 128 characters long and can be consist of any UTF-8
          characters. If not provided upon creation, the job's display_name is used.
      model_labels: The labels with user-defined metadata to
          organize your Models.
          Label keys and values can be no longer than 64
          characters (Unicode codepoints), can only
          contain lowercase letters, numeric characters,
          underscores and dashes. International characters
          are allowed.
          See https://goo.gl/xmQnxf for more information
          and examples of labels.
      disable_early_stopping: bool = False
          If true, the entire budget is used. This disables the early stopping
          feature. By default, the early stopping feature is enabled, which means
          that training might stop before the entire training budget has been
          used, if further training does no longer brings significant improvement
          to the model.
      display_name: The user-defined name of this TrainingPipeline.
      prediction_type: The type of prediction the Model is to produce, one of:
              "classification" - Predict one out of multiple target values is picked for each row.
              "object_detection" - Predict a value based on its relation to other values.
                  This type is available only to columns that contain
                  semantically numeric values, i.e. integers or floating
                  point number, even if stored as e.g. strings.
      multi_label: bool = False
          Default is False.
          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).
          This is only applicable for the "classification" prediction_type and
          will be ignored otherwise.
      model_type: str = "CLOUD"
          One of the following:
              "CLOUD" - Default for Image Classification.
                  A Model best tailored to be used within Google Cloud, and
                  which cannot be exported.
              "CLOUD_HIGH_ACCURACY_1" - Default for Image Object Detection.
                  A model best tailored to be used within Google Cloud, and
                  which cannot be exported. Expected to have a higher latency,
                  but should also have a higher prediction quality than other
                  cloud models.
              "CLOUD_LOW_LATENCY_1" - A model best tailored to be used within
                  Google Cloud, and which cannot be exported. Expected to have a
                  low latency, but may have lower prediction quality than other
                  cloud models.
              "MOBILE_TF_LOW_LATENCY_1" - A model that, in addition to being
                  available within Google Cloud, can also be exported 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" - A model that, in addition to being
                  available within Google Cloud, can also be exported as TensorFlow
                  or Core ML model and used on a mobile or edge device with afterwards.
              "MOBILE_TF_HIGH_ACCURACY_1" - A model that, in addition to being
                  available within Google Cloud, can also be exported 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.
      base_model: Optional[models.Model] = None
          Only permitted for Image Classification models.
          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 model_type.
      project: Project to retrieve dataset from.
      location: Optional location to retrieve dataset from.
      labels: The labels with user-defined metadata to
          organize TrainingPipelines.
          Label keys and values can be no longer than 64
          characters (Unicode codepoints), can only
          contain lowercase letters, numeric characters,
          underscores and dashes. International characters
          are allowed.
          See https://goo.gl/xmQnxf for more information
          and examples of labels.
      training_encryption_spec_key_name: The Cloud KMS resource identifier of the customer
          managed encryption key used to protect the training pipeline. Has the
          form:
          ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
          The key needs to be in the same region as where the compute
          resource is created.
          If set, this TrainingPipeline will be secured by this key.
          Note: Model trained by this TrainingPipeline is also secured
          by this key if ``model_to_upload`` is not set separately.
          Overrides encryption_spec_key_name set in aiplatform.init.
      model_encryption_spec_key_name: The Cloud KMS resource identifier of the customer
          managed encryption key used to protect the model. Has the
          form:
          ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
          The key needs to be in the same region as where the compute
          resource is created.
          If set, the trained Model will be secured by this key.
          Overrides encryption_spec_key_name set in aiplatform.init.

  Returns:
      model: The trained Vertex AI Model resource or None if training did not
          produce a Vertex AI Model.
  """
  # fmt: on

  return dsl.ContainerSpec(
      image=_image.GCPC_IMAGE_TAG,
      command=[
          'python3',
          '-m',
          'google_cloud_pipeline_components.container.v1.aiplatform.remote_runner',
          '--cls_name',
          'AutoMLImageTrainingJob',
          '--method_name',
          'run',
      ],
      args=[
          '--init.project',
          project,
          '--init.location',
          location,
          '--init.display_name',
          display_name,
          '--init.prediction_type',
          prediction_type,
          '--init.multi_label',
          multi_label,
          '--init.model_type',
          model_type,
          '--init.labels',
          labels,
          '--method.dataset',
          dataset.metadata['resourceName'],
          '--method.disable_early_stopping',
          disable_early_stopping,
          dsl.IfPresentPlaceholder(
              input_name='training_encryption_spec_key_name',
              then=[
                  '--init.training_encryption_spec_key_name',
                  training_encryption_spec_key_name,
              ],
          ),
          dsl.IfPresentPlaceholder(
              input_name='model_encryption_spec_key_name',
              then=[
                  '--init.model_encryption_spec_key_name',
                  model_encryption_spec_key_name,
              ],
          ),
          dsl.IfPresentPlaceholder(
              input_name='model_display_name',
              then=['--method.model_display_name', model_display_name],
          ),
          dsl.IfPresentPlaceholder(
              input_name='training_fraction_split',
              then=[
                  '--method.training_fraction_split',
                  training_fraction_split,
              ],
          ),
          dsl.IfPresentPlaceholder(
              input_name='validation_fraction_split',
              then=[
                  '--method.validation_fraction_split',
                  validation_fraction_split,
              ],
          ),
          dsl.IfPresentPlaceholder(
              input_name='test_fraction_split',
              then=['--method.test_fraction_split', test_fraction_split],
          ),
          dsl.IfPresentPlaceholder(
              input_name='budget_milli_node_hours',
              then=[
                  '--method.budget_milli_node_hours',
                  budget_milli_node_hours,
              ],
          ),
          dsl.IfPresentPlaceholder(
              input_name='training_filter_split',
              then=['--method.training_filter_split', training_filter_split],
          ),
          dsl.IfPresentPlaceholder(
              input_name='validation_filter_split',
              then=[
                  '--method.validation_filter_split',
                  validation_filter_split,
              ],
          ),
          dsl.IfPresentPlaceholder(
              input_name='test_filter_split',
              then=['--method.test_filter_split', test_filter_split],
          ),
          dsl.IfPresentPlaceholder(
              input_name='base_model',
              then=[
                  '--init.base_model',
                  base_model.metadata['resourceName'],
              ],
          ),
          dsl.IfPresentPlaceholder(
              input_name='model_labels',
              then=['--method.model_labels', model_labels],
          ),
          '--executor_input',
          '{{$}}',
          '--resource_name_output_artifact_uri',
          model.uri,
      ],
  )
